Science topic

Computational Neuroscience - Science topic

Computational neuroscience is the study of brain function in terms of the information processing properties of the structures that make up the nervous system. It is an interdisciplinary science that links the diverse fields of neuroscience, cognitive science and psychology with electrical engineering, computer science, mathematics and physics.
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In recent years there has been a tremendous surge in neuroimaging research, and in my experience the most exciting aspects lie in:
  • exploring how neural systems are able to process and integrate multiple inputs,
  • elucidating how complex neuronal circuits can be understood by computational modelling of simplified models (both static as well as dynamical),
  • elucidating what is the mechanism for synaptic plasticity and the mechanism underlying how brain regions communicate (the neurodevelopmental and plastic brain models of cognitive and computational processing and brain connectivity).
  • Understanding what is the underlying computational basis for the generation of complex neural activity in a given brain region. For example, can neurons with similar inputs and identical synaptic parameters but different weights in a given layer of the brain show a qualitatively distinct neural firing rate pattern?
To understand these kinds of neuronal computations at an integrative level, a systems view is a powerful framework to provide both mechanistic insight, while taking advantage, as a complementary method, also allowing the possibility of modelling the system in a rigorous and detailed way and providing insight into its behaviour. What's your (qualified) opinion?
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i think the most important future development in neuroscience will be an understanding of how the brain controls focus of attention. This is crucial to an understanding of the operation of the brain and consciousness.
Richard
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I'm searching for a good collaborator or a research group that might want to tackle an interesting problem involving the relationship between quantum dots generating nanoparticle clusters and their DNA/proteins corral. This relationship is encapsulated by geometric proximity, that is I'm looking for someone who might know how quantum mechanics impacts something like these nanoparticles, such as how close a nanoparticle is to another nanoparticle or a protein and whether sized clusters form. Ping me if you're in the bio sciences, computational biology, chemistry, biology or physical sciences and think you might be able to shed some light on the above.
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Navjot Singh This might surprise you but I recommend you analyse the problem without using quantum theory. If you take a look at the preprint linked below you will see a different approach to the analysis of molecular bonds:
This is based on the Spacetime Wave theory and shows how a stable bond is formed when the electrostatic and electromagnetic forces are in balance.
Richard
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I do recognise that there’s a well-known problem (hard though it is) of establishing how consciousness emerges or can be accounted for in physical processes. But I can’t at all agree that there’s a naturalistic, absolute hard problem of consciousness, because it’s an incoherent concept.
Nobody (at least nobody with a clue) supposes that neurophysiology can explain a qualitative difference in the way you and I experience the content of my music mix playing quietly in the background, or see the light reflect off a rainbow, or any of the other ways in which our qualitative experience discriminates from that of other live organisms. To suppose that just because you don’t know the mechanisms of the experience in your own head you will deny them the existence of them in somebody else’s is bizarre and reductionist.
Construct an imaginary metaphor of a magical, wizardry, thing-maker consciousness and you haven’t explained the qualitative data there either. It’s still the question of how consciousness comes into the work whether any magical things happen or whether there’s anybody there at all. To suppose a separate, inexplicable, mysterious, magic ingredient does neither any explanatory good, solve the hard problem, nor explain the evidence. All such arguments for a separate consciousness occurrent substance do, again, be it a magic nonsense or magic substance involved, reduce the hard problem of explaining thisness-of-consciousness (to pick a crazy approach) to the very same hard problem of explaining how consciousness arises in the first place.
If you identify the hard problem entirely with the mechanism through which the feeling-of-redness arises, or "the feeling of the future in an invariant past", or anything else you allude to, then you plainly have just traded in one way of asking a very simple question of the wrong approach. The question is, how do the millions of biological chunks and sub-systems interact with one another and integrate information over time and space? The sense of sight, sound, touch and soil all raise a “hard problem” of projection-understanding and categories-beyond-the-reliable-input-enumeration because by a vast over-engineering of the metaphor arms race (as even you must agree) the response-device signals of a single kind of appropriate examination will allow all in-the-know people to interpret an external reality quite differently. But the “hard problem” isn’t WHY is it that we can punch those signals at all, or make sense of the signals that come out the other end. That’s just the default condition of our very real neurological symposium. Whereas the “humanness” of that experience is also an entirely benignly apparent phenomenon, just as water’s polar nature is an entirely benignly apparentity.
For me the cardinal point is to reckon with how we perceive our own subjective value via multi-sensory data input both direct and indirect in both our two and three dimensional waking experience. And because at the very least you have to be wrong or qualified immensely if you think it’s not merely the interaction between general anatomy, organisation, information processing and output of your brain and all subjective processes such that personal conclusions then magically appear as relevant claims about reality.
P.S. I don't think evolution throws up any magical consciousness, either on its petri-dish experiments, or those novelty subjectiveness media that it comes up with sometimes. So I'd like to challenge that viewpoint, particularly in terms of our understanding of the nuances.
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Navjot Singh I define consciousness as the subjective experience that we each have arising from the operation of the brain.
In the paper titled the conscious brain, I have identified the importance of understanding how we control our focus of attention.
The brain is a particular combination of biology chemistry and physics and it is a lack of understanding of fundamental physics that has held us back.
Neuroscience has revealed the brain activity in the form of the network of neurons but we have to understand the effect of the electromagnetic wave activity generated by the brain on the operation of the brain as a whole.
Richard
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I'm a CS major, currently in my freshman. i have a huge enthusiasm for Computational NeuroscienceI'm a CS ma.. i just wanna know how to transition or incline myself so that i get easily accepted for this program for masters etc and satisfy my passion.
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Hi Maimoona,
It is pertinent to familiarize yourself with some fundamental ideas in neuroscience, such as the activity of a single neuron and the mechanisms that underlie it, such as the action potential and synaptic conductance.
For those interested in computer-based models of individual neurons, the Hudgkin-Huxley and Moris-Lecar models are good places to start. I would strongly advise you to study and explore some mathematical approaches for resolving equations such as the one involving a single neuron from that point onward (for example Euler method). In this regard, you can employ your programming skills, and can simulate the activity of a neuron, then add more neurons and connect them with different synapses. Afterwards, go through the process of simulating a neural network and modulating it by optimizing the parameters in your code. This is especially appropriate if your major is computer science (CS).
I believe that the ideal approach is to simultaneously improve your algorithmic and theoretical neuroscience abilities.
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Imagine the following scenario where 100 people walking into a clinic, each one of them presenting with a variety of neurological symptoms. Some of these symptoms are similar, some are different and a couple of these patients have kids. The doctors must employ their knowledge, expertise, experience and maybe use medical imaging and other methods to try to diagnose each of them.
It becomes clear that the possibility of a false positive or false negative is high since different neurological disorders present with similar symptoms or a single disorder presents with variable symptomatology.
What if we could use wearable sensors under a task common for all subjects and extract objective biometrics that characterize the temporal, spatial and dynamical behavior of their neuromotor activity? Then, we no longer have a collection of random symptomatology but objective data in a parameter space that stratifies a random cohort of the population. Different clusters within the population could be used to identify different neurological disorders as well as different subtypes of each disorder. This paves the way towards personalized medicine, since each patient is now a unique point in some parameter space that the clinician can track through time.
Check out our latest article at Springer Nature journal of Scientific Reports from Sensory Motor Integration Lab at Rutgers University (Prof. Elizabeth Barbara Torres) in collaboration with researchers at Stevens Institute of Technology and Columbia University.
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Hello Dr. Anand,
Thank you very much for your reply. Indeed, in many cases there is a motor component involved in neurodevelopmental or neurodegenerative disorders that stems from CNS impairment. For example, it has been proven that in the case of Autism Spectrum Disorder stochastic analysis of the motor component in individuals who have receive the diagnosis shows departure from the neurotypical case as a result of disruptions in the maturation process of proprioceptive mechanisms. Yet, ASD and other neurological and neuropsychiatric conditions are rarely considered to exhibit a motor component.
For more information, see "Autism: the micro-movement perspective". (Elizabeth B Torres et al, Front. Integr. Neurosci., 2013 )
Link:
The analysis that was involved in this research normalizes for anatomical differences between individuals, therefore the abnormal motor behavior is a result of CNS impairment rather than of biomechanical nature.
The use of wearable sensors and stochastic modelling can assist the diagnosis of neurological and neuropsychiatric disorders, since it allows for the detection of abnormalities on a microscopic level that is undetectable to the naked eye. Such abnormalities do not stem from neuromuscular abnormalities but from CNS impairment. Therefore, the absence of neuromuscular symptoms does not mean there is no motor component involved.
Hope this helped!!
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I am going to use a mas model for the dynamics of a population of neurons. I first used Wilson-Cowan (W-C) model, but the problem is W-C has oscillatory behavior in a limited range of parameters and the frequency of activities is limited.
It would be great to have a model to produce oscillation from low (1 Hz) to high (80 Hz) frequencies.
Do you know any references?
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So there is the trick explained in David and Friston (2004) that basically couples two distinct Jansen-Rit models with each other that are tuned to different frequencies. Essentially, this gives you a 6 population neural mass model with a much less narrow frequency response that you can tune.
As an alternative, we have extended the QIF model by Montbrio et al. (2015) by different plasticity mechanisms, yielding a single population model that can produce a slow-fast oscillatory regime ( ). This regime has a very fast frequency and a slow one that can both be tuned via the time constants of the membrane potential and the plasticity mechanism, respectively.
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How to load .eeg format in MNE library (Python)?
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Did you find a solution? I have the same problem...
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It is required for brain CSF flow dynamics. Like in hydrocephalus, when CSF accumulates inside ventricles, the nearby parenchyma get stressed. I need it for simulating the hydrocephalic brain. Any suggestions?
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Can I have the code please?
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What are possible sources of information (online courses, books, articles...) for people who want to acquire basic concepts in Computational Neuroscience?
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Can you help me with some database for neuroscience, for example fMRI database, or database which show underlying mechanisms of the brain, show the connection between brain and behavior, psychiatry database and other things which related to brain, if you were familiar with genetics we have for example Reactome, KEGG, STRING and other database which show lots of pathway and cell connection, I wonder if we have sth like that in neuroscience, a big database which help us to better understand the brain.
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Actually you can use those databases that you mentioned to conduct research and understand mechanisms of brain activity in health and disease in computational neuroscience!
We recently used KEGG and STRING to study gene networks in different psychiatric diseases.
However, if you are mainly interested in using fMRI and imaging databases in your research, please check the following as well:
1. Blue Brain project by EPFL has a great database in at circuit level that can be used:
2. Human Connectome Project (HCP) is a great database comprised of scans and analyses of more than 1100 subjects:
3. Allen Brain Atlas
and many other databases that you may find and use depending on your main focus in neuroscience.
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We are organizing, together with Matthieu Gilson, Adrià Tauste and Gorka Zamora-López, a Hands-on course on neural data science, in the frame of the XIII Summer School in Statistics at UPC-UB (Barcelona, Spain).
The course will cover statistics, time series modelling, machine learning and graph theory. The students will develop a data-driven project throughout the course to understand how these different tools can work together to analyse brain data. Reach out if you have any question!
When: July 1st to 5th from 3:00 PM to 6:00 PM
Where: Barcelona, UPC campus
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Thanks for sharing
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Hi, I am interested whether there have been researches done on making a biophysical neural-network version of the drift-diffusion decision-making model. Specifically, I hope to know whether such a biologically constrained model can achieve all of the following features:
1. Transformation of sensory signals to linearly increasing firing rate of neurons over time.
2. The rate of climbing firing rate reflects the strength of sensory evidence.
3. Giving the same strength of sensory evidence, the rate of climbing firing rate varies randomly from trials to trials.
4. Decision is made when the firing rate of neurons reaches a specific threshold.
5. The distribution of decision latency (or response time) fits well with experimentally measured response time in simple decision tasks.
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Good question; necessitates investigation. I wonder whether such a research can be given in man. The major problem is that decision making is a very complex process, depending on a large participation of the Reticular and Limbic (motivational system) and on the cortical Frontal Lobe structures.
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Hello everyone,
I am an MSc student in Psychology and right now I am in search of a research idea for my thesis. I am interested in neuropsychology / neuroscience / computational neuroscience / cognitive robotics and therefore I want to do a thesis on one of these areas because I am planning on applying for a relative PhD. The problem is that unfortunately, my University lacks lab equipment and therefore, any ideas that I have had so far are not testable because they require lab equipment.
Therefore, I was wondering if there are any alternative experimental methods for research in these areas that do not require lab equipment and if they do, I can run them on my laptop or a University computer.
Any suggestions (either experimental methods or reading) are more than welcome.
Thank you very much for your time.
A. Sigalas
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PIs are principal investigators, basically leaders of research groups.
Ok then it doesn't work of course. For my MSc we were encouraged to write the thesis abroad.
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Hi everybody.
I plan to use complex network analysis methods to investigate psychiatric disorders. Most researchers have used FMRI data to construct brain network. I wonder if it is possible to use EEG as well
and if so, what s the advantageous and disadvantageous of using EEG in comparison to fMRI?
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Consider looking at these answers regarding simoultaneous EEG-fMRI data.
There's no 'superiority' between EEG and fMRI (Dan Michael Psatta). EEG has fast timescales and records electric activity, but has practically no spatial resolution. FMRI has slow timescales and records blood-oxygenation related signals (BOLD) but has a remarkable spatial resolution.
You may want to deepen the study of both these techniques before choosing. Your choice will depend on your objectives.
Consider also reading
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I need a standard brain connectivity matrix (CM) for healthy and pathological conditions, such as Alzheimer's disease, Down syndrom, etc. namely, a text or binary file which has all the connection strengths between some regions of interest (ROIs). I need this matrix, in addition to the MNI map of the ROIs used in the matrix. I couldn't find any database for it. It seems that each group measures these matrices by their own, rather than referring to a general database.
Is there a general database for brain connectivity that anyone can use and refer to? A matrix for the connections and physical locations of the default mode network (DMN) would also help.
Thanks in advance for your help!
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You can see the TVB: The Virtual Brain platform
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In sleep, brain activity, specially in the default mode network (DMN) is reduced. For example, Amyloid beta production by neurons has a 70 % jump in wakefulness compared with sleep (Kress et al. 2018 J Exper Med). Can one assume that the same would happen in case of coma, or anaesthesia? Are they similar at all in terms of brain activity?
As I searched, it seems we are able to induce an artificial coma, like what we do for anaesthesia? If so, how long could it last at most?
The last but not least, have people taken effect systematic MRI during comma, anaesthesia, and sleep?
I thank you in advance for your help.
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Hi.
Yes they are differ, there are difference in the state of the brain among coma, sleep, and anaesthesia.
I recomend to read this article, it exlain the differences very clear.
General Anesthesia, Sleep, and Coma
Emery N. Brown, M.D., Ph.D., Ralph Lydic, Ph.D., and Nicholas D. Schiff, M.D.Author
Amit
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When a neuron goes into depolarization block (e.g. due to very high extracellular potassium) does it continue to release neurotransmitters? Surely action potentials no longer propagate along the neuron, but intracellular calcium can go quite high during depolarization block. Would this cause it to release synaptic vesicles? During depolarization block is the axon depolarized as well? Would the cell dump its available synaptic vesicles or would it stop transmitting all together? If it matters, I'm thinking of a cortical pyramidal neuron during cortical spreading depolarization/depression. Thanks for your thoughts.
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Dear Thomas,
Your question is a very good one and the answer depends on numerous highly complex factors.
1. The type of neuron and especially calcium channels enriched at presynaptic terminals. As you may know, voltage-gated calcium channels (currents) driving transmitter release undergo rapid inactivation (few hundred ms) under prolonged depolarization. So, you may have a persistent depolarization, but this does not necessarily mean increased influx of calcium over extended time period.
2. The type and amount of calcium buffering proteins expressed at nerve terminals. In inhibitory interneurons, for example, under normal conditions, depolarization induced activation of transmitter release is typically very rapid, and is curtailed by calcium buffering mechanisms. As a result, to induce transmitter release, these terminals need very high-amplitude spikes with massive rise of calcium, to overcome the buffering barriers and to drive the fusion reaction.
3. Much also depends on the calcium sensors operating at nerve terminals (i.e. synaptotagmin isoforms and other sensors) as they have widely variable sensitivity for calcium and binding kinetics.
4. If I remember correctly, the membrane potential during depolarization block remains within negative ranges (between -35 and -20 mV), which is by any means not enough to drive sufficient calcium influx to activate exocytosis. Having said that, this is good enough to unblock presynaptic NMDA receptors and activate Ca2+ influx. If there is sufficient extracellular glutamate, then one would expect increased exocytosis over some time period. Again, this will be only transient as NMDA receptors also inactivate.
There are also other more complex players in this game, which should be considered, to answer your short question. Cutting the story short, the answer is conditional NO. More precisely, one would expect transient increase of exocytosis during the initial depolarization, which is followed by a shut-down of exocytosis at nerve terminals in most synapses.
If you have any questions, feel free to contact for further discussions.
Best regards,
Saak
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I'm looking for references to justify max and min conductance values of synapses containg AMPA and/or NMDA receptors in a biophysical model using the Neuron simulation environment. If I can be really specific I'd like values from stellate cells in the medial entorhinal cortex. But general values would be fine. I have some general numbers I'm playing with that I've found from different sources but those sources don't have any citations. So for AMPA I've found a range form 0.1-1.5 nS and for NMDA 0.05-3.9 nS.
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You can search in pharmacology guide web site.
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I'm wondering how long a neuron could continue to fire action potentials if all transporters (those using ATP) and co-transporters (those using other ion gradients) were blocked. Would it be several seconds, several minutes, an hour? I'm specifically wondering about mammalian cortical neurons.
I'd be interested in either empirical evidence or in back of the envelope biophysical calculations.
I'm guessing it is in the range of a minute since hypoxia can cause problems within a few minutes (and even that is buffered by the volume of blood).
Why I ask: Making a model. My simulation (based on biophysical assumptions) is losing it's potassium gradient faster than I would have expected.
Thanks in advanced. Really appreciate it.
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INTERESTED TOO
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I have computed a FA images for a DTI dataset and registered the FA images to a specific brainstem atlas. I now need to analyze the tractography of this atlas and its relation to the FA image. What statistical toolboxes or methods do you recommend?
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Probabilistic tractography can be performed with FSL's BEDPOSTX via seeding from the ROI's in the brainstem atlas and capturing tracts which pass through the ROI.
Deterministic tractography can be performed with 3D Slicer (http://dmri.slicer.org), it automatically applies a housemade atlas though I'm not sure brainstem tracts are included and fiber tracts
for those ROI may have to be clustered by hand (this is a pretty insignificant extra step).
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In Pattern Activation/Recognition Theory of Mind (Frontiers in Computational Neuroscience, doi 10.3389/fncom.2015.00090), I have shown that neurons can describe other neurons.
I have also shown that this process of description allows ssociating representations via metaphoric mapping of one set of neurons to another one.
Here this metaphoric mapping would apply between a description of neurons representing the self and one representing others. It is then applicable to representing the self in relations to others, in short the social self?
Regards. Bertrand du Castel.
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It is neither a memory nor an empathy function. It is lower in the hierarchy. The metaphoric mapping works as follows:
1. consider a set of explicitly described neurons providing an input/output function
2. consider another set of neurons, also explicitly described, and itself describing the first set of neurons
3. then I show how yet another set of neurons, also explicitly described, can apply the function 1 to different input or/and output by added connections to specific neurons of the descriptions
4. I also show how another set of neurons, still explicitly described, can modify the function 1 itself. I even show how a set of neurons can modify the function 2, and actually, even itself, recursively up to a set of neurons that can universally describe any other set of neurons, by adding neurons and/or connections to other neuronal descriptions
5. above 3 and 4 are the neuronal mechanisms governing metaphorical descriptions
In terms of social self, 3 can describe the self (a given input/output) or describe others (different input/output) via 3, and 4 allows modifying the self/other relationship by modifying the corresponding functions, or, also, modifying the self and other descriptions themselves as applicable.
Memory or empathy functions should then be describable that way, as would be constructions on top of the neuronal descriptions I introduced, but they are not a product of them per se. I say "should" and "would" because I have never done an exercise at that higher level. The examples of treatment I provide in the article are fundamentals, regarding in particular learning, self-description, metaphors, and organization, and specifically intended to present the mechanisms involved. Applying those mechanisms on the scale necessary for your project is work yet to be done, but I do believe that the scaffolding is there, and I sure can help if needed.
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In axons, action potentials can move both in ortho-dromic (normal) direction as well as in anti-dromic direction, if stimulated in the right way. But what happens if two action potentials are generated simultaneously, one in the distal axon end and one at the soma, that are moving towards each other to collide? Will they penetrate (move past each other) or annihilate?
According to classical Hodgkin-Huxley model and theory of neurophysiology they will annihilate due to the in-activation of the sodium conductance. This effect has also given rise to the experimental method called "the collision test", which is used to confirm axon projection from one brain region to another by means of antidromic stimulation. 
Nevertheless, a recent paper claims that two colliding action potentials will penetrate just as two colliding waves on a sea of water:
My question: Does anyone know the original literature about collision of action potentials? This must be back in the 1950'ties or 1940'ties. Who did the investigation and what are the publication references?  I have been trying to find the original papers, because I am sure that scientist investigated this back in those days. The only one I could find was this:
I. Tasaki, Collision of Two Nerve Impulses in the Nerve Fiber, Biochim. Biophys. Acta 3, 494 (1949).
thanks,
Rune
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Hello everyone.
Finally our rebuttal comment has been accepted for publication in Physical Review X, almost 2 years after our first submission. We could not reproduce the observation of penetration of action potentials by the Heimburg group. Instead we consistently observed annihilation of colliding action potentials.
Further we show that their measurements of action potential velocity was flawed and indicate that their measurement electrodes were so close to the stimulation electrodes, that 1) they could not contain two action potentials 2) passive propagation may explain part of their observations. 
Also, they did not demonstrate the All-or-none property of action potentials, which again suggest that they observe either activation of multiple nerves, or they generate passive electric signals. Attached is preprints and supplementary information.
Cheers!
Rune
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A linear summate and fire default model of post-synaptic integration would not require inputting axons to synapse at any particular place within the dendritic tree of the receiving neuron. However, if post-synaptic integration was non-linear and possibly pattern sensitive it might be essential that the site of each synapse bore a relation to the meaning or significance of a specific input signal. Thus for a multimodal high-level sensory neuron signals originating from different primary cortices might arrive at different domains of the dendritic tree.
I would be very interested to know whether there is already useful information on this topic or whether anyone hopes to collect such information.
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The doi seems to match. The Trettenbrein paper is certainly interesting. I see it makes a lot of Randy Gallistel's 2009 book, which I was looking at last week for other reasons. I have sympathy with the idea of information being stored in cell structure independently of synaptic 'weight' but have always thought Gallistel was sticking too closely to a Turing analysis in developing his question. 
And I guess my question remains the same: do we know if synaptic site matters for meaning?
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In neuroscience, sharp wave ripples correspond to big synchronous event in the hippocampus. I wonder how many of them occur during a sleep cycle (order of magnitude).
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Hi Romain, 
Sharp-wave ripples are described as "random" high-frequency events mostly occurring during the slow-wave stage of sleep. According to several studies in rats, they can present an averaged frequency of occurrence of ~0.2Hz, however, as they are random events this number can vary due to several factors, such as amount of REM/slow-wave sleep, amount of sleep/activity, etc.
Hope this helps.
Best,
Claudio
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I've been looking at phase-amplitude coupling (PAC) of the brain and wanted to test my PAC measurement method by having a positive control. i.e. a wave that certainly has PAC at certain frequency-frequency pairs.
I tried simulating this wave by removing amplitude of higher frequency wave at certain phases of low frequency wave. Then, I added higher frequency wave to low frequency wave. However, the resulting wave seems to have produced PAC at unexpected bands. I do not know whether it is my measurement method that is a problem or my PAC wave generating method that is wrong. If someone could give me a link to a paper or knows how to build a good PAC wave without artifacts in other frequency bands, I would greatly appreciate it.
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HI such wave forms depend on how they are generated and shaped....and any mixture is going to give harmonics, irrespective of whether you see them or not. What looks like a pure sine wave will not be one...it will be an approximation depending on the numbers of square/saw-tooths generated to form it.
You are creating harmonics in what you are doing with your interacting waveforms .. that's your visual indicator and your visual indicator not only has limitations but it interacts with the circuit which it is reading. All these electronic systems work to certain limits of accuracy and they all load the circuit. What is designed in and out are the parameters under which the circuits will give an output which will enable 'acceptable' harmonics and shaping.  There's theory that a pure sine cannot be created and I guess what you have to first decide is 'what wave form limitations suits my purpose' ans 'will I know the difference anyway' Brains are a mass of electronics and will have the same limitations as other electronic generators.
How is  your  'pure wave' generated?...likely as an infinite  series of modified sawtooth/ square waves shaped in an LC circuit however whatever the generation as soon as any 'flattening' or distortion  of the curve occurs harmonics will generate...There may be ways I don't know of today to computer generate purity however the reason one normally doesn't see harmonics in a 'pure' wave is that the 'escaping' harmonics are so high in frequency that their energy level renders them so weak they have no noticeable effect in circuitry and electronics is generally a mathematical trade-off.
I'm not into your experimentation so I think my best advice is as above....learn the limitations, learn about wave interaction and decide what parameters can give you the answers for which you seek. I don't know where all my old electronics engineering texts are but this (just chosen at random) may give you an idea of waveform interaction..http://www.allaboutcircuits.com/textbook/alternating-current/chpt-7/square-wave-signals/       If I've missed your point...apologies but I don't think I have . Regards
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Dear all,
I want to build neural network based of data provided by Allen institute of brain. Before this, i need to reduce multi compartment neuron to single compartment neuron. Can anyone have tips, articles or other things that can help me?
Best,
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I do believe it is quite complicated if ever possible at all. The problem with such reduction is that: in real neuron, non-linear dendrites do spacial-temporal integration. Multicompartment models tend to describe this process in details (see for example Jarsky et al, 2005). The reduction of a multicompartment model to a point model totally removes spacial component, and remains only the temporal integration. If you have a mathematical background, you should understand the limits of possible reduction a partial differential equation to a single ODE.
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I recently started working on computational neuroscience. I am looking forward to reproducing results from some papers which are related to my field of interest. I can't find a value for the initial Ca ion concentrations for subthalamic nucleus cells and the globus pallidus cells. Also what are their resting membrane potentials. I would be very glad if someone could provide me these values.
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I can't help you with initial Ca ion concentrations - if this isn't published in literature, you may need to measure it yourself or open a collaboration with someone who can measure it for you.  For the resting membrane potentials, you might find NeuroElectro useful.  NeuroElectro is a semi-automated literature text mining database that contains alot of electrophysiological data pulled directly from literature.  Try browsing that to see if any of the cell types you are interested in have been listed:  http://neuroelectro.org/
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my question is,a signal recorded at each electrode is an activity of the same brain area of the electrode placed or other areas of a brain activity will affect its recording ? if it so how can we analyze that signal, is there is any chance to study the different brain areas by constructing an algorithm based on measuring EEG signals ?
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EEG activity is influenced by cortical brain activity near both the electrode of interest and the reference electrode because a recording requires current passing through both electrodes. Recordings may also be influenced by seemingly far away brain regions due to volume conduction. Although this is rare in simulation.
See the attached book chapter, section 4.
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Hi,
I want to analyze a size volume of intense spots in a MRI data file in FIJI (ImageJ)
I firstly project this file to 3D format using the Stacks -> 3D project tool.
Using the point tool, I can select which intensity should be used for calculating (for which I now only know 3D object counter, but this is a bit messy in my view, options? :) ).
Then using 3D object counter it calculates the amount of available spots.
But now I want only to use part of the 3D scan (which I compiled first) to be able to analyze only the intense spots in the frontal lobe and prefrontal cortex and in the hippocampus.
Is there an easy way to calculate this? (total volume of hyperintense spots in frontal lobe and hippocampus from MRI (.nii) data)
Thanks in advance,
A
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Hi Mr. Barthel,
Thank you for your answer. Unfortunately the project is already finished. I quantified the volumes manually or using 3D object counter per slice. But I will try your suggestion later.
Regards,
Alexander
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Hi,
I’m using the NEURON software to simulate a layer V pyramidal neuron from the visual cortex.
My goal is to be able to measure the voltage and calcium concentration in the dendritic spines of this neuron. So far, I’ve been doing this with a pyramidal neuron model I found, and by adding different modules (pumps, receptors, etc) to simulate the behavior of calcium in the spines; however, I fear that my model might not be accurate enough.
So I would like to know if there is a specific model of a pyramidal neuron that would allow me to measure precisely the voltage and calcium concentration in the dendritic spine heads.
Thank you
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Dear Bruno! You, please read carefully the article in the application.
Vladimir
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The past few weeks I've been analyzing the particular solutions generated by my ANN. I've seen that even for simple tasks like a delayed XOR function the solutions can be highly convoluted and highly distributed across many different neurons making it really difficult for the human eye discern what specific computations are happening across this network. (See the weight matrix below which solves a delayed XOR task. The solution happens to be beautiful but is very complex and highly distributed for this simple function.)
These observations signal to me just how daunting the task of reverse engineering real neuronal networks are where:
- I can't control every aspect of my experiment
-  There are stochastic elements
- Complexity orders of magnitude beyond simple ANN's
- The specific computational function isn't known
- The physiological details aren't fully known
- etc. 
Hence, it is clear to me that work needs to be done in developing a theory for making sense of distributed computation in dynamical networks. Has there been work on this? Am I the first one to ask this question (or is this Dunning-Kruger in motion)
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If you would like a non-standard, somewhat heterodox, take on connectionist (ANN) computation, I would recommend looking up analogue computational theory that includes "structural representation". It's 'analogue' not in the sense you would assume.
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Hello everyone,
I was reading some papers about computational neuroscience. I could find just limited sources which briefly explain the invariance for consciousness. What are some good sources to have a profound perception on the invariance of human visual cortex?  
Thanks,
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'not sure what you mean by the invariance of consciousness. However, take a look at the vector math in Chris Eliasmith's How to Build a Brain for the construction and comparison of representations.
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I'm in high school and am passionate about my research in computational neuroscience. I'm looking at global dynamics in C Elegans in the context of its connectivity and structural motifs. 
My recent experience at a computational neuroscience hackathon – having fascinating conversations with people, confirming my ideas, being immersed in the environment of intellectual curiosity, exploration, etc was incredible. I realize were I to be in such an environment routinely I'd grow tremendously. 
I'm wondering if anyone here can provide some strategies for getting into a lab, what to expect, what are the unwritten prerequisites that people expect, etc. 
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If New York City is not too far for you, please consider my institution, Weill Cornell Medicine. We are always looking out for bright high schoolers with strong independent work ethic. You can help your case tremendously by taking a self-taught or school course in programming, especially in MATLAB or Python. Everything in comp neuro requires good coding skills. 
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Dear all, I will start my electrophysiological experiment of fEPSP recording, but I just finished the rig setup. So can you give me detailed suggestion about axon 700B settings for fEPSP recording under I-Clamp?
I will record fEPSP in the pathway of CA3-CA1 in the hippocampal slices. In my experience, the amplitude of fEPSP ranges from 0.5 mV to even 3 mV.
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Also, I think you can use the '100x AC potential (100mV/mV)' gain setting on the headstage (selectable from MultiClamp Commander) to optimize the gain setting.
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Anyone here expert in solving differential equation using neural network? Is this approach has really some benefit?
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I would like to try how fixation correction works using softwares.  Please suggest some softwares.
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With my pleasure, all the best. Vladimir
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I am planning to use DNN in my research and I just wonder where can I find a good implementation in R or Python? Something that has Recurrent Neural Network or Convolutional neural network would be even better.
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Thanks for the suggestion.
I will definitely take a look at it.
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Can anyone suggest about the 3 D brain on chip development for CNS regeneration study..What are the cell types which we should mainly focus or cell lines. Else directly neural progenitor cell will be more feasible to study.
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DRG cells are peripheral nerve fbers which their myelins are made from Schwann cell. Their axons can regenerate. In the brain, for example the optic nerve, their myelins are from oligodendrocyte cells. I had read that their axons of these kind of nerve fibers can not regenerate. 
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Why shapiro wilk don't change when standardized (Z and T)score?
the data is 13 cases and variables are cognitive test. The test have automatic transformation in software but i want calculate t score because parameters ara based in other population.  However, i have tested shapiro-wilk with original T score and it´s normal distribution.
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Any linear transformation  of a probability distribution function changes the mean and standard deviation of the distribution but not its shape. Hence getting the Z score which is a linear transform will not change the shape.
Non-linear transforms do change the shape.
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My area of research is effect tDCS (Trans-Cranial Direct Current Stimulation) on brain potentials.
I don’t have a good knowledge in tDCS modelling but would like use it a bit in my thesis. As I have already completed 2 years of my PHD I don’t want drift into new field or code the stuff for modelling. I would like to observe how current flows into brain when tDCS is applied. I looked for tDCS modelling software and couldn’t find many in my search. I found one named SPHERES (http://neuralengr.com/spheres/?page_id=17), which doesn’t have human brain model instead have a sphere but can play with parameters (amount of current, electrodes location where tDCS is applied etc).
Could anyone please help me with a simulation software where I can play with parameters like amount of current, electrodes location where tDCS is applied etc. Any help would be really appreciated.
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You can try  SCIRun 5.0  Brain Stimulator FEM Toolkit developed at the University of Utah:
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In perfect integrate model of single neuron, there is no leaky channel. When we set a threshold for spike generation as 'theta', excitatory input rate is 'r_e', each step of excitatory input would increase the membrane potential for on uniform amplitude 'A'. To simplify the question, we set A = 1.
In this case: input rate = r_e, output rate = r_e*A/theta = r_e/theta
so the generation of output is a gamma process.
BUT, in real neuron, there should be both excitatory input and inhibitory input to keep the network balance.
So, each inhibitory input would cancel a excitatory input. And excitatory input and inhibitory input are both from Poisson distribution. Based on perfect model, excitatory and inhibitory input cause exactly same amplitude A effect but in reversed direction.
In this case, let's assume k inputs would generate a spike, so the inputs between two spikes are 2k+m, excitatory input: k+m, inhibitory input is k, and net input is m, which cause the membrane potential rise to threshold.
When we are computing the transient probability of the new neuron model, we set probability of increase as r_e/(r_e+r_i) and probability of decrease as r_i/(r_i+r_e). Then this is a binomial distribution.
Resulted Transient Probability is the product of a probability of Poisson Process (to generate 2k+m inputs in total) and probability of binomial distribution (to have k+m excitatory inputs and k inhibitory inputs)
I was wondering to simplify the process by making a net input (m spikes) from Poisson Process
Or I would say, the new net_rate = r_e-r_i
so we don't need transient probability (Markov process) to understand the transient process from one spike to another, this make the problem again a pseudo-excitatoryinput-only perfect model.
Does this applicable? My supervisor said I should not cancel a excitatory input with a inhibitory input, because Poisson process subtract a Poisson process doesn't make sense. But if I cancel randomly (with the right number), this is again a Poisson Process.
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The process you describe is called a birth-and-death process, and has been extensively studied in the context e.g. of queuing theory. Most recently, I think this has been used by e.g. Mike Shadlen to account for his neurons that integrate to threshold in order to do decision making.
You can't make the reduction to a Poisson process with a rate that is the difference. While the averages fit, the distribution would be substantially narrower, and so the firing probabilities would be much less. As an example, think about a process with r_e=r_i. Your equivalent Poisson process would have a rate of 0, so be identically equal to 0, but the model with two separate processes of excitation and inhibition would still have probability to fire.
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Some kind of neurons show a tonically active behavior in absence of stimulation, either by firing action potentials or releasing neurotransmitter (e.g. inhibitory Off-cells from rostral-ventromedial medulla and photoreceptors). Can the current spiking neuron models (e.g. Hodgkin & Huxley, Izhikevich, FitzHugh-Nagumo, ...) be used to model this particular kind of neuron?
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Hi Adnéne
Most of Neuron models are capable of show spiking behavior, but only as a consequence of external stimuli (e.g., a current injection). In other words. no current injection, no spikes. I need to model a tonically-active neuron by finding a neuron model that can 1) show spiking behavior in absence of stimulation, and 2) cease activity when a stimulus is applied.
Thank you in advance for all your support with this regard!
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Hi! I use CED Software for the recording of mIPSC. I wonder if it is possible to analyse rise time and decay time of the individual events in Spike2.0? The amplitude and ISI seems not to be a problem (I have created my own script).
Or maybe anyone knows how to export the Spike files in order to analyse events in different program?
Thank you for the answers!
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Hi,
Maybe you can check on CED website for specific scripts to analyze minis. Also they have good support if you need to write new scripts.
Hope that helped
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One of the most accepted theories for synaptic plasticity is STDP, but it fails when the frequency of neural firing is very high or very low, since it works in medium rates , my question is: is there any model for plasticity that consider more than one factor?
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Both the Clopath and the Sjoestrom models sound important. However, the word "plasticity" has now taken on so many meanings that I doubt that any single model, particularly one limited to spike-timing, can handle all the instances of 'plasticity." I am not aware of a math model of the old work by Mersenich et al on cortical allocation of finger regions in the macaque. The range of intracortical oscillatory activity known today varies from <1 to 200 Hz, with faster peaks up to 600 Hz. Hippocampal episodic and retrieval is linked to theta-gamma oscillations, where "gamma" may range from perhaps 40-200 Hz. As Dehaene and colleagues have shown, learning of conscious visual stimuli (plasticity) is also associated with cortical binding and propagation in visual cortex, showing resonant oscillatory activity of high amplitude and long-distance activity spread. Spike timing could be linked to population oscillations via single unit phase-linking to a dominant waveform. Pop oscillations in turn can change single unit firing rate by adding to regional depolarizing voltages. Thus unit activity and population oscillations could be closely linked. Steriade (2006) has indeed suggested widespread slow-to-fast oscillations, yielding complex waveforms that may peak in many places in cortex. 
Do we have models of plasticity? Only of some kinds of plasticity.   This is a major challenge, and we would need to know more about neuronal transmitters and receptors, glutamate and NMDA, etc. 
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I've been scouring PubMed for a figure showing spiking in an electrophysiological trace recorded simultaneously with optical imaging of a calcium indicator dye (specifically fura and fluo-4) to no avail. The closest I've seen is a response to a single electrical field stimulus in Akerboom's GCaMP5 paper. I've also seen simultaneous electrophysiology and calcium dyes in non-spiking cells. Do we know what the single-spike response of these dyes looks like?
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Hi Noah. Try Kerr and Plenz, J Neurosci 2002 (attached) - Fig 6B shows a fura-2 loaded cell response to differing numbers of sub threshold depolarisations and supra threshold responses from 1 to 4 spikes. Fig 7B as well.
I know this isn't quite what you asked but does it help?  
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I am looking for some scalp-EEG based epilepsy seizure detection software/code that is implemented in MATLAB. I would appreciate any help regarding this issue. Thank you in advance :)
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You can find the complete code for implementation of Lan-Lan Chena, JianZhanga, Jun-ZhongZoua, Chen-JieZhaob, Gui-SongWang, A framework on wavelet-based non linear features and extreme learning machine for epileptic seizure detection, Elsevier, Biomedical signal procesisng and control doi : http://dx.doi.org/10.1016/j.bspc.2013.11.010) in the same
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I am looking for the whole-brain masks (4300 voxels) for all subjects of Haxby (2001) dataset. If you have them, can you notify me?
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Thanks for the answer. I have found a way to compute them. I will publish the masks soon. I will post a link in this topic.
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Is there a model that can represent different firing patterns of neurons in the cortex?
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s which microcircuit you want to model; if you are interested in L5 pyramidal cell circuit - then see http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1002107
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Miguel Nicolelis and Ronald Cicurel claim that the brain is relativistic and cannot be simulated by a Turing Machine which is contrary to well marketed ideas of simulating /mapping the whole brain  https://www.youtube.com/watch?v=7HYQsJUkyHQ  If it cannot be simulated on digital computers what is the solution to understand the brain language?
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 Dear friends, Dorian, Dick, Roman, Mario, et al.,
            There is hope (Haykin and Fuster, Proc. IEEE, 102, 608-628, 2014).  But first we have to modify our computer and some of our traditional ideas about the brain. With respect to both, here I offer humbly some of my views after half a century of working in cognitive neuroscience.  I shall be brief and cautious.  For an account of empirical evidence, read my “Cortex and Mind”  (Oxford, 2003).
            1.  Alas, the computer cannot be only digital, but also must be analog.  Most all the cognitive operation in the brain are based on analog transactions at many levels (membrane potentials, spike frequencies, firing thresholds, metabolic gradients, dendritic potentials, neurotransmitter concentrations, synaptic weights, etc., etc.). Further, the computer must be able to compute and work with probabilities, because cognition is largely probabilistic in the Bayesian sense, which means that our computer must also have a degree of plasticity.
            2.  The computer must also have distributed memory.  In the brain, especially the cortex, cognitive information is contained in a complex system of distributed, interactive and overlapping neuronal networks formed by Hebbian rules by association between temporally coincident inputs (i.e., sensory stimuli or inputs from other activated networks).  The cognitive “code” is therefore essentially relational or relativistic, and is defined by connective structure, by associations of context and temporal coincidence.  That is why, theoretically, connectionism and the connectome make some sense.
            3.  It is true that the soma of a neuron contains “memory”: in the mitochondria.  But that is genetic memory (what I call “phyletic memory,” memory of the species), some of which was acquired in evolution.  It is important for brain development and for the function of primary sensory and motor systems.  It is also important for regeneration after injury.  Further, it is the ground-base on which individual cognitive memory will be formed. But the latter consists of more or less widely distributed cortical networks or “cognits” (J. Cog. Neurosci. 21, 2047–2072, 2009).  These overlap and interact to a large extent, whereby a neuron or group of neurons practically anywhere in the cortex can be part of many networks, thus many memories or items of knowledge.  This is trouble for the connectome which, if ever comes to fruition, will be vastly more complex than the genome.
            4.  Our present tools to define the structure, let alone the dynamics, of the connectome appear rather inadequate to deal with those facts and hypotheses.   Consider DTI (diffusor tensor imaging), one of those tools presently in fashion and widely used to trace neural connections.  It is based on the analysis of the orientation of water molecules in a magnetic field.  Therefore, it can successfully visualize nerve fibers with high water content, such as myelinated fibers and some large unmyelinated ones.  But the method (I dub it “water-based paint”) is good for tractography, for visualizing large, fast conducting fibers, but not for the fine connective stuff that defines memory networks.
            5.  What’s more, those networks change all the time, even during sleep.  In sum, it is difficult to imagine a dynamic connectome that would instantiate the vicissitudes and idiosyncrasies of our thinking, remembering, perceiving, calculating and predictive brain.
            Some of this may be wrong.  But that’s the way I see it, and may be useful to model the real brain.  Cheers, Joaquín
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when adjust the threshhold well,"apply" icon is clicked.After that, a dialogue box "set background pixels to nan"popuping. If i tick it, the gray clolor of the images to be analyzed is going to became thin, and the fininal value of the mean intensity is less than the one in which the popup box not ticked.
Moreover, the mean density of area outside of selected region in mask by thresholding and using ticking "set background pixels to nan" is much higer than the  not ticking one.  e.g. 146vs NaN
So, how to understand "set background pixels to NaN" popuping box when using threshold function?
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I got it, Thank you very much!
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Hi! I have datasets of irregular spiking of neurons. I have calculated ISI of each spike train, and have computed the fano factor for each spike train as std(isi)^(2)/mean(isi). The values I get are way to high (~100), and for this type of neuron ought to be ~0.2.
Any suggestions about what I might be doing wrong?
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What is ISI in your experiment? Can u give a bit more detail? I'm assuming u aren't making any of the common programming mistakes (mixing variables, squaring the wrong variable, etc)
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For example, in Cessac 1995 "Increase in complexity in random neural networks" he proved that in the mean-field approximation the transition to chaos is very sharp, while in the real network it develops through the emergence of a limit cycle and a torus. Do you know other examples? I would like to know if there are cases when the mean-field approximation completely neglects important dynamical phenomena.Thanks in advance for any help you may provide.
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Sure: take the Curie-Weiss mean field theory of ferromagnetism. It predicts a second order phase transition, between a paramagnetic, high temperature phase and a ferromagnetic, low temperature phase,  in any dimension. Fluctuations eliminate the low temperature phase in one dimension for the Ising approximation and, also, in two dimensions, for the Heisenberg ferromagnet (the planar restriction has a special phase transition in d=2, the Kosterlitz-Thouless transition, that's completely different from the transition described by the mean field approximation). In two dimensions and three dimensions, fluctuations don't eliminate the low temperature phase, in the Ising approximation but the physical properties are considerably modified.  
Disorder can affect-or not-the phase diagram, sometimes quite significantly. Mathematical results may be found here: https://www.ias.edu/people/faculty-and-emeriti/spencer, here: http://www.mathunion.org/ICM/ICM1986.2/Main/icm1986.2.1312.1318.ocr.pdf  
For neural networks and disordered systems, a very nice reference is ``Field Theory, Disordered Systems and Simulations,  is http://www.worldscientific.com/worldscibooks/10.1142/1655, by Giorgio Parisi; cf., also, http://chimera.roma1.infn.it/GIORGIO/indexhome.htm and the home page of Marc Mézard, http://lptms.u-psud.fr/membres/mezard/lipub_recent.html and of Bernard Derrida, http://www.lps.ens.fr/~derrida/publi.html
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I would like to do segmentation of hippocampus using MATLAB coding. I would like to know, what feature of hippocampus is considered in segmentation which is different from its surrounding.
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I am not sure whether it would be of help to you but I would look at iTK-snap software; I used it to segment hippocampus to create a ROI template; they actually give an example of segmentation of hippocamus. It is s a free software.
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Dear experts,
My experiment was 2*3 mixed design, with group (3 levels) as a between-group factor, and session (2 levels, pre- and post-intervention) as a within-group factor. I used the flexible factorial module in SPM8 to explore the main effects of session and the group*session interaction. Since insula was a region of interest based on previous studies, I selected the insula from AAL atlas and generated a mask using PickAtlas. I then selected this mask in Explicit Mask option in factorial design specification to do the ROI analysis. However, errors occurred in model estimation step, and the detailed information was in attached figure 1 and 2. I am really appreciated if anyone could help me solve this problem.
Thank you in advance.
Yuan-Wei
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Hi Ioana,
Thank you for your very helpful comments!
I will check again if there was any problems in data analysis. If not, there may be something wrong in the experiment design or hypotheses generating.
Thank you again for your reply.
Best,
Yuan-Wei
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Is there any video editing software that can measure till milliseconds? I need to know the exact milliseconds time latency of Macaca fascicularis' behavioral response right after a predator shows up.
My video is at 30 fps. and i know that the lowest millisecond which can be achieved is 33,33 ms. Is it possible though using that kind of software to observe what happen in <33,33 ms. Or should I really change my camera with better precision? Thank you 
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You need a high-framerate camera for the purpose, the higher, the best temporal resolution you shall have (and more precise results). if you work on unix/Linux based computer, you may ask for someone to program that for you. i heard that VirtualDub is somewhat useful.
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There are several interesting papers on ways to measure emotions. For instance, Klaus R. Scherer What are emotions? And how can they be measured?, http://lep.unige.ch/system/files/biblio/2005_Scherer_SSI.pdf
The topic is wide, so contributions come from different scientific areas. My interest is related to applications sprung from IOT. Could you provide hints or examples of brilliant solutions?
Many thanks!
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Dear Riccardo
I think, it's better to check a paper about using IoT in Public Health. it maybe give you some good ideas.
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Any recommended textbook about Brain Computer Interface? I already use Recent Advances in Brain-Computer Interface Systems by Reza Fazel-Rezai, any other easy understanding books?
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Actually depends on interest/topic within BCI, clinical or otherwise oriented, invasive-noninvasive oriented, more hands-on etc. In any case, would recommend the following to start with:
Schalk, G., & Mellinger, J. (2010). A Practical Guide to Brain–Computer Interfacing with BCI2000: General-Purpose Software for Brain-Computer Interface Research, Data Acquisition, Stimulus Presentation, and Brain Monitoring. Springer Science & Business Media. http://www.amazon.com/dp/1849960917
Dornhege, G. (Ed.). (2007). Toward brain-computer interfacing. MIT press. http://www.amazon.com/gp/product/0262042444/
Rao, R. P. (2013). Brain-computer interfacing: an introduction. Cambridge University Press. http://www.amazon.com/gp/product/0521769418/
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Can someone explain how to get the dielectric permittivity and conductivity of a neuron cell?
I am making the voxel of a neuron cell to investigate affect of electromagnetic wave on it.So I need to have biological parameters of it.In my model there are 3 parts and I want to assign every part with a permittivity and conductance .These three parts are:1) extracellular side, 2) membrane of the cell  and  3) inner of the cell or cytoplasm.
Thank you
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If you are in electrophysiology, you may want to use voltage-clamp and measure the membrane currents in response to small hyperpolarizing step-pulses. Integrating the current yields the charge Q which you can plot to several voltage pulses used to obtain capacitance C from the slope C=dQ/dV. Make sure to use either hyperpolarizing and/or sub-threshold depolarizing pusles not activating non-linear currents from voltage-gated ion channels.
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In 'Eigenvalue Spectra of Random Matrices for Neural Networks' (PRL, 2006) Rajan describes the conditions for a random connectivity matrix of a stable linear rate network. These conditions are: full connectivity, variance <= 1/sqrt(N) and rowsum=0 (the net input to each neuron). Sticking to this parameters all eigenvalues are shown to fall into a circle of radius 1 in the complex plane, which guarantees stability. It amazes me to see that this stability does not depend on the mean strength of the synapses (provided they are balanced). The article states 'Our results were obtained using Gaussian distributions,but they apply more generally. For example, they apply even though synaptic strength distributions are non-Gaussian [15] and include a zero-strength delta function due to the sparseness of neuronal connectivity.' But when I calculate the eigenvalues of sparse matrices, for example with connection probability of 0.1, I find that the radius of the circle does depend on the mean values (eventhough they are balanced for each neuron). My question is, how do I calculate how large mean and variance can maximally be for a given connection probability smaller 1?
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This is a good question.
Properties of sparse neural networks are considered in
S. Waydo, Explicit object representation by sparse neural codes, Ph.D. thesis, Caltech, 2008:
See Section 2.1.4 (Trevor-Rolls Sparseness), starting page 12).   A probability density function for a neurone's response rates is considered in terms of the mean and variance (equation 2.4).    The sparseness is small if the variance is large compared with the mean,  i.e., when a neuron has widely separated responses to different stimuli. 
Sparseness measures are considered in detail in Section 2.1, starting on page 8.   It is observed that sparseness can be very difficult to define and quantify.   Perhaps you will find the selectivity index (Section 2.1.5, starting on page 13) of interest, since it considers threshold values between maxima and minima of responses.
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Hello - 
I plan to screen neural circuits for a certain behavior using GAL4/UAS system. I am unsure about which toolkits I should take to activate or inactivate neurons. From what I can find, people usually use TNT/TrpA/kir2.1/etc for such procedure. For my purpose, it seems more reasonable to block neuron activity rather than activate it. So I guess I need to choose between TNT and kir2.1. Do anyone know the differences between these two?
Thanks,
Yang 
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Hello,
there is an article published recently in Genetics by Pauls et al., Potency of Transgenic Effectors for Neurogenetic Manipulation in Drosophila Larvae. There are many effectors for manipulating diverse neurons described and compared! May be it can be useful for you!
Best,
Radostina
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Could you refer me to cognitive models that explain the types of errors in typing text and ways to correct them?
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You asked about cognitive models of typing errors.  Such models have been developed in the ACT-R architecture and are available on the associated webpage:
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I am trying to use ImageJ/Fiji to count neurons stained for their nuclei that are only surrounded by a different stain. As such, I do not believe making the image binary will be the best option for me, as it will count all the cells, even the ones without the stain surrounding it. 
I have tried the plugin "Nucleus Counter" as an option but I have found that it uses 16-/8-bit images and therefore I cannot adjust for the color threshold. Also the shape of the cells I am counting are irregular and this plugin often mistakes 1 cell as 2 cells etc..
Is there a different way of going about this task?
Thank you in advance
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When I used ImageJ to detect the number of spots with a certain color (I had red/ green and blue spots) I know I made the image binary.First I split the image in the 3 color channels and analyzed only the one where my color of interest is (blue for instance). I assume there will be more blue in the images where the stain is, and then you can make the binary image based on the threshold you define. This way you can count the stained nuclei. 
Additionally you can use this method if you have 2 or 3 different colors. 
Another method is to make a Matlab script which is more versatile because you can define your thresholds more accurate. 
I hope it helps. 
Best, 
Felicia
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I'm wondering whether there exists a Psychtoolbox demo, or some other example code, for the dot probe task (or something similar that could be adapted)? I should be able to write the code myself, but don't want to re-invent the wheel unnecessarily! I couldn't see anything in the PTB demos folder.
Thank you in advance!
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Suggestion:  whatever you do, calibrate the screen afterwards by measuring the time between the target and the probe. Make both really large so there is a enough light to trigger the photo-detector (for the purpose of calibration)..
this way you can be sure that the timing you ask for is the one generated by the program.  (It often is not, unless you have special hardware to create a 'movie mode' and know how to pre-load all the frames before each trial, and how to present the movie at the right time during the trial.)
adam reeves
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Do you research on this issue? In search of the neural code investigated various characteristics of the reaction of the neuron. However, the variability in their response to the same stimuli is indicating that they are not suitable to be the only candidate. There are hypotheses about the multiparameter coding. But maybe these studies are an attempt to "find a black cat in a dark room, if it does not exist there"? Obviously, there is a statistical correlation parameter of any input and output signals of the neuron. In my opinion, requires only one reliable candidate for the role of neural code. Otherwise we cannot consider the neuron as a system. 
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Why answers disappeared?
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Do you research on this issue? In search of the neural code investigated various characteristics of the reaction of the neuron. However, the variability in their response to the same stimuli is indicating that they are not suitable to be the only candidate. There are hypotheses about the multiparameter coding. But maybe these studies are an attempt to "find a black cat in a dark room, if it does not exist there"? Obviously, there is a statistical correlation parameter of any input and output signals of the neuron. In my opinion, requires only one reliable candidate for the role of neural code. Otherwise we cannot consider the neuron as a system.
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Dear Yuri,
Yes, very interesting question. Nevertheless there are many correlated questions. On one side a first question is , where is the code expressed? In the spike sequence? If so variability can depend on the previous history of the neuron, on experimental condition and on the diverse state of the neuron in the moment it receive the stimulus. What in the sequence express the code? The mean inter spike interval and correlated statistics? Could be since seems that spikes are the main output of the neuron and the way they transmit information information to other neurons. But what about the synapses. A single pyramidal neuron can have between 5000 and 60000 synapses that influences the output activity of the neuron. Each of these synapse belong to a neuron who has its own state with its variability in his spike generation. Are we sure that the spike sequence is the code? Can we exclude that spikes are only reset systems and that information transferred to other neurons are not as for example "Hi now I stop working time to work you?". We expect that code generation is of computational type, related to non linear process of neuron that end with a spike when "electrical activity" reach a threshold. We expect that code is of binary and some time of decimal or exadecimal type. I used this system too and model according to this idea, but I am not sure it is the place where to find the code. I am not at all sure that neuron has a digital type code. I think it is the most suitable for our scientific culture but this does not means necessarily that it is the right one. Another point. Why we should expect a single code for all type of neurones? In different area of brain they process different stimuli. They perform taskes very different each other. Why they should use a single code for all these activities? As a metaphor, it is the same as to expect that People of all the world speak the same language (uses the same code). All uses words (spikes) but languages are different (code) in grammar (spike sequence) and in words (spike duration and amplitude). So yes, it is a very interesting question the one you posted, but discussion on it can be very long, tedious and difficult. 
All the best.
Vito
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We have an artificial neural network to detect patterns in input. training the system to learn specific pattern when the position of pattern is constant is easy and it can be done by feed forward neural network and using back propagation. But the question is that how can we make it position invariant so it can detect the pattern in any place of input ?
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weight sharing just can do on a little bit shift . it can not do on big shift.
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does anyone has some Ideas about the relationship between these two phenomena?
aren't they the same thing with different names?
if they are not, what is their exact difference?
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Hi Abolfazl,
neuronal avalanches are very different from waves.  You can imagine an avalanche as a single pulse that propagates through whatever medium iwith an equal likelihood to continue or die out in the next time step/generation.  This is best modeled by a critical branching process.  the resulting avalanches form distinct spatiotemporal patterns that selectively branch different points in space.  avalanches have in common with waves that most of their propagation is carried out by local interactions, like dominos that topple and the cascade of toppling dominoes bridges long distances.  The important general aspect of avalanches is that they indicate a particular, critical state of the system which has many advantages for information processing. 
hope that helps,
best
dietmar plenz
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We need to obtain the kinematic viscosity coefficient of the brain. This ratio is calculated by dividing the coefficient of dynamic viscosity and density of the brain.
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Yes the kinematic viscosity is the dynamic viscosity divided on the mass density, but the dynamic viscosity of brain can not be measured  since the brain is not liquid at all.
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I am working on invariant object recognition problem. Now, i required to compare my model with CNNs. I am looking for an open source code for CNNs. Please let me know if there is open code for CNNs.
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You can take a look in:
There are three implementations of convultional NNs there.
Hope this helps
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I want to model a regular spiking basket cell in NEURON. Of course there is a lot of ion-currents, that are involved here, but I am searching for the minimal amount of ionic currents that can describe this cell, with a fairly good approximation. I would be grateful if someone could advise me on this subject with the names of the currents (such as Ia -the A-type potassium current.... etc) that are enough to model this cell. And also, how do these parameters depend on the morphology of the cell, besides the density. Is it enough to have a standard ionic current, and insert it to a reconstructed cell, or is it crucial to have these currents from the cell, where the model came from?
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Have you checked Model DB (http://senselab.med.yale.edu/modeldb/default.asp)? Just to be sure that you have made first, most general step...
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Reason for neuro degenerative diseases is its inability of regeneration, why its so?
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Neurons from brain and spinal cord (i.e., central nervous system or CNS) do not regenerate well but the precise mechanisms underlying regenerative failure are still not known. We do know that there are "roadblocks" present in the diseased or injured CNS that thwart attempts by the neuron to regenerate its axons. These roadblocks are diverse and complex but include different molecules found in the extracellular matrix (e.g., CSPGs) and proteins that become exposed once the CNS is injured. Within the neurons, there also are certain genetic "brakes" that after development of the CNS, are turned on. Research is beginning to show that silencing these genes can turn on growth programs. Many exciting research projects are now focused on trying to silence different genes (e.g., Pten) that inhibit neuron growth or alternatively, overexpress genes that favor growth programs. 
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The criticality hypothesis asserts that the brain is a critical system, like a paramagnetic material in the critical temperature. being in the critical state can maximize the repertoire of a system (physicists call it susceptibility) beside unpredictability and coherence of states.
There are experimental evidences for it, neural avalanches show a great similarity to critical systems. Dante chialvo and other physicists provided a good understanding of the critical brain in recent years.
BUT, can Criticality hypothesis spark a new understanding in the field of consciousness research?
A.A
conscioustronics
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Thank you Abolfazl, I will attempt to though I despair of the details. It seems that Maclean got the Reptile part of the brain wrong. Mammals branched off before reptiles in the Cade analysis.
In any case, the idea is that evolution has brought a number of versions of the brain into existence over time. While the exact nature of the evolution of the brain is still controversial the idea is that Human consciousness is the result of a process of evolution that resulted in the development of the modern brain.
The evolution of the brain brings with it, a more elaborated brain, with greater capabilities by picking up on specific epoches in the process we can see a sort of progression from basic core consciousness to a more fully elaborated human consciousness.
While I am not sure of the exact epoches to use, and we can be sure that within those epoches some species may tend to reduce their elaborations rather than increase them, The idea is that the process of elaboration took time, and was done in stages.
We only have the end results to work from, which means that we have lost some of the information in between losing some of the enabling steps due to die offs and extinctions. As well, evolutionary evidence does not stay still, some portion of the evidence is always changing, so even the less elaborated brains that exist today are probably not the same as the ones that existed in prehistoric times.
But beyond all those caveats, we can see a sort of progression of elaborations that might explain the eventual development of the human brain.
I am currently studying an introductory work on comparative neuro-anatomy of Vertebrates under the mentorship of John LaMuth. So hopefully my theory of consciousness will develop under his tutelage.
In any case, the idea of brain development in stages brings with it, the idea of a progression of stages that results eventually in the human brain. This should be seen as different from scala Naturalae in that it doesn't assume that the human brain is the pinnacle of evolution, just the most elaborated of the brains under study.
Evolution moves in both the direction of elaboration and simplification, depending on the ecological niche of the species, so it is possible that a simplified species is actually better evolved for its niche than humans are for theirs.
Whether or not we look to cade analysis, consciousness probably developed in stages along with the brain. My research is attempting to find a model that will explain the complexity of human consciousness in the best way possible while it assures that the complexity grows according to recognizeable steps.
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