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Regulation of gamma‐frequency oscillation by feedforward inhibition: A computational modeling study
Abstract
Throughout the brain, reciprocally connected excitatory and inhibitory neurons interact to produce gamma‐frequency oscillations. The emergent gamma rhythm synchronizes local neural activity and helps to select which cells should fire in each cycle. We previously found that such excitation‐inhibition microcircuits, however, have a potentially significant caveat: the frequency of the gamma oscillation and the level of selection (i.e., the percentage of cells that are allowed to fire) vary with the magnitude of the input signal. In networks with varying levels of brain activity, such a feature may produce undesirable instability on the time and spatial structure of the neural signal with a potential for adversely impacting important neural processing mechanisms. Here we propose that feedforward inhibition solves the latter instability problem of the excitation‐inhibition microcircuit. Using computer simulations, we show that the feedforward inhibitory signal reduces the dependence of both the frequency of population oscillation and the level of selection on the magnitude of the input excitation. Such a mechanism can produce stable gamma oscillations with its frequency determined only by the properties of the feedforward network, as observed in the hippocampus. As feedforward and feedback inhibition motifs commonly appear together in the brain, we hypothesize that their interaction underlies a robust implementation of general computational principles of neural processing involved in several cognitive tasks, including the formation of cell assemblies and the routing of information between brain areas.
... These classes of interneurons have been implicated in specific computational functions in neural circuits. One important function proposed for feedforward inhibition is "background subtraction, " whereby inhibition grows in proportion to and cancels the average level of input to the circuit, enabling only large fluctuations in inputs above the average level to drive circuit output (Grienberger et al., 2017;Rennó-Costa et al., 2019). Here we ask whether this background subtraction mechanism can support a constant level of output given a wide range in the total number of active inputs. ...
... Feedback inhibition has been proposed to regulate the maximum number of output neurons that respond to the same pattern of input (de Almeida et al., 2009;Stefanelli et al., 2016;Rennó-Costa et al., 2019). This circuit function has been termed "winner-take-all, " or "lateral" inhibition, whereby the neurons receiving the highest level of excitatory input recruit feedback inhibition that suppresses neighboring neurons which are receiving less excitation. ...
... This circuit function has been termed "winner-take-all, " or "lateral" inhibition, whereby the neurons receiving the highest level of excitatory input recruit feedback inhibition that suppresses neighboring neurons which are receiving less excitation. However, previous modeling work has shown that feedback inhibition alone is not able to prevent the number of active output neurons from increasing as the total amount of afferent input grows (Rennó-Costa et al., 2019). Furthermore, it is not clear if the extremely low fraction of active output neurons in circuits with ultrasparse representations like the hippocampal dentate gyrus is sufficient to activate the level of feedback inhibition necessary to support "winner-takeall" competition. ...
It is generally appreciated that storing memories of specific events in the mammalian brain, and associating features of the environment with behavioral outcomes requires fine-tuning of the strengths of connections between neurons through synaptic plasticity. It is less understood whether the organization of neuronal circuits comprised of multiple distinct neuronal cell types provides an architectural prior that facilitates learning and memory by generating unique patterns of neuronal activity in response to different stimuli in the environment, even before plasticity and learning occur. Here we simulated a neuronal network responding to sensory stimuli, and systematically determined the effects of specific neuronal cell types and connections on three key metrics of neuronal sensory representations: sparsity, selectivity, and discriminability. We found that when the total amount of input varied considerably across stimuli, standard feedforward and feedback inhibitory circuit motifs failed to discriminate all stimuli without sacrificing sparsity or selectivity. Interestingly, networks that included dedicated excitatory feedback interneurons based on the mossy cells of the hippocampal dentate gyrus exhibited improved pattern separation, a result that depended on the indirect recruitment of feedback inhibition. These results elucidate the roles of cellular diversity and neural circuit architecture on generating neuronal representations with properties advantageous for memory storage and recall.
... window (e.g. the ~15 ms duration of a single gamma cycle). This highlights the important roles that inhibitory neurons play in regulating sparsity (how many cells are co-active), selectivity (which cells are active), and rhythmicity (when cells fire) in recurrent networks (Almeida et al., 2009b;Rennó-Costa et al., 2019;Stark et al., 2014;Stefanelli et al., 2016). However, while oscillatory feedback inhibition 95 provides a network mechanism for parsing neuronal sequences into discrete elements, additional mechanisms are still required to ensure that distinct subsets of excitatory neurons are activated in a particular order across successive cycles of a rhythm (Lisman et al., 2005;Ramirez-Villegas et al., 2018). ...
... Specifically, inhibitory feedback connections have been shown to regulate the number of simultaneously active neurons (sparsity) (Stefanelli et al., 2016), and to contribute to the generation of theta and gamma network oscillations (Bezaire et al., 2016;Geisler et 200 al., 2005;Rennó-Costa et al., 2019;Stark et al., 2014;Wang, 2010). Plastic excitatory connections between excitatory neurons have long been implicated in stimulus selectivity and the storage and recall of memories (Almeida et al., 2007;Hopfield, 1982;Lisman and Jensen, 2013). ...
During spatial exploration, neural circuits in the hippocampus store memories of sequences of sensory events encountered in the environment. When sensory information is absent during "offline" resting periods, brief neuronal population bursts can "replay" sequences of activity that resemble bouts of sensory experience. These sequences can occur in either forward or reverse order, and can even include spatial trajectories that have not been experienced, but are consistent with the topology of the environment. The neural circuit mechanisms underlying this variable and flexible sequence generation are unknown. Here we demonstrate in a recurrent spiking network model of hippocampal area CA3 that experimental constraints on network dynamics such as spike rate adaptation, population sparsity, stimulus selectivity, and rhythmicity enable additional emergent properties, including variable offline memory replay. In an online stimulus-driven state, we observed the emergence of neuronal sequences that swept from representations of past to future stimuli on the timescale of the theta rhythm. In an offline state driven only by noise, the network generated both forward and reverse neuronal sequences, and recapitulated the experimental observation that offline memory replay events tend to include salient locations like the site of a reward. These results demonstrate that biological constraints on the dynamics of recurrent neural circuits are sufficient to enable memories of sensory events stored in the strengths of synaptic connections to be flexibly read out during rest and sleep, which is thought to be important for memory consolidation and planning of future behavior.
... Phase shift relative to the local field potential (LFP) provides a mechanism for temporal/phase coding to accompany and complement rate coding in the central nervous system (CNS) (11)(12)(13)(78)(79)(80). Responses to optogenetic stimulation depend on the phase of stimulus activation (81)(82)(83); phase reset is an important aspect of phase determination and network behavior that will be affected by nonstationarity at the cellular scale (84)(85)(86)(87). CNS phase coding has been most thoroughly studied in the context of "phase precession" in hippocampal place cells but has also been seen in entorhinal grid cells (53), as well as in successive theta waves in medial prefrontal cortex (88), and ventral striatum (89). ...
Rhythmic activity is ubiquitous in neural systems, with theta-resonant pyramidal neurons integratingrhythmic inputs in many cortical structures. Impedance analysis has been widely used to examinefrequency-dependent responses of neuronal membranes to rhythmic inputs, but it assumes that the neuronalmembrane is a linear system, requiring the use of small signals to stay in a near-linear regime. However,postsynaptic potentials are often large and trigger nonlinear mechanisms (voltage-gated ion channels). Thegoals of this work were to 1. develop an analysis method to evaluate membrane responsesin this nonlinear domain and 2. explore phase relationships between rhythmic stimuli and subthreshold and spiking membrane potential (Vmemb) responses in models of theta-resonant pyramidal neurons. Responses in these output regimes were asymmetrical, with different phase shifts during hyperpolarizing and depolarizing half-cycles.Suprathreshold theta-rhythmic stimuli produced nonstationary Vmemb responses .Sinusoidal inputs produced phase retreat: action potentials occurred progressively later in cycles of theinput stimulus, resulting from adaptation. Sinusoidal current with increasing amplitude overcycles produced phase advance: action potentials occurred progressively earlier. Phase retreat,phase advance, and subthreshold phase shifts were modulated by multiple ion channel conductances. Our results suggest differential responses of cortical neurons depending on the frequency of oscillatory input, which will play a role inneuronal responses to shifts in network state. We hypothesize that intrinsic cellular properties complementnetwork properties and contribute to in vivo phase-shift phenomena such as phase precession, seen in placeand grid cells, and phase roll, also observed in hippocampal CA1 neurons.
... The computational model was formed of a network composed of a memory module and a decision-making module, built with associated hippocampal and prefrontal properties. The memory module is modeled as a usual hippocampal layer with a convergent projection from input to memory space, local competition at a memory layer, and stable sparsity (Rennó-Costa et al., 2019. The decision-making module follows standard cortical dynamics (Marcos et al., 2013). ...
The therapeutic use of classical psychedelic substances such as d-lysergic acid diethylamide (LSD) surged in recent years. Studies in rodents suggest that these effects are produced by increased neural plasticity, including stimulation of the mTOR pathway, a key regulator of metabolism, plasticity, and aging. Could psychedelic-induced neural plasticity be harnessed to enhance cognition? Here we show that LSD treatment enhanced performance in a novel object recognition task in rats, and in a visuo-spatial memory task in humans. A proteomic analysis of human brain organoids showed that LSD affected metabolic pathways associated with neural plasticity, including mTOR. To gain insight into the relation of neural plasticity, aging and LSD-induced cognitive gains, we emulated the experiments in rats and humans with a neural network model of a cortico-hippocampal circuit. Using the baseline strength of plasticity as a proxy for age and assuming an increase in plasticity strength related to LSD dose, the simulations provided a good fit for the experimental data. Altogether, the results suggest that LSD has nootropic effects.
The hippocampus is the brain’s center for episodic memories. Its subregions, the dentate gyrus and CA1-3, are differentially involved in memory encoding and recall. Hippocampal principal cells represent episodic features like movement, space, and context, but less is known about GABAergic interneurons. Here, we performed two-photon calcium imaging of parvalbumin- and somatostatin-expressing interneurons in the dentate gyrus and CA1-3 of male mice exploring virtual environments. Parvalbumin-interneurons increased activity with running-speed and reduced it in novel environments. Somatostatin-interneurons in CA1-3 behaved similar to parvalbumin-expressing cells, but their dentate gyrus counterparts increased activity during rest and in novel environments. Congruently, chemogenetic silencing of dentate parvalbumin-interneurons had prominent effects in familiar contexts, while silencing somatostatin-expressing cells increased similarity of granule cell representations between novel and familiar environments. Our data indicate unique roles for parvalbumin- and somatostatin-positive interneurons in the dentate gyrus that are distinct from those in CA1-3 and may support routing of novel information.
Cognitive impairment is a common symptom following mild traumatic brain injury (mTBI or concussion) and can persist for years in some individuals. Hippocampal slice preparations following closed-head, rotational acceleration injury in swine have previously demonstrated reduced axonal function and hippocampal circuitry disruption. However, electrophysiological changes in hippocampal neurons and their subtypes in a large animal mTBI model have not been examined. Using in vivo electrophysiology techniques, we examined laminar oscillatory field potentials and single unit activity in the hippocampal network 7 days post-injury in anesthetized minipigs. Concussion altered the electrophysiological properties of pyramidal cells and interneurons differently in area CA1. While the firing rate, spike width and amplitude of CA1 interneurons were significantly decreased post-mTBI, these parameters were unchanged in CA1 pyramidal neurons. In addition, CA1 pyramidal neurons in TBI animals were less entrained to hippocampal gamma (40–80 Hz) oscillations. Stimulation of the Schaffer collaterals also revealed hyperexcitability across the CA1 lamina post-mTBI. Computational simulations suggest that reported changes in interneuronal physiology may be due to alterations in voltage-gated sodium channels. These data demonstrate that a single concussion can lead to significant neuronal and circuit level changes in the hippocampus, which may contribute to cognitive dysfunction following mTBI.
During spatial exploration, neural circuits in the hippocampus store memories of sequences of sensory events encountered in the environment. When sensory information is absent during ‘offline’ resting periods, brief neuronal population bursts can ‘replay’ sequences of activity that resemble bouts of sensory experience. These sequences can occur in either forward or reverse order, and can even include spatial trajectories that have not been experienced, but are consistent with the topology of the environment. The neural circuit mechanisms underlying this variable and flexible sequence generation are unknown. Here we demonstrate in a recurrent spiking network model of hippocampal area CA3 that experimental constraints on network dynamics such as population sparsity, stimulus selectivity, rhythmicity and spike rate adaptation, as well as associative synaptic connectivity, enable additional emergent properties, including variable offline memory replay. In an online stimulus‐driven state, we observed the emergence of neuronal sequences that swept from representations of past to future stimuli on the timescale of the theta rhythm. In an offline state driven only by noise, the network generated both forward and reverse neuronal sequences, and recapitulated the experimental observation that offline memory replay events tend to include salient locations like the site of a reward. These results demonstrate that biological constraints on the dynamics of recurrent neural circuits are sufficient to enable memories of sensory events stored in the strengths of synaptic connections to be flexibly read out during rest and sleep, which is thought to be important for memory consolidation and planning of future behaviour. image
Key points
A recurrent spiking network model of hippocampal area CA3 was optimized to recapitulate experimentally observed network dynamics during simulated spatial exploration.
During simulated offline rest, the network exhibited the emergent property of generating flexible forward, reverse and mixed direction memory replay events.
Network perturbations and analysis of model diversity and degeneracy identified associative synaptic connectivity and key features of network dynamics as important for offline sequence generation.
Network simulations demonstrate that population over‐representation of salient positions like the site of reward results in biased memory replay.
Cognitive impairment is a common symptom following mild traumatic brain injury (mTBI or concussion) and can persist for years in some individuals. While the underlying mechanisms driving these impairments remain unknown, structural changes in the hippocampus post-mTBI have been reported in human patients, as well as electrophysiological changes in rodents following impact models of TBI. In addition, slice preparations from a closed-head, rotational acceleration injury (RAI) in swine showed reduced axonal function and hippocampal circuitry disruption. However, electrophysiological changes in neurons and their subtypes have not been examined. Using in vivo electrophysiology techniques, we examined the laminar oscillatory field potentials and single unit activity in the intact hippocampal network at 7 days post-RAI in anesthetized minipigs. Concussion altered the electrophysiological properties of pyramidal cells and interneurons differently in area CA1. Specifically, while firing rate and burst occurrence of CA1 interneurons were significantly decreased post-mTBI, these parameters were unchanged in putative CA1 pyramidal cells. However, pyramidal CA1 cells in TBI animals were significantly less entrained to hippocampal gamma (40 - 80 Hz) oscillations. In addition, stimulation of the Schaffer collaterals revealed hyperexcitability in the laminar CA1 response post-mTBI. Finally, computational simulations suggest that the reported changes in interneuronal firing rate and action potentials may be due to alterations in voltage-gated sodium channels. These data demonstrate that a single concussion can lead to significant neuronal and circuit level changes in the hippocampus, and that the loss of pyramidal gamma entrainment and changes in interneuronal firing may be important contributors to cognitive dysfunction following mTBI.
Synchronous oscillations in neural populations are considered being controlled by inhibitory neurons. In the granular layer of the cerebellum, two major types of cells are excitatory granular cells (GCs) and inhibitory Golgi cells (GoCs). GC spatiotemporal dynamics, as the output of the granular layer, is highly regulated by GoCs. However, there are various types of inhibition implemented by GoCs. With inputs from mossy fibers, GCs and GoCs are reciprocally connected to exhibit different network motifs of synaptic connections. From the view of GCs, feedforward inhibition is expressed as the direct input from GoCs excited by mossy fibers, whereas feedback inhibition is from GoCs via GCs themselves. In addition, there are abundant gap junctions between GoCs showing another form of inhibition. It remains unclear how these diverse copies of inhibition regulate neural population oscillation changes. Leveraging a computational model of the granular layer network, we addressed this question to examine the emergence and modulation of network oscillation using different types of inhibition. We show that at the network level, feedback inhibition is crucial to generate neural oscillation. When short-term plasticity was equipped on GoC-GC synapses, oscillations were largely diminished. Robust oscillations can only appear with additional gap junctions. Moreover, there was a substantial level of cross-frequency coupling in oscillation dynamics. Such a coupling was adjusted and strengthened by GoCs through feedback inhibition. Taken together, our results suggest that the cooperation of distinct types of GoC inhibition plays an essential role in regulating synchronous oscillations of the GC population. With GCs as the sole output of the granular network, their oscillation dynamics could potentially enhance the computational capability of downstream neurons.
Gamma-band oscillations are implicated in modulation of attention, integration of sensory information and flexible communication among anatomically connected brain areas. How networks become entrained is incompletely understood. Specifically, it is unclear how the spectral and temporal characteristics of network oscillations can be altered on rapid timescales needed for efficient communication. We use closed-loop optogenetic modulation of principal cell excitability in mouse hippocampal slices to interrogate the dynamical properties of hippocampal oscillations. Gamma frequency and amplitude can be modulated bi-directionally, and dissociated, by phase-advancing or delaying optogenetic feedback to pyramidal cells. Closed-loop modulation alters the synchrony rather than average frequency of action potentials, in principle avoiding disruption of population rate-coding of information. Modulation of phasic excitatory currents in principal neurons is sufficient to manipulate oscillations, suggesting that feed-forward excitation of pyramidal cells has an important role in determining oscillatory dynamics and the ability of networks to couple with one another.
Theta oscillations reflect rhythmic inputs that continuously converge to the hippocampus during exploratory and memory-guided behavior. The theta-nested operations that organize hippocampal spiking could either occur regularly from one cycle to the next or be tuned on a cycle-by-cycle basis. To resolve this, we identified spectral components nested in individual theta cycles recorded from the mouse CA1 hippocampus. Our single-cycle profiling revealed theta spectral components associated with different firing modulations and distinguishable ensembles of principal cells. Moreover, novel co-firing patterns of principal cells in theta cycles nesting mid-gamma oscillations were the most strongly reactivated in subsequent offline sharp-wave/ripple events. Finally, theta-nested spectral components were differentially altered by behavioral stages of a memory task; the 80-Hz mid-gamma component was strengthened during learning, whereas the 22-Hz beta, 35-Hz slow gamma, and 54-Hz mid-gamma components increased during retrieval. We conclude that cycle-to-cycle variability of theta-nested spectral components allows parsing of theta oscillations into transient operating modes with complementary mnemonic roles. Copyright © 2018 The Author(s). Published by Elsevier Inc. All rights reserved.
Fast spiking interneurons in the CA1 area of the dorsal hippocampus were recorded from and filled with biocytin in anesthetized rats. The full extent of their dendrites and axonal arborizations as well as their calcium binding protein content were examined. Based on the spatial extent of axon collaterals, local circuit cells (basket and O-LM neurons) and long-range cells (bistratified, trilaminar, and backprojection neurons) could be distinguished. Basket cells were immunoreactive for parvalbumin and their axon collaterals were confined to the pyramidal layer. A single basket cell contacted more than 1500 pyramidal neurons and 60 other parvalbumin-positive interneurons. Commissural stimulation directly discharged basket cells, followed by an early and late IPSPs, indicating interneuronal inhibition of basket cells. The dendrites of another local circuit neuron (O-LM) were confined to stratum oriens and it had a small but high-density axonal terminal field in stratum lacunosum-moleculare. The fastest firing cell of all interneurons was a calbindin-immunoreactive bistratified neuron with axonal targets in stratum oriens and radiatum. Two neurons with their cell bodies in the alveus innervated the CA3 region (backprojection cells), in addition to rich axon collaterals in the CA1 region. The trilaminar interneuron had axon collaterals in strata radiatum, oriens and pyramidale with its dendrites confined to stratum oriens. Commissural stimulation evoked an early EPSP-IPSP-late depolarizing potential sequence in this cell. All interneurons formed symmetric synapses with their targets at the electron microscopic level. These findings indicate that interneurons with distinct axonal targets have differential functions in shaping the physiological patterns of the CA1 network.
Hippocampal network operations supporting spatial navigation and declarative memory are traditionally interpreted in a framework where each hippocampal area, such as the dentate gyrus, CA3, and CA1, consists of homogeneous populations of functionally equivalent principal neurons. However, heterogeneity within hippocampal principal cell populations, in particular within pyramidal cells at the main CA1 output node, is increasingly recognized and includes developmental, molecular, anatomical, and functional differences. Here we review recent progress in the delineation of hippocampal principal cell subpopulations by focusing on radially defined subpopulations of CA1 pyramidal cells, and we consider how functional segregation of information streams, in parallel channels with nonuniform properties, could represent a general organizational principle of the hippocampus supporting diverse behaviors.
Pattern separation, the ability to independently represent and store similar experiences, is a crucial facet of episodic memory. Growing evidence suggests that the hippocampus possesses unique circuitry that is computationally capable of resolving mnemonic interference by using pattern separation. In this Review, we discuss recent advances in the understanding of this process and evaluate the caveats and limitations of linking across animal and human studies. We summarize clinical and translational studies using methods that are sensitive to pattern separation impairments, an approach that stems from the fact that the hippocampus is a major site of disruption in many brain disorders. We critically evaluate the assumptions that guide fundamental and translational studies in this area. Finally, we suggest guidelines for future research and offer ways to overcome potential interpretational challenges to increase the utility of pattern separation as a construct that can further understanding of both memory processes and brain disease.
The entorhinal-hippocampal system is an important circuit in the brain, essential for certain cognitive tasks such as memory and navigation. Different gamma oscillations occur in this circuit, with the medial entorhinal cortex (mEC), CA3, and CA1 all generating gamma oscillations with different properties. These three gamma oscillations converge within CA1, where much work has gone into trying to isolate them from each other. Here we compared the gamma generators in the mEC, CA3, and CA1 using optogenetically-induced theta-gamma oscillations. Expressing channelrhodopsin (ChR2) in principal neurons in each of the three regions allowed for induction of gamma oscillations via sinusoidal blue light stimulation at theta frequency. Recording the oscillations in CA1 in vivo, we found that CA3 stimulation induced slower gamma oscillations than CA1 stimulation, matching in vivo reports of spontaneous CA3 and CA1 gamma oscillations. In brain slices ex vivo, optogenetic stimulation of CA3 induced slower gamma oscillations than stimulation of either mEC and CA1, whose gamma oscillations were of similar frequency. All three gamma oscillations had a current sink-source pair between the perisomatic and dendritic layers of the same region. Taking advantage of this powerful model to analyse gamma frequency mechanisms in slice, we showed using pharmacology that all three gamma oscillations were dependent on the same types of synaptic receptor, being abolished by blockade of either GABAA receptors or AMPA/kainate receptors, and insensitive to blockade of NMDA receptors. These results indicate that a fast excitatory-inhibitory feedback loop underlies the generation of gamma oscillations in all three regions.
During active somatosensation, neural signals expected from movement of the sensors are suppressed in the cortex, whereas information related to touch is enhanced. This tactile suppression underlies low-noise encoding of relevant tactile features and the brain’s ability to make fine tactile discriminations. Layer (L) 4 excitatory neurons in the barrel cortex, the major target of the somatosensory thalamus (VPM), respond to touch, but have low spike rates and low sensitivity to the movement of whiskers. Most neurons in VPM respond to touch and also show an increase in spike rate with whisker movement. Therefore, signals related to self-movement are suppressed in L4. Fast-spiking (FS) interneurons in L4 show similar dynamics to VPM neurons. Stimulation of halorhodopsin in FS interneurons causes a reduction in FS neuron activity and an increase in L4 excitatory neuron activity. This decrease of activity of L4 FS neurons contradicts the "paradoxical effect" predicted in networks stabilized by inhibition and in strongly-coupled networks. To explain these observations, we constructed a model of the L4 circuit, with connectivity constrained by in vitro measurements. The model explores the various synaptic conductance strengths for which L4 FS neurons actively suppress baseline and movement-related activity in layer 4 excitatory neurons. Feedforward inhibition, in concert with recurrent intracortical circuitry, produces tactile suppression. Synaptic delays in feedforward inhibition allow transmission of temporally brief volleys of activity associated with touch. Our model provides a mechanistic explanation of a behavior-related computation implemented by the thalamocortical circuit.
Dopamine release and phase-amplitude cross-frequency coupling (CFC) have independently been implicated in prefrontal cortex (PFC) functioning. To causally investigate whether dopamine release affects phase-amplitude comodulation between different frequencies in local field potentials (LFP) recorded from the medial PFC (mPFC) of behaving rats, we used RuBiDopa, a light-sensitive caged compound that releases the neurotransmitter dopamine when irradiated with visible light. LFP power did not change in any frequency band after the application of light-uncaged dopamine, but significantly strengthened phase-amplitude comodulation between delta and gamma oscillations. Saline did not exert significant changes, while injections of dopamine and RuBiDopa produced a slow increase in comodulation for several minutes after the injection. The results show that dopamine release in the medial PFC shifts phase-amplitude comodulation from theta-gamma to delta-gamma. Although being preliminary results due to the limitation of the low number of animals present in this study, our findings suggest that dopamine-mediated modification of the frequencies involved in comodulation could be a mechanism by which this neurotransmitter regulates functioning in mPFC.
In vivo, theta (4–7 Hz) and gamma (30–80 Hz) neuronal network oscillations are known to coexist and display phase-amplitude coupling (PAC). However, in vitro, these oscillations have for many years been studied in isolation. Using an improved brain slice preparation technique we have, using co-application of carbachol (10 μM) and kainic acid (150 nM), elicited simultaneous theta (6.6 ± 0.1 Hz) and gamma (36.6 ± 0.4 Hz) oscillations in rodent primary motor cortex (M1). Each oscillation showed greatest power in layer V. Using a variety of time series analyses we detected significant cross-frequency coupling 74% of slice preparations.
Changes in gamma oscillations (20–50 Hz) have been observed in several neurological disorders. However, the relationship between gamma oscillations and cellular pathologies is unclear. Here we show reduced, behaviourally driven gamma oscillations before the onset of plaque formation or cognitive decline in a mouse model of Alzheimer’s disease. Optogenetically driving fast-spiking parvalbumin-positive (FS-PV)-interneurons at gamma (40 Hz), but not other frequencies, reduces levels of amyloid-β (Aβ)1–40 and Aβ 1–42 isoforms. Gene expression profiling revealed induction of genes associated with morphological transformation of microglia, and histological analysis confirmed increased microglia co-localization with Aβ. Subsequently, we designed a non-invasive 40 Hz light-flickering regime that reduced Aβ1–40 and Aβ1–42 levels in the visual cortex of pre-depositing mice and mitigated plaque load in aged, depositing mice. Our findings uncover a previously unappreciated function of gamma rhythms in recruiting both neuronal and glial responses to attenuate Alzheimer’s-disease-associated pathology.
Several theories have been advanced to explain how cross-frequency coupling, the interaction of neuronal oscillations at different frequencies, could enable item multiplexing in neural systems. The communication-through-coherence theory proposes that phase-matching of gamma oscillations between areas enables selective processing of a single item at a time, and a later refinement of the theory includes a theta-frequency oscillation that provides a periodic reset of the system. Alternatively, the theta-gamma neural code theory proposes that a sequence of items is processed, one per gamma cycle, and that this sequence is repeated or updated across theta cycles. In short, both theories serve to segregate representations via the temporal domain, but differ on the number of objects concurrently represented. In this study, we set out to test whether each of these theories is actually physiologically plausible, by implementing them within a single model inspired by physiological data. Using a spiking network model of visual processing, we show that each of these theories is physiologically plausible and computationally useful. Both theories were implemented within a single network architecture, with two areas connected in a feedforward manner, and gamma oscillations generated by feedback inhibition within areas. Simply increasing the amplitude of global inhibition in the lower area, equivalent to an increase in the spatial scope of the gamma oscillation, yielded a switch from one mode to the other. Thus, these different processing modes may co-exist in the brain, enabling dynamic switching between exploratory and selective modes of attention.
Our day-to-day experiences are often similar to one another, occurring in the same place at the same time of day, with common people and objects, and with a shared purpose. Humans have an episodic memory to represent unique, personal events that are rich in detail [ 1 ]. For this to occur, at least two basic neural mechanisms are required: one to orthogonalize or “separate” overlapping input patterns at encoding and another to reinstate or “complete” memories from partial cues at retrieval [ 2–6 ]. To what extent do these purported “pattern separation” and “pattern completion” mechanisms rely on distinct subfields of the hippocampus [ 6 ]? Computational models [ 4–6 ] and lesion and genetic studies in rodents [ 7–12 ] largely point to the dentate gyrus as responsible for pattern separation and the CA3 and CA1 subfields for pattern completion (but see [ 13–16 ]). In high-resolution fMRI studies of humans, behavioral discrimination and completion tasks designed to approximate pattern separation and pattern completion, respectively, elicit the predicted pattern of activity in the dentate gyrus and CA3/CA1 [ 17–21 ]. Likewise, impaired behavioral discrimination has been demonstrated in individuals with hippocampal lesions [ 22, 23 ], but the lesions most likely encompass other subfields. Examination of these processes in individuals with selective lesions to hippocampal subfields is needed to infer causation [ 19 ]. Here, we report the rare case of BL, a 54-year-old man with bilateral ischemic lesions to the hippocampus [ 24 ] primarily affecting the dentate gyrus. Studying BL provides the unique opportunity to directly evaluate theories of hippocampal function that assign the dentate gyrus a specific role in discriminating old from new memories.
Experimental methods in neuroscience, such as calcium-imaging and recordings with multi-electrode arrays, are advancing at a rapid pace. They produce insight into the simultaneous activity of large numbers of neurons, and into plasticity processes in the brains of awake and behaving animals. These new data constrain models for neural computation and network plasticity that underlie perception, cognition, behavior, and learning. I will discuss in this short article four such constraints: inherent recurrent network activity and heterogeneous dynamic properties of neurons and synapses, stereotypical spatio-temporal activity patterns in networks of neurons, high trial-to-trial variability of network responses, and functional stability in spite of permanently ongoing changes in the network. I am proposing that these constraints provide hints to underlying principles of brain computation and learning.
Unlabelled:
To investigate excitatory and inhibitory GABA actions in cortical neuronal networks, we present a novel optogenetic approach using a mouse knock-in line with conditional expression of channelrhodopsin-2 (ChR2) in GABAergic interneurons. During whole-cell recordings from hippocampal and neocortical slices from postnatal day (P) 2-P15 mice, photostimulation caused depolarization and excitation of interneurons and evoked barrages of postsynaptic GABAergic currents. Excitatory/inhibitory GABA actions on pyramidal cells were assessed by monitoring the alteration in the frequency of EPSCs during photostimulation of interneurons. We found that in slices from P2-P8 mice, photostimulation evoked an increase in EPSC frequency, whereas in P9-P15 mice the response switched to a reduction in EPSC frequency, indicating a developmental excitatory-to-inhibitory switch in GABA actions on glutamatergic neurons. Using a similar approach in urethane-anesthetized animals in vivo, we found that photostimulation of interneurons reduces EPSC frequency at ages P3-P9. Thus, expression of ChR2 in GABAergic interneurons of mice enables selective photostimulation of interneurons during the early postnatal period, and these mice display a developmental excitatory-to-inhibitory switch in GABA action in cortical slices in vitro, but so far show mainly inhibitory GABA actions on spontaneous EPSCs in the immature hippocampus and neocortex in vivo
Significance statement:
We report a novel optogenetic approach for investigating excitatory and inhibitory GABA actions in mice with conditional expression of channelrhodopsin-2 in GABAergic interneurons. This approach shows a developmental excitatory-to-inhibitory switch in the actions of GABA on glutamatergic neurons in neocortical and hippocampal slices from neonatal mouse pups in vitro, but also reveals inhibitory GABA actions in the neonatal mouse neocortex and hippocampus in vivo.
Inhibitory interneurons play critical roles in shaping the firing patterns of principal neurons in many brain systems. Despite difference in the anatomy or functions of neuronal circuits containing inhibition, two basic motifs repeatedly emerge: feed-forward and feedback. In the locust, it was proposed that a subset of lateral horn interneurons (LHNs), provide feed-forward inhibition onto Kenyon cells (KCs) to maintain their sparse firing-a property critical for olfactory learning and memory. But recently it was established that a single inhibitory cell, the giant GABAergic neuron (GGN), is the main and perhaps sole source of inhibition in the mushroom body, and that inhibition from this cell is mediated by a feedback (FB) loop including KCs and the GGN. To clarify basic differences in the effects of feedback vs. feed-forward inhibition in circuit dynamics we here use a model of the locust olfactory system. We found both inhibitory motifs were able to maintain sparse KCs responses and provide optimal odor discrimination. However, we further found that only FB inhibition could create a phase response consistent with data recorded in vivo. These findings describe general rules for feed-forward versus feedback inhibition and suggest GGN is potentially capable of providing the primary source of inhibition to the KCs. A better understanding of how inhibitory motifs impact post-synaptic neuronal activity could be used to reveal unknown inhibitory structures within biological networks.
The cortex encodes a broad range of inputs. This breadth of operation requires sensitivity to weak inputs yet non-saturating responses to strong inputs. If individual pyramidal neurons were to have a narrow dynamic range, as previously claimed, then staggered all-or-none recruitment of those neurons would be necessary for the population to achieve a broad dynamic range. Contrary to this explanation, we show here through dynamic clamp experiments in vitro and computer simulations that pyramidal neurons have a broad dynamic range under the noisy conditions that exist in the intact brain due to background synaptic input. Feedforward inhibition capitalizes on those noise effects to control neuronal gain and thereby regulates the population dynamic range. Importantly, noise allows neurons to be recruited gradually and occludes the staggered recruitment previously attributed to heterogeneous excitation. Feedforward inhibition protects spike timing against the disruptive effects of noise, meaning noise can enable the gain control required for rate coding without compromising the precise spike timing required for temporal coding.
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Sleep is critical for hippocampus-dependent memory consolidation. However, the underlying mechanisms of synaptic plasticity are poorly understood. The central controversy is on whether long-term potentiation (LTP) takes a role during sleep and which would be its specific effect on memory. To address this question, we used immunohistochemistry to measure phosphorylation of Ca2+/calmodulin-dependent protein kinase II (pCaMKIIα) in the rat hippocampus immediately after specific sleep-wake states were interrupted. Control animals not exposed to novel objects during waking (WK) showed stable pCaMKIIα levels across the sleep-wake cycle, but animals exposed to novel objects showed a decrease during subsequent slow-wave sleep (SWS) followed by a rebound during rapid-eye-movement sleep (REM). The levels of pCaMKIIα during REM were proportional to cortical spindles near SWS/REM transitions. Based on these results, we modeled sleep-dependent LTP on a network of fully connected excitatory neurons fed with spikes recorded from the rat hippocampus across WK, SWS and REM. Sleep without LTP orderly rescaled synaptic weights to a narrow range of intermediate values. In contrast, LTP triggered near the SWS/REM transition led to marked swaps in synaptic weight ranking. To better understand the interaction between rescaling and restructuring during sleep, we implemented synaptic homeostasis and embossing in a detailed hippocampal-cortical model with both excitatory and inhibitory neurons. Synaptic homeostasis was implemented by weakening potentiation and strengthening depression, while synaptic embossing was simulated by evoking LTP on selected synapses. We observed that synaptic homeostasis facilitates controlled synaptic restructuring. The results imply a mechanism for a cognitive synergy between SWS and REM, and suggest that LTP at the SWS/REM transition critically influences the effect of sleep: Its lack determines synaptic homeostasis, its presence causes synaptic restructuring.
Striatal medium spiny neurons (MSNs) receive lateral inhibitory projections from other MSNs and feedforward inhibitory projections from fast-spiking, parvalbumin-containing striatal interneurons (FSIs). The functional roles of these connections are unknown, and difficult to study in an experimental preparation. We therefore investigated the functionality of both lateral (MSN-MSN) and feedforward (FSI-MSN) inhibition using a large-scale computational model of the striatal network. The model consists of 2744 MSNs comprised of 189 compartments each and 121 FSIs comprised of 148 compartments each, with dendrites explicitly represented and almost all known ionic currents included and strictly constrained by biological data as appropriate. Our analysis of the model indicates that both lateral inhibition and feedforward inhibition function at the population level to limit non-ensemble MSN spiking while preserving ensemble MSN spiking. Specifically, lateral inhibition enables large ensembles of MSNs firing synchronously to strongly suppress non-ensemble MSNs over a short time-scale (10-30 ms). Feedforward inhibition enables FSIs to strongly inhibit weakly activated, non-ensemble MSNs while moderately inhibiting activated ensemble MSNs. Importantly, FSIs appear to more effectively inhibit MSNs when FSIs fire asynchronously. Both types of inhibition would increase the signal-to-noise ratio of responding MSN ensembles and contribute to the formation and dissolution of MSN ensembles in the striatal network.
Grid cells in the medial entorhinal cortex encode space with firing fields that are arranged on the nodes of spatial hexagonal lattices. Potential candidates to read out the space information of this grid code and to combine it with other sensory cues are hippocampal place cells. In this paper, we investigate a population of grid cells providing feed-forward input to place cells. The capacity of the underlying synaptic transformation is determined by both spatial acuity and the number of different spatial environments that can be represented. The codes for different environments arise from phase shifts of the periodical entorhinal cortex patterns that induce a global remapping of hippocampal place fields, i.e., a new random assignment of place fields for each environment. If only a single environment is encoded, the grid code can be read out at high acuity with only few place cells. A surplus in place cells can be used to store a space code for more environments via remapping. The number of stored environments can be increased even more efficiently by stronger recurrent inhibition and by partitioning the place cell population such that learning affects only a small fraction of them in each environment. We find that the spatial decoding acuity is much more resilient to multiple remappings than the sparseness of the place code. Since the hippocampal place code is sparse, we thus conclude that the projection from grid cells to the place cells is not using its full capacity to transfer space information. Both populations may encode different aspects of space.
The brain adapts to dynamic environments by adjusting the attentional gain or precision afforded to salient and predictable sensory input. Previous research suggests that this involves the regulation of cortical excitability (reflected in prestimulus alpha oscillations) before stimulus onset that modulates subsequent stimulus processing (reflected in stimulus-bound gamma oscillations). We present two spatial attention experiments in humans, where we first replicate the classic finding of prestimulus attentional alpha modulation and poststimulus gamma modulation. In the second experiment, the task-relevant target was a stimulus change that occurred after stimulus onset. This enabled us to show that attentional alpha modulation reflects the predictability (precision) of an upcoming sensory target, rather than an attenuation of alpha activity induced by neuronal excitation related to stimulus onset. In particular, we show that the strength of attentional alpha modulations increases with the predictability of the anticipated sensory target, regardless of current afferent drive. By contrast, we show that the poststimulus attentional gamma enhancement is stimulus-bound and decreases when the subsequent target becomes more predictable. Hence, this pattern suggests that the strength of gamma oscillations is not merely a function of cortical excitability, but also depends on the relative mismatch of predictions and sensory evidence. Together, these findings support recent theoretical proposals for distinct roles of alpha/beta and gamma oscillations in hierarchical perceptual inference and predictive coding.
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Significance
The ability to form episodic memories in rapid succession depends on the hippocampus, but how do hippocampal neurons represent such memories? Most neurocomputational models envision a sparse distributed coding scheme in which individual neurons each participate in the coding of a few memories, and each memory is coded by a small fraction of hippocampal neurons. We investigated this issue with epilepsy patients undergoing intracranial monitoring and found evidence consistent with sparse distributed coding in the hippocampus. These findings shed new light on the basic neural mechanisms that underlie the ability to remember events. A detailed characterization of those mechanisms is an essential part of the larger effort to understand memory loss associated with normal aging and dementia.
The notion of attractor networks is the leading hypothesis for how associative memories are stored and recalled. A defining anatomical feature of such networks is excitatory recurrent connections. These "attract" the firing pattern of the network to a stored pattern, even when the external input is incomplete (pattern completion). The CA3 region of the hippocampus has been postulated to be such an attractor network; however, the experimental evidence has been ambiguous, leading to the suggestion that CA3 is not an attractor network. In order to resolve this controversy and to better understand how CA3 functions, we simulated CA3 and its input structures. In our simulation, we could reproduce critical experimental results and establish the criteria for identifying attractor properties. Notably, under conditions in which there is continuous input, the output should be "attracted" to a stored pattern. However, contrary to previous expectations, as a pattern is gradually "morphed" from one stored pattern to another, a sharp transition between output patterns is not expected. The observed firing patterns of CA3 meet these criteria and can be quantitatively accounted for by our model. Notably, as morphing proceeds, the activity pattern in the dentate gyrus changes; in contrast, the activity pattern in the downstream CA3 network is attracted to a stored pattern and thus undergoes little change. We furthermore show that other aspects of the observed firing patterns can be explained by learning that occurs during behavioral testing. The CA3 thus displays both the learning and recall signatures of an attractor network. These observations, taken together with existing anatomical and behavioral evidence, make the strong case that CA3 constructs associative memories based on attractor dynamics.
Mammalian brains exhibit population oscillations, the structures of which vary in time and space according to behavioural state. A proposed function of these oscillations is to control the flow of signals among anatomically connected networks. However, the nature of neural coding that may support selective communication that depends on oscillations has received relatively little attention. Here, we consider the role of multiplexing, whereby multiple information streams share a common neural substrate. We suggest that multiplexing implemented through periodic modulation of firing-rate population codes enables flexible reconfiguration of effective connectivity among brain areas.
The vast diversity of GABAergic interneurons is believed to endow hippocampal microcircuits with the required flexibility for memory encoding and retrieval. However, dissection of the functional roles of defined interneuron types has been hampered by the lack of cell-specific tools. We identified a precise molecular marker for a population of hippocampal GABAergic interneurons known as oriens lacunosum-moleculare (OLM) cells. By combining transgenic mice and optogenetic tools, we found that OLM cells are important for gating the information flow in CA1, facilitating the transmission of intrahippocampal information (from CA3) while reducing the influence of extrahippocampal inputs (from the entorhinal cortex). Furthermore, we found that OLM cells were interconnected by gap junctions, received direct cholinergic inputs from subcortical afferents and accounted for the effect of nicotine on synaptic plasticity of the Schaffer collateral pathway. Our results suggest that acetylcholine acting through OLM cells can control the mnemonic processes executed by the hippocampus.
Gamma rhythms are commonly observed in many brain regions during both waking and sleep states, yet their functions and mechanisms remain a matter of debate. Here we review the cellular and synaptic mechanisms underlying gamma oscillations and outline empirical questions and controversial conceptual issues. Our main points are as follows: First, gamma-band rhythmogenesis is inextricably tied to perisomatic inhibition. Second, gamma oscillations are short-lived and typically emerge from the coordinated interaction of excitation and inhibition, which can be detected as local field potentials. Third, gamma rhythm typically concurs with irregular firing of single neurons, and the network frequency of gamma oscillations varies extensively depending on the underlying mechanism. To document gamma oscillations, efforts should be made to distinguish them from mere increases of gamma-band power and/or increased spiking activity. Fourth, the magnitude of gamma oscillation is modulated by slower rhythms. Such cross-frequency coupling may serve to couple active patches of cortical circuits. Because of their ubiquitous nature and strong correlation with the "operational modes" of local circuits, gamma oscillations continue to provide important clues about neuronal population dynamics in health and disease.
Local inhibition by GABA-releasing neurons is important for the operation of sensory cortices, but the details of these inhibitory circuits remain unclear. We addressed this question in the olfactory system by making targeted recordings from identified classes of inhibitory and glutamatergic neurons in the piriform cortex (PC) of mice. First, we looked for feedforward synaptic inhibition provided by interneurons located in the outermost layer of the PC, layer Ia, which is the unique recipient of afferent fibers from the olfactory bulb. We found two types of feedforward inhibition: a fast-rising, spatially restricted kind that was generated by horizontal cells, and a slow-rising, more diffuse kind generated by neurogliaform cells. Both cell types targeted the distal apical dendrites of layer II principal neurons. Next, we studied feedback synaptic inhibition in isolation by making a tissue cut across layer I to selectively remove feedforward inhibitory connections. We identified a powerful type of feedback inhibition of layer II neurons, mostly generated by soma-targeting fast-spiking multipolar cells in layer III, which in turn were driven by feedforward excitation from layer II semilunar cells. Dynamic clamp simulation of feedback inhibition revealed differential effects of this inhibition on the two main types of layer II principal neurons. Thus, our results articulate the connectivity and functions of two important classes of inhibitory microcircuits in the PC. Feedforward and feedback inhibition generated by these circuits is likely to be required for the operation of this sensory paleocortex during the processing of olfactory information.
We study pacemaker rhythms generated by two nonoscillatory model cells that are coupled by inhibitory synapses. A minimal ionic model that exhibits postinhibitory rebound (PIR) is presented. When the post-synaptic conductance depends instantaneously on presynaptic potential the classical alternating rhythm is obtained. Using phase-plane analysis we identify two underlying mechanisms, “release” and “escape,” for the out-of-phase oscillation. When the postsynaptic conductance is not instantaneous but decays slowly, the two cells can oscillate synchronously with no phase difference. In each case, different stable activity patterns can coexist over a substantial parameter range.
In 1949, Donald Hebb postulated that assemblies of synchronously activated neurons are the elementary units of information processing in the brain. Despite being one of the most influential theories in neuroscience, Hebb's cell assembly hypothesis only started to become testable in the past two decades due to technological advances. However, while the technology for the simultaneous recording of large neuronal populations undergoes fast development, there is still a paucity of analytical methods that can properly detect and track the activity of cell assemblies. Here we describe a principal component-based method that is able to (1) identify all cell assemblies present in the neuronal population investigated, (2) determine the number of neurons involved in ensemble activity, (3) specify the precise identity of the neurons pertaining to each cell assembly, and (4) unravel the time course of the individual activity of multiple assemblies. Application of the method to multielectrode recordings of awake and behaving rats revealed that assemblies detected in the cerebral cortex and hippocampus typically contain overlapping neurons. The results indicate that the PCA method presented here is able to properly detect, track and specify neuronal assemblies, irrespective of overlapping membership.
The mammalian brain exhibits profuse interregional connectivity. How information flow is rapidly and flexibly switched among connected areas remains poorly understood. Task-dependent changes in the power and interregion coherence of network oscillations suggest that such oscillations play a role in signal routing. We show that switching one of several convergent pathways from an asynchronous to an oscillatory state allows accurate selective transmission of population-coded information, which can be extracted even when other convergent pathways fire asynchronously at comparable rates. We further show that the band-pass filtering required to perform this information extraction can be implemented in a simple spiking network model with a single feed-forward interneuron layer. This constitutes a mechanism for flexible signal routing in neural circuits, which exploits sparsely synchronized network oscillations and temporal filtering by feed-forward inhibition.
Video Abstract
Place cells in the hippocampus and grid cells in the medial entorhinal cortex have different codes for space. However, how one code relates to the other is ill understood. Based on the anatomy of the entorhinal-hippocampal circuitry, we constructed a model of place and grid cells organized in a loop to investigate their mutual influence in the establishment of their codes for space. Using computer simulations, we first replicated experiments in rats that measured place and grid cell activity in different environments, and then assessed which features of the model account for different phenomena observed in neurophysiological data, such as pattern completion and pattern separation, global and rate remapping of place cells and realignment of grid cells. We found that (1) the interaction between grid and place cells converges quickly; (2) the spatial code of place cells does not require - but is altered by - grid cell input; (3) plasticity in sensory inputs to place cells is key for pattern completion but not pattern separation; (4) grid realignment can be explained in terms of place cell remapping as opposed to the other way around; (5) the switch between global and rate remapping is self-organized; (6) grid cell input to place cells helps stabilize their code under noisy and/or inconsistent sensory input. We conclude that the hippocampus-entorhinal circuit uses the mutual interaction of place and grid cells to encode the surrounding environment and propose a theory on how such interdependence underlies the formation and use of the cognitive map.SIGNIFICANCE STATEMENTThe mammalian brain implements a positional system with two key pieces: place and grid cells. To gain insight into the dynamics of place and grid cell interaction, we built a computational model with the two cell types organized in a loop. The proposed model accounts for differences in how place and grid cells represent different environments and provides a new interpretation in which place and grid cells mutually interact to form a coupled code for space.
Much evidence indicates that the perirhinal cortex is involved in the familiarity discrimination aspect of recognition memory. It has been previously shown under selective conditions that neural networks performing familiarity discrimination can achieve very high storage capacity, being able to deal with many times more stimuli than associative memory networks can in associative recall. The capacity of associative memories for recall has been shown to be highly dependent on the sparseness of coding. However, previous work on the networks of Bogacz et al, Norman and O'Reilly and Sohal and Hasselmo that model familiarity discrimination in the perirhinal cortex has not investigated the effects of the sparseness of encoding on capacity. This paper explores how sparseness of coding influences the capacity of each of these published models and establishes that sparse coding influences the capacity of the different models in different ways. The capacity of the Bogacz et al model can be made independent of the sparseness of coding. Capacity increases as coding becomes sparser for a simplified version of the neocortical part of the Norman and O'Reilly model, whereas capacity decreases as coding becomes sparser for a simplified version of the Sohal and Hasselmo model. Thus in general, and in contrast to associative memory networks, sparse encoding results in little or no advantage for the capacity of familiarity discrimination networks. Hence it may be less important for coding to be sparse in the perirhinal cortex than it is in the hippocampus. Additionally, it is established that the capacities of the networks are strongly dependent on the precise form of the learning rules (synaptic plasticity) used in the network. This finding indicates that the precise characteristics of synaptic plastic changes in the real brain are likely to have major influences on storage capacity.
Unlabelled:
The hippocampus is proposed to be critical in distinguishing between similar experiences by performing pattern separation computations that create orthogonalized representations for related episodes. Previous neuroimaging studies have provided indirect evidence that the dentate gyrus (DG) and CA3 hippocampal subregions support pattern separation by inferring the nature of underlying representations from the observation of novelty signals. Here, we use ultra-high-resolution fMRI at 7 T and multivariate pattern analysis to provide compelling evidence that the DG subregion specifically sustains representations of similar scenes that are less overlapping than in other hippocampal (e.g., CA3) and medial temporal lobe regions (e.g., entorhinal cortex). Further, we provide evidence that novelty signals within the DG are stimulus specific rather than generic in nature. Our study, in providing a mechanistic link between novelty signals and the underlying representations, constitutes the first demonstration that the human DG performs pattern separation.
Significance statement:
A fundamental property of an episodic memory system is the ability to minimize interference between similar episodes. The dentate gyrus (DG) subregion of the hippocampus is widely viewed to realize this function through a computation referred to as pattern separation, which creates distinct nonoverlapping neural codes for individual events. Here, we leveraged 7 T fMRI to test the hypothesis that this region supports pattern separation. Our results demonstrate that the DG supports representations of similar scenes that are less overlapping than those in neighboring subregions. The current study therefore is the first to offer compelling evidence that the human DG supports pattern separation by obtaining critical empirical data at the representational level: the level where this computation is defined.
This book travels a colorful journey into the fascinatingly diverse world of interneurons, an important class of highly heterogeneous cells found in all cortical neuronal networks. Interneurons are known to play key roles in many brain functions, from sensory processing to neuronal oscillations linked to learning and memory. This book aims to provide new insights into the striking degree of cellular diversity found in interneuronal microcircuits. The book discusses the history of research into interneuronal variability, the developmental origins of interneuronal diversity, the functional roles of heterogeneity in neuronal circuits, contemporary interneuronal classification systems, and the genetic and homeostatic mechanisms that shape the degree of cell to cell variability within interneuronal populations. It elaborates on new ideas about interneuronal diversity that rest upon recent theoretical and experimental results, with arguments touching upon evolution, animal behavior, and the mathematical theory of small world networks.
I propose that synchronization affects communication between neuronal groups. Gamma-band (30-90 Hz) synchronization modulates excitation rapidly enough that it escapes the following inhibition and activates postsynaptic neurons effectively. Synchronization also ensures that a presynaptic activation pattern arrives at postsynaptic neurons in a temporally coordinated manner. At a postsynaptic neuron, multiple presynaptic groups converge, e.g., representing different stimuli. If a stimulus is selected by attention, its neuronal representation shows stronger and higher-frequency gamma-band synchronization. Thereby, the attended stimulus representation selectively entrains postsynaptic neurons. The entrainment creates sequences of short excitation and longer inhibition that are coordinated between pre- and postsynaptic groups to transmit the attended representation and shut out competing inputs. The predominantly bottom-up-directed gamma-band influences are controlled by predominantly top-down-directed alpha-beta-band (8-20 Hz) influences. Attention itself samples stimuli at a 7-8 Hz theta rhythm. Thus, several rhythms and their interplay render neuronal communication effective, precise, and selective.
It remains an outstanding question whether gamma-band oscillations reflect unitary cognitive processes within the same task. EEG/MEG studies do lack the resolution or coverage to address the highly debated question whether single gamma activity patterns are linked with multiple cognitive modules or alternatively each pattern associates with a specific cognitive module, within the same coherent perceptual task. One way to disentangle these issues would be to provide direct identification of their sources, by combining different techniques. Here, we directly examined these questions by performing simultaneous EEG/fMRI using an ambiguous perception paradigm requiring holistic integration. We found that distinct gamma frequency sub-bands reflect different neural substrates and cognitive mechanisms when comparing object perception states vs. no categorical perception. A low gamma sub-band (near 40 Hz) activity was tightly related to the decision making network, and in particular the anterior insula. A high gamma sub-band (∼60 Hz) could be linked to early visual processing regions. The demonstration of a clear functional topography for distinct gamma sub-bands within the same task shows that distinct gamma-band modulations underlie sensory processing and perceptual decision mechanisms. Hum Brain Mapp, 2014. 2014 Wiley Periodicals, Inc.
Neuronal oscillations have been hypothesized to play an important role in cognition and its ensuing behavior, but evidence that links a specific neuronal oscillation to a discrete cognitive event is largely lacking. We measured neuronal activity in the entorhinal-hippocampal circuit while mice performed a reward-based spatial working memory task. During the memory retention period, a transient burst of high gamma synchronization preceded an animal's correct choice in both prospective planning and retrospective mistake correction, but not an animal's incorrect choice. Optogenetic inhibition of the circuit targeted to the choice point area resulted in a coordinated reduction in both high gamma synchrony and correct execution of a working-memory-guided behavior. These findings suggest that transient high gamma synchrony contributes to the successful execution of spatial working memory. Furthermore, our data are consistent with an association between transient high gamma synchrony and explicit awareness of the working memory content.
There is now strong evidence that gamma frequency oscillations occur during the engagement of cortical regions. These oscillations involve gamma frequency feedback inhibition. Thus, understanding the properties of this form of inhibition is critical to understanding how excitation and inhibition interact to determine which cells fire and, more generally, how cortex performs computations. In previous work, we argued that gamma frequency inhibition performs a type of winner-take-all computation that obeys simple rules: 1) cells fire if their excitation is within E% of the cell with maximum excitation; 2) E%max is determined by the delay of feedback inhibition and the membrane time constant. This framework was previously applied to the best-studied cortical computation, orientation selectivity of cells in V1. Measurements show that orientation tuning is insensitive to illumination contrast. We showed that this finding can be simply explained by the E%max model. Recently, a new property of orientation selectivity has been discovered: orientation tuning varies with the phase of the gamma oscillation. Here, we show that this too can be simply explained by the E%max model. These successes suggest that simple rules underlie the selection of which cells fire in cortical networks.
Gamma frequency (30-80 Hz) oscillations are implicated in memory processing. Such rhythmic activity can be generated intrinsically in the CA3 region of the hippocampus from where it can propagate to the CA1 area. To uncover the synaptic mechanisms underlying the intrahippocampal spread of gamma oscillations, we recorded local field potentials, as well as action potentials and synaptic currents in anatomically identified CA1 and CA3 neurons during carbachol-induced gamma oscillations in mouse hippocampal slices. The firing of the vast majority of CA1 neurons and all CA3 neurons was phase-coupled to the oscillations recorded in the stratum pyramidale of the CA1 region. The predominant synaptic input to CA1 interneurons was excitatory, and their discharge followed the firing of CA3 pyramidal cells at a latency indicative of monosynaptic connections. Correlation analysis of the input-output characteristics of the neurons and local pharmacological block of inhibition both agree with a model in which glutamatergic CA3 input controls the firing of CA1 interneurons, with local pyramidal cell activity having a minimal role. The firing of phase-coupled CA1 pyramidal cells was controlled principally by their inhibitory inputs, which dominated over excitation. Our results indicate that the synchronous firing of CA3 pyramidal cells rhythmically recruits CA1 interneurons and that this feedforward inhibition generates the oscillatory activity in CA1. These findings identify distinct synaptic mechanisms underlying the generation of gamma frequency oscillations in neighboring hippocampal subregions.
Although inhibition plays a major role in the function of the mammalian neocortex, the circuit connectivity of GABAergic interneurons has remained poorly understood. The authors review recent studies of the connections made to and from interneurons, highlighting the overarching principle of a high density of unspecific connections in inhibitory connectivity. Whereas specificity remains in the subcellular targeting of excitatory neurons by interneurons, the general strategy appears to be for interneurons to provide a global "blanket of inhibition" to nearby neurons. In the review, the authors highlight the fact that the function of interneurons, which remains elusive, will be informed by understanding the structure of their connectivity as well as the dynamics of inhibitory synaptic connections. In a last section, the authors describe briefly the link between dense inhibitory networks and different interneuron functions described in the neocortex.
Networks of hippocampal interneurons, with pyramidal neuronspharmacologically disconnected, can generate gamma-frequency(20 Hz and above) oscillations. Experiments and models have shownhow the network frequency depends on excitation of the interneurons,and on the parameters of GABA{\rm A}-mediated IPSCs betweenthe interneurons (conductance and time course). Herewe use network simulations to investigate how pyramidal cells, connected tothe interneurons and to each other throughAMPA-type and/or NMDA-type glutamatereceptors, might modify the interneuron network oscillation. With orwithout AMPA-receptor mediated excitation of the interneurons, the pyramidal cells and interneurons fired in phaseduring the gamma oscillation. Synaptic excitation of the interneuronsby pyramidal cellscaused them to fire spike doublets or short bursts at gammafrequencies, thereby slowing the population rhythm.Rhythmic synchronized IPSPs allowed the pyramidal cells toencode their mean excitation by their phase of firing relativeto the population waves.Recurrent excitation between the pyramidal cells couldmodify the phase of firing relative to the population waves.Our model suggests that pools of synaptically interconnectedinhibitory cells are sufficient to produce gamma frequency rhythms,but the network behavior can be modified by participation ofpyramidal cells.
The curse of dimensionality poses severe challenges to both technical and conceptual progress in neuroscience. In particular, it plagues our ability to acquire, process, and model high-dimensional data sets. Moreover, neural systems must cope with the challenge of processing data in high dimensions to learn and operate successfully within a complex world. We review recent mathematical advances that provide ways to combat dimensionality in specific situations. These advances shed light on two dual questions in neuroscience. First, how can we as neuroscientists rapidly acquire high-dimensional data from the brain and subsequently extract meaningful models from limited amounts of these data? And second, how do brains themselves process information in their intrinsically high-dimensional patterns of neural activity as well as learn meaningful, generalizable models of the external world from limited experience?
Neurons can operate in two distinct ways, depending on the duration of the interval over which they effectively summate incoming synaptic potentials. If this interval is of the order of the mean interspike interval or longer, neurons act effectively as temporal integrators and transmit temporal patterns with only low reliability. If, by contrast, the integration interval is short compared to the interspike interval, neurons act essentially as coincidence detectors, relay preferentially synchronized input, and the temporal structure of their output is a direct function of the input pattern. Recently, interest in this distinction has been revived because experimental and theoretical results suggest that synchronous firing of neurons might play an important role for information processing in the cortex. Here, we argue that coincidence detection, rather than temporal integration, might be a prevalent operation mode of cortical neurons. We base our arguments on established biophysical properties of cortical neurons and on particular features of cortical dynamics.
Cortical processing reflects the interplay of synaptic excitation and synaptic inhibition. Rapidly accumulating evidence is highlighting the crucial role of inhibition in shaping spontaneous and sensory-evoked cortical activity and thus underscores how a better knowledge of inhibitory circuits is necessary for our understanding of cortical function. We discuss current views of how inhibition regulates the function of cortical neurons and point to a number of important open questions.
The human hippocampus supports the formation of episodic memory without confusing new memories with old ones. To accomplish this, the brain must disambiguate memories (i.e., accentuate the differences between experiences). There is convergent evidence linking pattern separation to the dentate gyrus. Damage to the dentate gyrus reduces an organism's ability to differentiate between similar objects. The dentate gyrus has tenfold more principle cells than its cortical input, allowing for a divergence in information flow. Dentate gyrus granule neurons also show a very different pattern of representing the environment than "classic" place cells in CA1 and CA3, or grid cells in the entorhinal cortex. More recently immediate early genes have been used to "timestamp" activity of individual cells throughout the dentate gyrus. These data indicate that the dentate gyrus robustly differentiates similar situations. The degree of differentiation is non-linear, with even small changes in input inducing a near maximal response in the dentate. Furthermore this differentiation occurs throughout the dentate gyrus longitudinal (dorsal-ventral) axis. Conversely, the data point to a divergence in information processing between the dentate gyrus suprapyramidal and infrapyramidal blades possibly related to differences in organization within these regions. The accumulated evidence from different approaches converges to support a role for the dentate gyrus in pattern separation. There are however inconsistencies that may require incorporation of neurogenesis and hippocampal microcircuits into the currents models. They also suggest different roles for the dentate gyrus suprapyramidal and infrapyramidal blades, and the responsiveness of CA3 to dentate input.
When the brain goes from wakefulness to sleep, cortical neurons begin to undergo slow oscillations in their membrane potential that are synchronized by thalamocortical circuits and reflected in EEG slow waves. To provide a self-consistent account of the transition from wakefulness to sleep and of the generation of sleep slow waves, we have constructed a large-scale computer model that encompasses portions of two visual areas and associated thalamic and reticular thalamic nuclei. Thousands of model neurons, incorporating several intrinsic currents, are interconnected with millions of thalamocortical, corticothalamic, and both intra- and interareal corticocortical connections. In the waking mode, the model exhibits irregular spontaneous firing and selective responses to visual stimuli. In the sleep mode, neuromodulatory changes lead to slow oscillations that closely resemble those observed in vivo and in vitro. A systematic exploration of the effects of intrinsic currents and network parameters on the initiation, maintenance, and termination of slow oscillations shows the following. 1) An increase in potassium leak conductances is sufficient to trigger the transition from wakefulness to sleep. 2) The activation of persistent sodium currents is sufficient to initiate the up-state of the slow oscillation. 3) A combination of intrinsic and synaptic currents is sufficient to maintain the up-state. 4) Depolarization-activated potassium currents and synaptic depression terminate the up-state. 5) Corticocortical connections synchronize the slow oscillation. The model is the first to integrate intrinsic neuronal properties with detailed thalamocortical anatomy and reproduce neural activity patterns in both wakefulness and sleep, thereby providing a powerful tool to investigate the role of sleep in information transmission and plasticity.
Rate remapping is a recently revealed neural code in which sensory information modulates the firing rate of hippocampal place cells. The mechanism underlying rate remapping is unknown. Its characteristic modulation, however, must arise from the interaction of the two major inputs to the hippocampus, the medial entorhinal cortex (MEC), in which grid cells represent the spatial position of the rat, and the lateral entorhinal cortex (LEC), in which cells represent the sensory properties of the environment. We have used computational methods to elucidate the mechanism by which this interaction produces rate remapping. We show that the convergence of LEC and MEC inputs, in conjunction with a competitive network process mediated by feedback inhibition, can account quantitatively for this phenomenon. The same principle accounts for why different place fields of the same cell vary independently as sensory information is altered. Our results show that rate remapping can be explained in terms of known mechanisms.
Recent studies suggest that cross-frequency coupling (CFC) might play a functional role in neuronal computation, communication and learning. In particular, the strength of phase-amplitude CFC differs across brain areas in a task-relevant manner, changes quickly in response to sensory, motor and cognitive events, and correlates with performance in learning tasks. Importantly, whereas high-frequency brain activity reflects local domains of cortical processing, low-frequency brain rhythms are dynamically entrained across distributed brain regions by both external sensory input and internal cognitive events. CFC might thus serve as a mechanism to transfer information from large-scale brain networks operating at behavioral timescales to the fast, local cortical processing required for effective computation and synaptic modification, thus integrating functional systems across multiple spatiotemporal scales.
Granule cells of the dentate gyrus (DG) generally have multiple place fields, whereas CA3 cells, which are second order, have only a single place field. Here, we explore the mechanisms by which the high selectivity of CA3 cells is achieved. Previous work showed that the multiple place fields of DG neurons could be quantitatively accounted for by a model based on the number and strength of grid cell inputs and a competitive network interaction in the DG that is mediated by gamma frequency feedback inhibition. We have now built a model of CA3 based on similar principles. CA3 cells receive input from an average of one active DG cell and from 1,400 cortical grid cells. Based on experimental findings, we have assumed a linear interaction of the two pathways. The results show that simulated CA3 cells generally have a single place field, as observed experimentally. Thus, a two-step process based on simple rules (and that can occur without learning) is able to explain how grid cell inputs to the hippocampus give rise to cells having ultimate spatial selectivity. The CA3 processes that produce a single place depend critically on the competitive network processes and do not require the direct cortical inputs to CA3, which are therefore likely to perform some other unknown function.