Gordon M. Shepherd’s research while affiliated with Northwestern University and other places

What is this page?


This page lists works of an author who doesn't have a ResearchGate profile or hasn't added the works to their profile yet. It is automatically generated from public (personal) data to further our legitimate goal of comprehensive and accurate scientific recordkeeping. If you are this author and want this page removed, please let us know.

Publications (316)


ModelDB
  • Chapter

June 2022

·

10 Reads

·

1 Citation

·

Rixin Wang

·

·

[...]

·

Gordon M. Shepherd


Multiscale Model of Primary Motor Cortex Circuits Predicts in vivo Cell Type-Specific, Behavioral State-Dependent Dynamics

January 2022

·

24 Reads

·

2 Citations

SSRN Electronic Journal

Understanding cortical function requires studying multiple scales: molecular, cellular, circuit, and behavioral. We develop a multiscale, biophysically detailed model of mouse primary motor cortex (M1) with over 10,000 neurons and 30 million synapses. Neuron types, densities, spatial distributions, morphologies, biophysics, connectivity, and dendritic synapse locations are constrained by experimental data. The model includes long-range inputs from seven thalamic and cortical regions and noradrenergic inputs. Connectivity depends on cell class and cortical depth at sublaminar resolution. The model accurately predicts in vivo layer- and cell-type-specific responses (firing rates and LFP) associated with behavioral states (quiet wakefulness and movement) and experimental manipulations (noradrenaline receptor blockade and thalamus inactivation). We generate mechanistic hypotheses underlying the observed activity and analyzed low-dimensional population latent dynamics. This quantitative theoretical framework can be used to integrate and interpret M1 experimental data and sheds light on the cell-type-specific multiscale dynamics associated with several experimental conditions and behaviors.


FIGURE 1 | The cellular composition and basic connectivity of the mammalian olfactory pathway according to Ramon y Cajal. A, olfactory sensory neuron; B, olfactory axon endings in olfactory glomerulus; C, mitral cell; D, granule cell; E, lateral olfactory tract; a, tufted cell; b, terminals of mitral cell recurrent axon branch; c, granule cell dendritic branches; e, mitral cell recurrent axon. On the right, the beginning of the anterior olfactory cortex and piriform cortex. Ramon y Cajal, Cajal Institute. From Cajal (1894); see also Figueres-Oñate et al. (2014).
FIGURE 2 | Orderly spatial activity patterns elicited in the rodent glomerular sheet by different odor molecular types using different methods. (A) Schematic view of olfactory epithelium divided into four zones, with representative OSNs projecting to a single glomerulus in the olfactory bulb (OB). In the OB, fMRI activity patterns are elicited in the entire glomerular sheet by a four carbon aldehyde. Red: strong response; yellow: moderate response; blue: weak response. (B) Maps of activity elicited in the whole OB by 4, 5, and 6 carbon aldehydes. Brackets show the part of the lateral OB seen in (A). (C) Results of optical experiment limited to the rodent dorsal OB, recording intrinsic signals of responses to aldehydes of increasing carbon length, from short (red), medium (green), and long (blue) carbon chains. Map orientation of the dorsal OB is lateral up, medial down, anterior left, posterior right. (D) Intrinsic signals in the dorsal OB for stimulation with odors due to alcohols of increasing carbon lengths. The experiments confirmed burst firing of cells recorded in the circled areas. (A,B) Xu et al. (2003); (C,D) Uchida et al. (2000).
FIGURE 3 | Microcircuit organization of a glomerular unit. B, Blanes (deep short-axon) cell; c, centrifugal axon; ET, external tufted cell; Gm, mitral connected granule cell; Gt, tufted cell connected granule cell; M, mitral cell; PGe, periglomerular cell (input from ET cell); PGo, periglomerular cell (input from OSN); sSA, superficial short-axon cell; T, middle tufted cell. Excitatory actions shown by red cells and terminals; inhibitory by blue cells and terminals. All cells except the M and T cells turn over during life. Note the multiple layers for lateral inhibitory and related processing actions. The complex patterns of glomerular synapses are under active investigation. From Shepherd et al. (2020), representing a synthesis of studies by multiple authors, including Cleland and Sethupathy (2006); Wachowiak and Shipley (2006), Migliore et al. (2010); Nagayama et al. (2014), Burton and Urban (2015), and Cavarretta et al. (2016).
FIGURE 4 | Example of a microcircuit function: lateral inhibition in the retina and olfactory bulb. (A) Lateral inhibition in the retina; central excitation of a retinal ganglion cell is surrounded by inhibition, a classical example of spatial contrast enhancement, a fundamental operation in processing spatial patterns in sensory systems. From Kuffler (1953). Center-surround inhibition also underlies color contrast mechanisms in the retina (see text). (B) Example of contrast enhancement by "lateral" inhibition in the olfactory bulb, in a chemical series of aldehydes of differing carbon lengths, which heightens contrast between odor molecules by excitation of a mitral cell by one odor molecule type (n-hexylaldehyde 6CHO) and inhibiting responses to neighboring related odor molecules (4)CHO and (8)CHO in the series. Based on Yokoi et al. (1995).
FIGURE 5 | Update of Cajal's olfactory pathway in Figure 1 based on new knowledge of microcircuits. Close interactions between olfactory bulb and olfactory cortex can be seen to form a multiregional multidimensional processing unit underlying olfactory perception. Red indicates excitatory synaptic action, blue inhibitory synaptic action. Arrows indicate direction of impulse propagation and synaptic action. GLOM, glomeruli; GC, granule cell; INH, inhibitory interneuron; LOT, lateral olfactory tract; MOD, modulatory systems; MT, mitral/tufted cell; PN, pyramidal neuron; OFC, orbitofrontal cortex. Asterisk denotes excitatory inputs to the granule cell from multiple sources (see text). Note that lateral circuits in the olfactory cortex for processing the sensory input from the LOT are in the excitatory and inhibitory layers closest to the LOT, whereas circuits for associative processing are closest to the PN cell bodies. Feedforward inhibition predominates in the APC, inhibitory feedback in the PPC. Based on many authors (see text).

+3

An Evolutionary Microcircuit Approach to the Neural Basis of High Dimensional Sensory Processing in Olfaction
  • Article
  • Full-text available

April 2021

·

183 Reads

·

13 Citations

Frontiers in Cellular Neuroscience

Odor stimuli consist of thousands of possible molecules, each molecule with many different properties, each property a dimension of the stimulus. Processing these high dimensional stimuli would appear to require many stages in the brain to reach odor perception, yet, in mammals, after the sensory receptors this is accomplished through only two regions, the olfactory bulb and olfactory cortex. We take a first step toward a fundamental understanding by identifying the sequence of local operations carried out by microcircuits in the pathway. Parallel research provided strong evidence that processed odor information is spatial representations of odor molecules that constitute odor images in the olfactory bulb and odor objects in olfactory cortex. Paleontology provides a unique advantage with evolutionary insights providing evidence that the basic architecture of the olfactory pathway almost from the start ∼330 million years ago (mya) has included an overwhelming input from olfactory sensory neurons combined with a large olfactory bulb and olfactory cortex to process that input, driven by olfactory receptor gene duplications. We identify a sequence of over 20 microcircuits that are involved, and expand on results of research on several microcircuits that give the best insights thus far into the nature of the high dimensional processing.

Download

Figure 3. Cuneate!VPL circuit analysis. (A) Schematic of injection strategy: PO was injected with AAVretro-GFP and VPL was injected with AAVretro-RFP. (B) Left: Coronal section showing injection sites in VPL and PO. Middle: Sagittal section showing labeled VPL-projecting neurons in the cuneate nucleus. Right: Same, showing absence of PO-projecting neurons in the same region. (C) Same, but with coronal sections, at the level of the right thalamus (left) and left cuneate (middle and right). (D) Labeled PO-projecting neurons in the trigeminal nucleus. (E) Schematic of injection strategy: forelimb S1 was injected with a retrograde tracer CTB647, and cuneate nucleus was injected with AAV-eGFP. (F) Left: Labeling at site of AAV-eGFP injection in the cuneate nucleus (left side of the brainstem). Middle: Labeled cuneothalamic axons in VPL thalamus. Right: VPL S1-proj neurons are situated within the field of labeled cuneothalamic axons. (G) Schematic of injection strategy: the cuneate nucleus was injected with AAV-ChR2. (H) Example traces showing strong excitatory synaptic responses recorded in a VPL neuron in a thalamic brain slice, evoked by photostimulation of ChR2-expressing cuneothalamic axons. (I) Example traces (left) and group data (right) showing strong synaptic depression of responses to trains of photostimuli (amplitude of the i th response divided by that of the first; gray, individual neurons; black, group mean).
Figure 5. PO axons mainly excite L5A M1-proj neurons in S1. (A) Schematic of injection strategy: the PO was injected with AAV-hChR2, and the forelimb M1, PO, and/or C6 spinal cord with retrograde tracer(s) (CTB647 and/or red Retrobeads). (B) Left: coronal section showing labeling at the injection site in PO (green). Right: coronal section showing labeled PO axons (green) ramifying primarily in L1 and L5A of S1, and also showing the retrogradely labeled corticospinal neurons (red). (C) Left: example traces of EPSCs evoked by photostimulating the ChR2-expressing PO axons, recorded in L2/3 M1-proj , L4, and L5 M1-proj neurons in S1. Middle: Histogram of the normalized cortical depths of each of the S1 cell types sampled. Numbers of cells per group are given in parentheses below the cell type labels. Right: Plot of EPSC amplitudes recorded in the three types of postsynaptic S1 neurons. Asterisks (*) indicate significant differences between groups (details in main text). (D) Same, comparing PO inputs to L5A and corticospinal C6-proj neurons in S1. (E) Same, comparing PO inputs to L5A and L5B PO-proj neurons in S1. (F) Same, comparing PO inputs to L5A and L6 PO-proj neurons in S1. (G) Schematic summary of the main findings.
Figure 6. Corticocortical axons from S1 mainly excite L2/3 neurons in M1. (A) Schematic of injection strategy: the cervical spinal cord was injected at level C6 with retrograde tracer (red Retrobeads), and hand S1 was injected with AAV-DIO-hChR2, in a Tlx3-Cre mouse. (B) Left: Coronal section at the level of hand S1, showing labeling primarily of L5A neurons at the site of injection (arrow). Corticospinal neurons in L5B are also observed (red; red arrow). White arrowhead marks the approximate location of the medial border of hand S1. Center: Same, for a more anterior coronal section at the level of hand M1. Right: Same, showing an enlarged view of the labeling pattern in forelimb M1. (C) Left: Example traces of EPSCs evoked by photostimulating the ChR2-expressing S1 axons, recorded in L2/3, L5A, L6, and corticospinal C6-proj neurons in M1. Middle: Histogram of the normalized cortical depths of each of the S1 cell types sampled. Numbers of cells per group are given in parentheses below the cell type labels. Right: Plot of EPSC amplitudes recorded in the four types of postsynaptic M1 neurons. (D) Schematic summary of the main findings. (E-H) Same, but using shallow injections in S1 to label L2/3 neurons, to analyze the S1-L2/3!M1 connections.
Figure 7. Summary wiring diagram of the major excitatory connections along the hand/forelimb-related somatosensory-to-motor transcortical circuit. The thickest arrows emphasize the strongest connections. The lemnisco-cortical circuit, arising from the cuneate nucleus, traverses the VPL via strong, depressing-type excitatory connections, and primarily targets L4 neurons in hand-related S1. In hand S1, similar to other sensory areas, L4 neurons connect strongly to L2/3 neurons. Neurons in both L2/3 and L5A in turn project to M1, forming convergent excitatory connections onto L2/3 neurons there. Strong local L2/3 connections to corticospinal neurons form the last connection to close the circuit leading back to the cervical spinal cord and the motor neurons controlling the forelimb musculature.
Circuit organization of the excitatory sensorimotor loop through hand/forelimb S1 and M1

April 2021

·

136 Reads

·

46 Citations

eLife

Sensory-guided limb control relies on communication across sensorimotor loops. For active touch with the hand, the longest loop is the transcortical continuation of ascending pathways, particularly the lemnisco-cortical and corticocortical pathways carrying tactile signals via the cuneate nucleus, ventral posterior lateral (VPL) thalamus, and primary somatosensory (S1) and motor (M1) cortices to reach corticospinal neurons and influence descending activity. We characterized excitatory connectivity along this pathway in the mouse. In the lemnisco-cortical leg, disynaptic cuneate→VPL→S1 connections excited mainly layer (L) 4 neurons. In the corticocortical leg, S1→M1 connections from L2/3 and L5A neurons mainly excited downstream L2/3 neurons, which excite corticospinal neurons. The findings provide a detailed new wiring diagram for the hand/forelimb-related transcortical circuit, delineating a basic but complex set of cell-type-specific feedforward excitatory connections that selectively and extensively engage diverse intratelencephalic projection neurons, thereby polysynaptically linking subcortical somatosensory input to cortical motor output to spinal cord.


Predicting brain organization with a computational model: Fifty year perspective on lateral inhibition and oscillatory gating by dendrodendritic synapses

July 2020

·

37 Reads

·

5 Citations

Journal of Neurophysiology

The first compartmental computer models of brain neurons using the Rall method predicted novel and unexpected dendrodendritic interactions between mitral and granule cells in the olfactory bulb. We review the models from a 50 year perspective on the work that has challenged, supported and extended the original proposal that these interactions mediate both lateral inhibition and oscillatory activity, essential steps in the neural basis of olfactory processing and perception. We highlight strategies behind the neurophysiological experiments and the Rall method that enhance the ability of detailed compartmental modeling to give counter intuitive predictions that lead to deeper insights into neural organization at the synaptic, circuit, and behavioral level. Extensions of the methods to mechanisms of neurogenesis and plasticity are exciting challenges for the future.


Fig. 1. (A) Process of converting 3D MRI data to a flatmap of the glomerular sheet (GS). The glomerular layer is traced on each coronal slice, the trace is cut at the bottom and flattened to a row, and then all such rows are combined to form the flatmap. Within each slice of the MRI data, the layer mask forms a ring shape, or an inverted-U shape. Thus, each row of the coronal image has either one or two sections of the layer in it. If there are two sections, these are the left and right sides of the layer. Arranging these voxels from left-bottom to left-top, concatenated with right-top to right-bottom converts the layer into a one-voxel row in the flatmap. As one row corresponds to one slice in the original MRI, combining all of the rows in slice order creates a flatmap. (B) Orientation of each flatmap. This flatmap will have a rostral-caudal dimension, assuming rows from coronal slices were concatenated. The other dimension will go ventral-medial-dorsal-lateral-ventral based on how it was unwrapped. Thus, a 2D image is produced of the layer. Code is provided in (Thompson et al., 2018).
Fig. 2. (A) Glomerular sheet activation for methyl valerate (MV) and ethyl butyrate (EB) odor during orthonasal stimulation (n ¼ 6 rats). Percentage change in fMRI BOLD activation maps was calculated between baseline versus odor stimulation. (B) As A, except retronasal stimulation (n ¼ 5 rats). While all odors are similar (all are esters, fruit-like odors), there are slight differences in the activation patterns between them. Odor concentrations were different for ortho (20%) versus (40%) retronasal stimulation, needed to make BOLD signal amplitudes more similar. Orientation of all flat maps is shown on the right side. Reproducibility maps across trials and subjects were shown in Supplemental Fig. 1.
Orthonasal versus retronasal glomerular activity in rat olfactory bulb by fMRI

February 2020

·

136 Reads

·

24 Citations

NeuroImage

Odorants can reach olfactory receptor neurons (ORNs) by two routes: orthonasally, when volatiles enter the nasal cavity during inhalation/sniffing; and retronasally, when food volatiles released in the mouth pass into the nasal cavity during exhalation/eating. Previous work in humans has shown that orthonasal and retronasal delivery of the same odorant can evoke distinct perceptions and patterns of neural responses. Each delivery route is known to influence specific responses across the glomerular sheet of the dorsal parts of the olfactory bulb (OB), but spatial distributions across the entire glomerular sheet throughout the whole OB remain largely unexplored. We used functional MRI (fMRI) to measure and compare activations across the entire glomerular sheet in rat OB resulting from both orthonasal and retronasal stimulations of the same odors. We observed reproducible fMRI activation maps of the whole OB during both orthonasal and retronasal stimuli. However, retronasal stimuli required double the orthonasal odor concentration for similar response amplitudes in the OB. Regardless, both the magnitude and spatial extent of activity were larger during orthonasal versus retronasal stimuli for the same odor. Orthonasal and retronasal response patterns show overlap as well as some route-specific dominance. Orthonasal maps were dominant in dorsal-medial regions, whereas retronasal maps were dominant in caudal and lateral regions. These different whole OB encodings likely underlie differences in odor perception between these biologically important routes for odorants among mammals. These results establish the relationships between orthonasal and retronasal odor representations in the rat OB.


Automated Metadata Suggestion During Repository Submission

July 2019

·

280 Reads

·

8 Citations

Neuroinformatics

Knowledge discovery via an informatics resource is constrained by the completeness of the resource, both in terms of the amount of data it contains and in terms of the metadata that exists to describe the data. Increasing completeness in one of these categories risks reducing completeness in the other because manually curating metadata is time consuming and is restricted by familiarity with both the data and the metadata annotation scheme. The diverse interests of a research community may drive a resource to have hundreds of metadata tags with few examples for each making it challenging for humans or machine learning algorithms to learn how to assign metadata tags properly. We demonstrate with ModelDB, a computational neuroscience model discovery resource, that using manually-curated regular-expression based rules can overcome this challenge by parsing existing texts from data providers during user data entry to suggest metadata annotations and prompt them to suggest other related metadata annotations rather than leaving the task to a curator. In the ModelDB implementation, analyzing the abstract identified 6.4 metadata tags per abstract at 79% precision. Using the full-text produced higher recall with low precision (41%), and the title alone produced few (1.3) metadata annotations per entry; we thus recommend data providers use their abstract during upload. Grouping the possible metadata annotations into categories (e.g. cell type, biological topic) revealed that precision and recall for the different text sources varies by category. Given this proof-of-concept, other bioinformatics resources can likewise improve the quality of their metadata by adopting our approach of prompting data uploaders with relevant metadata at the minimal cost of formalizing rules for each potential metadata annotation.


Neuron Names: A Gene- and Property-Based Name Format, With Special Reference to Cortical Neurons

March 2019

·

483 Reads

·

31 Citations

Frontiers in Neuroanatomy

Precision in neuron names is increasingly needed. We are entering a new era in which classical anatomical criteria are only the beginning toward defining the identity of a neuron as carried in its name. New criteria include patterns of gene expression, membrane properties of channels and receptors, pharmacology of neurotransmitters and neuropeptides, physiological properties of impulse firing, and state-dependent variations in expression of characteristic genes and proteins. These gene and functional properties are increasingly defining neuron types and subtypes. Clarity will therefore be enhanced by conveying as much as possible the genes and properties in the neuron name. Using a tested format of parent-child relations for the region and subregion for naming a neuron, we show how the format can be extended so that these additional properties can become an explicit part of a neuron’s identity and name, or archived in a linked properties database. Based on the mouse, examples are provided for neurons in several brain regions as proof of principle, with extension to the complexities of neuron names in the cerebral cortex. The format has dual advantages, of ensuring order in archiving the hundreds of neuron types across all brain regions, as well as facilitating investigation of a given neuron type or given gene or property in the context of all its properties. In particular, we show how the format is extensible to the variety of neuron types and subtypes being revealed by RNA-seq and optogenetics. As current research reveals increasingly complex properties, the proposed approach can facilitate a consensus that goes beyond traditional neuron types.


Figure 3: Specification of dendritic distribution of synapses. A) Experimental data showing the density of synapses across a 2D grid. This data can be directly used as part of NetPyNE's high-level network specifications and used to automatically calculate the location of synapses in different cell morphologies. B) Example synaptic density plot generated by NetPyNE based on experimental data shown in A).
Figure 4: Instantiated network example showing hierarchical data model. The instantiated network is represented using a Python object and is organized hierarchically in a standardized format. This makes it easy to access any element or parameter of the network. NEURON objects (boxes with red border) are embedded within this Python structure.
Figure 6: NetPyNE visualization and analysis plots for a simple 3-layer network example A) Connectivity matrix, B) stacked bar graph, C) 2D representation of cells and connnetions, D) voltage traces of 3 cells, E) raster plot, F) population firing rate statistics (boxplot).
Figure 7: LFP recording and analysis. A) LFP signals (left) from 10 extracellular recording electrodes located around a morphologically detailed cell (right) producing a single action potential (top-right). B) LFP signals, PSDs and spectrograms (left and center) from 4 extracellular recording electrodes located at different depths of a network of 120 5-compartment neurons (right) producing oscillatory activity (top-left).
Figure 8: NetPyNE graphical user interface (GUI) showing a multiscale model. Background shows 3D representation of example network with 6 populations of multi-channel multi-compartment neurons (background); plots from left to right: cell traces (voltage, intracellular and extracellular calcium concentration, and potassium current); raster plot; extracellular potassium concentration; LFP signals recorded from 3 electrodes; and 3D location of the LFP electrodes within network.
NetPyNE: a tool for data-driven multiscale modeling of brain circuits

November 2018

·

896 Reads

·

1 Citation

Biophysical modeling of neuronal networks helps to integrate and interpret rapidly growing and disparate experimental datasets at multiple scales. The NetPyNE tool ( www.netpyne.org ) provides both programmatic and graphical interfaces to develop data-driven multiscale network models in NEURON. NetPyNE clearly separates model parameters from implementation code. Users provide specifications at a high level via a standardized declarative language, e.g., a connectivity rule, instead of tens of loops to create millions of cell-to-cell connections. Users can then generate the NEURON network, run efficiently parallelized simulations, optimize and explore network parameters through automated batch runs, and use built-in functions for visualization and analysis – connectivity matrices, voltage traces, raster plots, local field potentials, and information theoretic measures. NetPyNE also facilitates model sharing by exporting and importing using NeuroML and SONATA standardized formats. NetPyNE is already being used to teach computational neuroscience students and by modelers to investigate different brain regions and phenomena.


Citations (66)


... These limitations highlight the need for more detailed and accurate modeling of neuronal dynamics, the integration of cortical influences and developmental changes, and continuous experimental validation to strengthen hypotheses and improve model accuracy. A crucial consideration is the presence of a corticothalamic pathway, where layer VI pyramidal neurons excitatorily project onto thalamic Rs. 22,25,26 In light of the described pattern completion process, this pathway could enable direct cortical influence on the reticular inhibition process in the relay layer. Phenomena such as perception, hallucinations, dreams, and consciousness 17 may, to some extent, find their roots in the processes simulated by our network. ...

Reference:

Evaluating the cognitive and electrophysiological plausibility of a thalamic computational model
Handbook of Brain Microcircuits
  • Citing Article
  • August 2010

... However, examination of a newborn's fontanelles or an adult's brain either during open-skull surgery, or under phase-based-motion-amplified magnetic resonance imaging reveals a highly dynamic pulsatile organ [5] 1 . Indeed, as early as 1880, Mosso, studying adult patients with skull abnormalities, developed a technique known as plethysmography, with which he directly observed the brief cerebral-volume pulsations associated with cardiac and respiratory rhythms [6,7]. Furthermore, Mosso recorded sudden increases in slower-volume pulsations when his subjects engaged in mental activities, thereby paving the way for modern-day functional brain imaging-techniques (i.e., positron emission tomography and functional magnetic resonance imaging) that measure localized increases in blood flow known as functional hyperemia [8][9][10][11]. ...

Angelo Mosso's Circulation of Blood in the Human Brain
  • Citing Book
  • September 2014

... • Hybrid approaches. [293] integrate predicted connectivity with simplified dynamics for some components and detailed biophysics for others. For example, corticospinal and corticostriatal cell model morphologies had 706 and 325 compartments, but excitatory and inhibitory neurons had 6 and 3 compartments (soma, axon, dendrite). ...

Multiscale Model of Primary Motor Cortex Circuits Predicts in vivo Cell Type-Specific, Behavioral State-Dependent Dynamics
  • Citing Article
  • January 2022

SSRN Electronic Journal

... The OB has a very high ratio of INs to projection neurons (100:1) compared to any other brain structure (Shepherd et al., 2021). OB INs are small, often anaxonic, and modulate the activity of the OB projection neurons (Whitman and Greer, 2007a). ...

An Evolutionary Microcircuit Approach to the Neural Basis of High Dimensional Sensory Processing in Olfaction

Frontiers in Cellular Neuroscience

... However, an interaction between genotype and object distance during pull-back sessions was detected in total correct choices (F(4, 60) = 2.6, p = 0.04, Fig. 3d). Consistent with impaired sensitivity (e.g., reduced precision) for detecting object location, the pull-back curve Discrimination index wt (8) +/- (6) wt (10) +/- (10) wt (7) +/- a Novel object recognition texture (NOR-T) task structure in which mice are tasked to discriminate between visually identical objects having either 8 or 9 verticals ribs/cm. The light level in the arena is 85 lux. ...

Circuit organization of the excitatory sensorimotor loop through hand/forelimb S1 and M1

eLife

... The result appears to be a beautiful, orderly activation pattern for each odorant, comparable to a fingerprint for each odor molecule in the brain (Xu, Greer, & Shepherd, 2000). If olfaction operated similarly to the visual system, this bulbar topography would be expected to be maintained throughout further cortical processing (although, strictly speaking, not the entire striatum but its layer IV revealed such strong signal correspondence, the underlying topographic paradigm has been central Note. the bulb's extensive interneuron circuitry, which generates temporally highly differentiated signals via complicated inhibitory and excitatory networks, is omitted (Shepherd, Hines, Migliore, Chen, & Greer, 2020). to olfaction; Shepherd, 2012). ...

Predicting brain organization with a computational model: Fifty year perspective on lateral inhibition and oscillatory gating by dendrodendritic synapses
  • Citing Article
  • July 2020

Journal of Neurophysiology

... The inability to recognize the same object across different concentrations would clearly be disadvantageous, particularly for salient odors such as food. Natural interaction with food stuffs would enable an animal to associate a range of concentrations with the same object; consuming the food provides much weaker activation of the olfactory epithelium by retronasal olfaction (48). We therefore investigated whether natural 'passive' association of the odor with food was able to endow perceptual constancy for ethyl tiglate across the full range of concentrations we used. ...

Orthonasal versus retronasal glomerular activity in rat olfactory bulb by fMRI

NeuroImage

... Over three-quarters of all neurons in the mammalian cortex are pyramidal cells (see Fig A in S1 Text), which have dendrites spanning the thickness of the cortex (several mm) and AIS lengths on the order of tens of mm [8][9][10][11]. The AIS requires a high density of voltage-gated sodium channels (Na V s) to prime and initiate action potentials (APs) [12][13][14]. In pyramidal cells, the AIS features two Na V subtypes, with an interesting spatial distribution: Na V 1.2 channels cluster near the soma (i.e. at the proximal AIS) while Na V 1.6 cluster toward the distal AIS [15][16][17]. ...

Neuron Names: A Gene- and Property-Based Name Format, With Special Reference to Cortical Neurons

Frontiers in Neuroanatomy

... [83] and NetPyNE (www.netpyne.org) [84], a Python package to facilitate the development of biological neuronal networks in the NEURON simulator. Upon publication, we will make the full source code available on github and ModelDB. ...

NetPyNE: a tool for data-driven multiscale modeling of brain circuits