Inhibitory plasticity dictates the sign of plasticity at excitatory synapses

The gut-brain vagal axis scales hippocampal memory processes and plasticity

The vagus nerve serves as an interoceptive relay between the body and the brain. Despite its well-established role in feeding behaviors, energy metabolism, and cognitive functions, the intricate functional processes linking the vagus nerve to the hippocampus and its contribution to learning and memory dynamics remain still elusive. Here, we investigated whether and how the gut-brain vagal axis contributes to hippocampal learning and memory processes at behavioral, functional, cellular, and molecular levels. Our results indicate that the integrity of the vagal axis is essential for long-term recognition memories, while sparing other forms of memory. In addition, by combing multi-scale approaches, our findings show that the gut-brain vagal tone exerts a permissive role in scaling intracellular signaling events, gene expressions, hippocampal dendritic spines density as well as functional long-term plasticities (LTD and LTP). These results highlight the critical role of the gut-brain vagal axis in maintaining the spontaneous and homeostatic functions of hippocampal ensembles and in regulating their learning and memory functions. In conclusion, our study provides comprehensive insights into the multifaceted involvement of the gut-brain vagal axis in shaping time-dependent hippocampal learning and memory dynamics. Understanding the mechanisms underlying this interoceptive body-brain neuronal communication may pave the way for novel therapeutic approaches in conditions associated with cognitive decline, including neurodegenerative disorders.

Developmental hearing loss-induced perceptual deficits are rescued by genetic restoration of cortical inhibition

Even a transient period of hearing loss during the developmental critical period can induce long-lasting deficits in temporal and spectral perception. These perceptual deficits correlate with speech perception in humans. In gerbils, these hearing loss-induced perceptual deficits are correlated with a reduction of both ionotropic GABAA and metabotropic GABAB receptor-mediated synaptic inhibition in auditory cortex, but most research on critical period plasticity has focused on GABAA receptors. Therefore, we developed viral vectors to express proteins that would upregulate gerbil postsynaptic inhibitory receptor subunits (GABAA, Gabra1; GABAB, Gabbr1b) in pyramidal neurons, and an enzyme that mediates GABA synthesis (GAD65) presynaptically in parvalbumin-expressing interneurons. A transient period of developmental hearing loss during the auditory critical period significantly impaired perceptual performance on two auditory tasks: amplitude modulation depth detection and spectral modulation depth detection. We then tested the capacity of each vector to restore perceptual performance on these auditory tasks. While both GABA receptor vectors increased the amplitude of cortical inhibitory postsynaptic potentials, only viral expression of postsynaptic GABAB receptors improved perceptual thresholds to control levels. Similarly, presynaptic GAD65 expression improved perceptual performance on spectral modulation detection. These findings suggest that recovering performance on auditory perceptual tasks depends on GABAB receptor-dependent transmission at the auditory cortex parvalbumin to pyramidal synapse and point to potential therapeutic targets for developmental sensory disorders.

Co-existence of synaptic plasticity and metastable dynamics in a spiking model of cortical circuits

Evidence for metastable dynamics and its role in brain function is emerging at a fast pace and is changing our understanding of neural coding by putting an emphasis on hidden states of transient activity. Clustered networks of spiking neurons have enhanced synaptic connections among groups of neurons forming structures called cell assemblies; such networks are capable of producing metastable dynamics that is in agreement with many experimental results. However, it is unclear how a clustered network structure producing metastable dynamics may emerge from a fully local plasticity rule, i.e., a plasticity rule where each synapse has only access to the activity of the neurons it connects (as opposed to the activity of other neurons or other synapses). Here, we propose a local plasticity rule producing ongoing metastable dynamics in a deterministic, recurrent network of spiking neurons. The metastable dynamics co-exists with ongoing plasticity and is the consequence of a self-tuning mechanism that keeps the synaptic weights close to the instability line where memories are spontaneously reactivated. In turn, the synaptic structure is stable to ongoing dynamics and random perturbations, yet it remains sufficiently plastic to remap sensory representations to encode new sets of stimuli. Both the plasticity rule and the metastable dynamics scale well with network size, with synaptic stability increasing with the number of neurons. Overall, our results show that it is possible to generate metastable dynamics over meaningful hidden states using a simple but biologically plausible plasticity rule which co-exists with ongoing neural dynamics.

Co-dependent excitatory and inhibitory plasticity accounts for quick, stable and long-lasting memories in biological networks

The brain's functionality is developed and maintained through synaptic plasticity. As synapses undergo plasticity, they also affect each other. The nature of such 'co-dependency' is difficult to disentangle experimentally, because multiple synapses must be monitored simultaneously. To help understand the experimentally observed phenomena, we introduce a framework that formalizes synaptic co-dependency between different connection types. The resulting model explains how inhibition can gate excitatory plasticity while neighboring excitatory-excitatory interactions determine the strength of long-term potentiation. Furthermore, we show how the interplay between excitatory and inhibitory synapses can account for the quick rise and long-term stability of a variety of synaptic weight profiles, such as orientation tuning and dendritic clustering of co-active synapses. In recurrent neuronal networks, co-dependent plasticity produces rich and stable motor cortex-like dynamics with high input sensitivity. Our results suggest an essential role for the neighborly synaptic interaction during learning, connecting micro-level physiology with network-wide phenomena.

Somatostatin-expressing interneurons modulate neocortical network through GABAb receptors in a synapse-specific manner

The firing activity of somatostatin-expressing inhibitory neurons (SST-INs) can suppress network activity via both GABAa and GABAb receptors (Rs). Although SST-INs do not receive GABAaR input from other SST-INs, it is possible that SST-IN-released GABA could suppress the activity of SST-INs themselves via GABAbRs, providing a negative feedback loop. Here we characterized the influence of GABAbR modulation on SST-IN activity in layer 2/3 of the somatosensory cortex in mice. We compared this to the effects of GABAbR activation on parvalbumin-expressing interneurons (PV-INs). Using in vitro whole-cell patch clamp recordings, pharmacological and optogenetic manipulations, we found that the firing activity of SST-INs suppresses excitatory drive to themselves via presynaptic GABAbRs. Postsynaptic GABAbRs did not influence SST-IN spontaneous activity or intrinsic excitability. Although GABAbRs at pre- and postsynaptic inputs to PV-INs are modestly activated during cortical network activity in vitro, the spontaneous firing of SST-INs was not the source of GABA driving this GABAbR activation. Thus, SST-IN firing regulates excitatory synaptic strength through presynaptic GABAbRs at connections between pyramidal neurons (Pyr-Pyr) and synapses between pyramidal neurons and SST-INs (Pyr-SST), but not Pyr-PV and PV-Pyr synapses. Our study indicates that two main types of neocortical inhibitory interneurons are differentially modulated by SST-IN-mediated GABA release.

Advances in the Electrophysiological Recordings of Long-Term Potentiation

Understanding neuronal firing patterns and long-term potentiation (LTP) induction in studying learning, memory, and neurological diseases is critical. However, recently, despite the rapid advancement in neuroscience, we are still constrained by the experimental design, detection tools for exploring the mechanisms and pathways involved in LTP induction, and detection ability of neuronal action potentiation signals. This review will reiterate LTP-related electrophysiological recordings in the mammalian brain for nearly 50 years and explain how excitatory and inhibitory neural LTP results have been detected and described by field- and single-cell potentials, respectively. Furthermore, we focus on describing the classic model of LTP of inhibition and discuss the inhibitory neuron activity when excitatory neurons are activated to induce LTP. Finally, we propose recording excitatory and inhibitory neurons under the same experimental conditions by combining various electrophysiological technologies and novel design suggestions for future research. We discussed different types of synaptic plasticity, and the potential of astrocytes to induce LTP also deserves to be explored in the future.

The plasticitome of cortical interneurons

Hebb postulated that, to store information in the brain, assemblies of excitatory neurons coding for a percept are bound together via associative long-term synaptic plasticity. In this view, it is unclear what role, if any, is carried out by inhibitory interneurons. Indeed, some have argued that inhibitory interneurons are not plastic. Yet numerous recent studies have demonstrated that, similar to excitatory neurons, inhibitory interneurons also undergo long-term plasticity. Here, we discuss the many diverse forms of long-term plasticity that are found at inputs to and outputs from several types of cortical inhibitory interneuron, including their plasticity of intrinsic excitability and their homeostatic plasticity. We explain key plasticity terminology, highlight key interneuron plasticity mechanisms, extract overarching principles and point out implications for healthy brain functionality as well as for neuropathology. We introduce the concept of the plasticitome - the synaptic plasticity counterpart to the genome or the connectome - as well as nomenclature and definitions for dealing with this rich diversity of plasticity. We argue that the great diversity of interneuron plasticity rules is best understood at the circuit level, for example as a way of elucidating how the credit-assignment problem is solved in deep biological neural networks.

Experience-dependent plasticity of gustatory insular cortex circuits and taste preferences

Early experience with food influences taste preference in adulthood. How gustatory experience influences development of taste preferences and refinement of cortical circuits has not been investigated. Here, we exposed weanling mice to an array of taste solutions and determined the effects on the preference for sweet in adulthood. We demonstrate an experience-dependent shift in sucrose preference persisting several weeks following the termination of exposure. A shift in sucrose palatability, altered neural responsiveness to sucrose, and inhibitory synaptic plasticity in the gustatory portion of the insular cortex (GC) were also induced. The modulation of sweet preference occurred within a restricted developmental window, but restoration of the capacity for inhibitory plasticity in adult GC reactivated the sensitivity of sucrose preference to taste experience. Our results establish a fundamental link between gustatory experience, sweet preference, inhibitory plasticity, and cortical circuit function and highlight the importance of early life nutrition in setting taste preferences.

Developmental hearing loss-induced perceptual deficits are rescued by cortical expression of GABAB receptors

Even transient periods of developmental hearing loss during the developmental critical period have been linked to long-lasting deficits in auditory perception, including temporal and spectral processing, which correlate with speech perception and educational attainment. In gerbils, hearing loss-induced perceptual deficits are correlated with a reduction of both ionotropic GABAA and metabotropic GABAB receptor-mediated synaptic inhibition in auditory cortex, but most research on critical period plasticity has focused on GABAA receptors. We developed viral vectors to express both endogenous GABAA or GABAB receptor subunits in auditory cortex and tested their capacity to restore perception of temporal and spectral auditory cues following critical period hearing loss in the Mongolian gerbil. HL significantly impaired perception of both temporal and spectral auditory cues. While both vectors similarly increased IPSCs in auditory cortex, only overexpression of GABAB receptors improved perceptual thresholds after HL to be similar to those of animals without developmental hearing loss. These findings identify the GABAB receptor as an important regulator of sensory perception in cortex and point to potential therapeutic targets for developmental sensory disorders.

Spike timing-dependent plasticity under imbalanced excitation and inhibition reduces the complexity of neural activity

Excitatory and inhibitory neurons are fundamental components of the brain, and healthy neural circuits are well balanced between excitation and inhibition (E/I balance). However, it is not clear how an E/I imbalance affects the self-organization of the network structure and function in general. In this study, we examined how locally altered E/I balance affects neural dynamics such as the connectivity by activity-dependent formation, the complexity (multiscale entropy) of neural activity, and information transmission. In our simulation, a spiking neural network model was used with the spike-timing dependent plasticity rule to explore the above neural dynamics. We controlled the number of inhibitory neurons and the inhibitory synaptic weights in a single neuron group out of multiple neuron groups. The results showed that a locally increased E/I ratio strengthens excitatory connections, reduces the complexity of neural activity, and decreases information transmission between neuron groups in response to an external input. Finally, we argued the relationship between our results and excessive connections and low complexity of brain activity in the neuropsychiatric brain disorders.

Where is the error? Hierarchical predictive coding through dendritic error computation

Top-down feedback in cortex is critical for guiding sensory processing, which has prominently been formalized in the theory of hierarchical predictive coding (hPC). However, experimental evidence for error units, which are central to the theory, is inconclusive and it remains unclear how hPC can be implemented with spiking neurons. To address this, we connect hPC to existing work on efficient coding in balanced networks with lateral inhibition and predictive computation at apical dendrites. Together, this work points to an efficient implementation of hPC with spiking neurons, where prediction errors are computed not in separate units, but locally in dendritic compartments. We then discuss the correspondence of this model to experimentally observed connectivity patterns, plasticity, and dynamics in cortex.

Stability and learning in excitatory synapses by nonlinear inhibitory plasticity

Synaptic changes are hypothesized to underlie learning and memory formation in the brain. But Hebbian synaptic plasticity of excitatory synapses on its own is unstable, leading to either unlimited growth of synaptic strengths or silencing of neuronal activity without additional homeostatic mechanisms. To control excitatory synaptic strengths, we propose a novel form of synaptic plasticity at inhibitory synapses. Using computational modeling, we suggest two key features of inhibitory plasticity, dominance of inhibition over excitation and a nonlinear dependence on the firing rate of postsynaptic excitatory neurons whereby inhibitory synaptic strengths change with the same sign (potentiate or depress) as excitatory synaptic strengths. We demonstrate that the stable synaptic strengths realized by this novel inhibitory plasticity model affects excitatory/inhibitory weight ratios in agreement with experimental results. Applying a disinhibitory signal can gate plasticity and lead to the generation of receptive fields and strong bidirectional connectivity in a recurrent network. Hence, a novel form of nonlinear inhibitory plasticity can simultaneously stabilize excitatory synaptic strengths and enable learning upon disinhibition.

Regulation of circuit organization and function through inhibitory synaptic plasticity

Diverse inhibitory neurons in the mammalian brain shape circuit connectivity and dynamics through mechanisms of synaptic plasticity. Inhibitory plasticity can establish excitation/inhibition (E/I) balance, control neuronal firing, and affect local calcium concentration, hence regulating neuronal activity at the network, single neuron, and dendritic level. Computational models can synthesize multiple experimental results and provide insight into how inhibitory plasticity controls circuit dynamics and sculpts connectivity by identifying phenomenological learning rules amenable to mathematical analysis. We highlight recent studies on the role of inhibitory plasticity in modulating excitatory plasticity, forming structured networks underlying memory formation and recall, and implementing adaptive phenomena and novelty detection. We conclude with experimental and modeling progress on the role of interneuron-specific plasticity in circuit computation and context-dependent learning.

Acetate supplementation restores cognitive deficits caused by ARID1A haploinsufficiency in excitatory neurons

Mutations in AT-rich interactive domain-containing protein 1A (ARID1A) cause Coffin-Siris syndrome (CSS), a rare genetic disorder that results in mild to severe intellectual disabilities. However, the biological role of ARID1A in the brain remains unclear. In this study, we report that the haploinsufficiency of ARID1A in excitatory neurons causes cognitive impairment and defects in hippocampal synaptic transmission and dendritic morphology in mice. Similarly, human embryonic stem cell-derived excitatory neurons with deleted ARID1A exhibit fewer dendritic branches and spines, and abnormal electrophysiological activity. Importantly, supplementation of acetate, an epigenetic metabolite, can ameliorate the morphological and electrophysiological deficits observed in mice with Arid1a haploinsufficiency, as well as in ARID1A-null human excitatory neurons. Mechanistically, transcriptomic and ChIP-seq analyses demonstrate that acetate supplementation can increase the levels of H3K27 acetylation at the promoters of key regulatory genes associated with neural development and synaptic transmission. Collectively, these findings support the essential roles of ARID1A in the excitatory neurons and cognition and suggest that acetate supplementation could be a potential therapeutic intervention for CSS.

Developmental depression-to-facilitation shift controls excitation-inhibition balance

Changes in the short-term dynamics of excitatory synapses over development have been observed throughout cortex, but their purpose and consequences remain unclear. Here, we propose that developmental changes in synaptic dynamics buffer the effect of slow inhibitory long-term plasticity, allowing for continuously stable neural activity. Using computational modeling we demonstrate that early in development excitatory short-term depression quickly stabilises neural activity, even in the face of strong, unbalanced excitation. We introduce a model of the commonly observed developmental shift from depression to facilitation and show that neural activity remains stable throughout development, while inhibitory synaptic plasticity slowly balances excitation, consistent with experimental observations. Our model predicts changes in the input responses from phasic to phasic-and-tonic and more precise spike timings. We also observe a gradual emergence of short-lasting memory traces governed by short-term plasticity development. We conclude that the developmental depression-to-facilitation shift may control excitation-inhibition balance throughout development with important functional consequences.

Using computational modelling this study proposes that the commonly observed depression-to-facilitation shift across development controls excitation-inhibition balance in the brain.

Interrogating the function of GABAA receptors in the brain with optogenetic pharmacology

To better understand neural circuits and behavior, microbial opsins have been developed as optogenetic tools for stimulating or inhibiting action potentials with high temporal and spatial precision. However, if we seek a more reductionist understanding of how neuronal circuits operate, we also need high-resolution tools for perturbing the function of synapses. By combining photochemical tools and molecular biology, a wide variety of light-regulated neurotransmitter receptors have been developed, enabling photo-control of excitatory, inhibitory, and modulatory synaptic transmission. Here we focus on photo-control of GABA_A receptors, ligand-gated Cl^- channels that underlie almost all synaptic inhibition in the mammalian brain. By conjugating a photoswitchable tethered ligand onto a genetically-modified subunit of the GABA_A receptor, light-sensitivity can be conferred onto specific isoforms of the receptor. Through gene editing, this attachment site can be knocked into the genome, enabling photocontrol of endogenous GABA_A receptors. This strategy can be employed to explore the cell biology and neurophysiology of GABA_A receptors. This includes investigating how specific isoforms contribute to synaptic and tonic inhibition and understanding the roles they play in brain development, long-term synaptic plasticity, and learning and memory.

Metastable dynamics of neural circuits and networks

Cortical neurons emit seemingly erratic trains of action potentials or "spikes," and neural network dynamics emerge from the coordinated spiking activity within neural circuits. These rich dynamics manifest themselves in a variety of patterns, which emerge spontaneously or in response to incoming activity produced by sensory inputs. In this Review, we focus on neural dynamics that is best understood as a sequence of repeated activations of a number of discrete hidden states. These transiently occupied states are termed "metastable" and have been linked to important sensory and cognitive functions. In the rodent gustatory cortex, for instance, metastable dynamics have been associated with stimulus coding, with states of expectation, and with decision making. In frontal, parietal, and motor areas of macaques, metastable activity has been related to behavioral performance, choice behavior, task difficulty, and attention. In this article, we review the experimental evidence for neural metastable dynamics together with theoretical approaches to the study of metastable activity in neural circuits. These approaches include (i) a theoretical framework based on non-equilibrium statistical physics for network dynamics; (ii) statistical approaches to extract information about metastable states from a variety of neural signals; and (iii) recent neural network approaches, informed by experimental results, to model the emergence of metastable dynamics. By discussing these topics, we aim to provide a cohesive view of how transitions between different states of activity may provide the neural underpinnings for essential functions such as perception, memory, expectation, or decision making, and more generally, how the study of metastable neural activity may advance our understanding of neural circuit function in health and disease.

Emergence of synaptic organization and computation in dendrites

Single neurons in the brain exhibit astounding computational capabilities, which gradually emerge throughout development and enable them to become integrated into complex neural circuits. These capabilities derive in part from the precise arrangement of synaptic inputs on the neurons' dendrites. While the full computational benefits of this arrangement are still unknown, a picture emerges in which synapses organize according to their functional properties across multiple spatial scales. In particular, on the local scale (tens of microns), excitatory synaptic inputs tend to form clusters according to their functional similarity, whereas on the scale of individual dendrites or the entire tree, synaptic inputs exhibit dendritic maps where excitatory synapse function varies smoothly with location on the tree. The development of this organization is supported by inhibitory synapses, which are carefully interleaved with excitatory synapses and can flexibly modulate activity and plasticity of excitatory synapses. Here, we summarize recent experimental and theoretical research on the developmental emergence of this synaptic organization and its impact on neural computations.

Local dendritic balance enables learning of efficient representations in networks of spiking neurons

How can neural networks learn to efficiently represent complex and high-dimensional inputs via local plasticity mechanisms? Classical models of representation learning assume that feedforward weights are learned via pairwise Hebbian-like plasticity. Here, we show that pairwise Hebbian-like plasticity works only under unrealistic requirements on neural dynamics and input statistics. To overcome these limitations, we derive from first principles a learning scheme based on voltage-dependent synaptic plasticity rules. Here, recurrent connections learn to locally balance feedforward input in individual dendritic compartments and thereby can modulate synaptic plasticity to learn efficient representations. We demonstrate in simulations that this learning scheme works robustly even for complex high-dimensional inputs and with inhibitory transmission delays, where Hebbian-like plasticity fails. Our results draw a direct connection between dendritic excitatory-inhibitory balance and voltage-dependent synaptic plasticity as observed in vivo and suggest that both are crucial for representation learning.

Sensory coding and contrast invariance emerge from the control of plastic inhibition over emergent selectivity

Visual stimuli are represented by a highly efficient code in the primary visual cortex, but the development of this code is still unclear. Two distinct factors control coding efficiency: Representational efficiency, which is determined by neuronal tuning diversity, and metabolic efficiency, which is influenced by neuronal gain. How these determinants of coding efficiency are shaped during development, supported by excitatory and inhibitory plasticity, is only partially understood. We investigate a fully plastic spiking network of the primary visual cortex, building on phenomenological plasticity rules. Our results suggest that inhibitory plasticity is key to the emergence of tuning diversity and accurate input encoding. We show that inhibitory feedback (random and specific) increases the metabolic efficiency by implementing a gain control mechanism. Interestingly, this led to the spontaneous emergence of contrast-invariant tuning curves. Our findings highlight that (1) interneuron plasticity is key to the development of tuning diversity and (2) that efficient sensory representations are an emergent property of the resulting network.

Synaptic plasticity is crucial for the development of efficient input representation in the different sensory cortices, such as the primary visual cortex. Efficient visual representation is determined by two factors: representational efficiency, i.e. how many different input features can be represented, and metabolic efficiency, i.e. how many spikes are required to represent a specific feature. Previous research has pointed out the importance of plasticity at excitatory synapses to achieve high representational efficiency and feedback inhibition as a gain control mechanism for controlling metabolic efficiency. However, it is only partially understood how the influence of inhibitory plasticity on excitatory plasticity can lead to an efficient representation. Using a spiking neural network, we show that plasticity at feed-forward and feedback inhibitory synapses is necessary for the emergence of well-distributed neuronal selectivity to improve representational efficiency. Further, the emergent balance between excitatory and inhibitory currents improves the metabolic efficiency, and leads to contrast-invariant tuning as an inherent network property. Extending previous work, our simulation results highlight the importance of plasticity at inhibitory synapses.

The generation of cortical novelty responses through inhibitory plasticity

Animals depend on fast and reliable detection of novel stimuli in their environment. Neurons in multiple sensory areas respond more strongly to novel in comparison to familiar stimuli. Yet, it remains unclear which circuit, cellular, and synaptic mechanisms underlie those responses. Here, we show that spike-timing-dependent plasticity of inhibitory-to-excitatory synapses generates novelty responses in a recurrent spiking network model. Inhibitory plasticity increases the inhibition onto excitatory neurons tuned to familiar stimuli, while inhibition for novel stimuli remains low, leading to a network novelty response. The generation of novelty responses does not depend on the periodicity but rather on the distribution of presented stimuli. By including tuning of inhibitory neurons, the network further captures stimulus-specific adaptation. Finally, we suggest that disinhibition can control the amplification of novelty responses. Therefore, inhibitory plasticity provides a flexible, biologically plausible mechanism to detect the novelty of bottom-up stimuli, enabling us to make experimentally testable predictions.

The Logic of Developing Neocortical Circuits in Health and Disease

The sensory and cognitive abilities of the mammalian neocortex are underpinned by intricate columnar and laminar circuits formed from an array of diverse neuronal populations. One approach to determining how interactions between these circuit components give rise to complex behavior is to investigate the rules by which cortical circuits are formed and acquire functionality during development. This review summarizes recent research on the development of the neocortex, from genetic determination in neural stem cells through to the dynamic role that specific neuronal populations play in the earliest circuits of neocortex, and how they contribute to emergent function and cognition. While many of these endeavors take advantage of model systems, consideration will also be given to advances in our understanding of activity in nascent human circuits. Such cross-species perspective is imperative when investigating the mechanisms underlying the dysfunction of early neocortical circuits in neurodevelopmental disorders, so that one can identify targets amenable to therapeutic intervention.

Relocation of an Extrasynaptic GABAA Receptor to Inhibitory Synapses Freezes Excitatory Synaptic Strength and Preserves Memory

The excitatory synapse between hippocampal CA3 and CA1 pyramidal neurons exhibits long-term potentiation (LTP), a positive feedback process implicated in learning and memory in which postsynaptic depolarization strengthens synapses, promoting further depolarization. Without mechanisms for interrupting positive feedback, excitatory synapses could strengthen inexorably, corrupting memory storage. Here, we reveal a hidden form of inhibitory synaptic plasticity that prevents accumulation of excitatory LTP. We developed a knockin mouse that allows optical control of endogenous α5-subunit-containing γ-aminobutyric acid (GABA)A receptors (α5-GABARs). Induction of excitatory LTP relocates α5-GABARs, which are ordinarily extrasynaptic, to inhibitory synapses, quashing further NMDA receptor activation necessary for inducing more excitatory LTP. Blockade of α5-GABARs accelerates reversal learning, a behavioral test for cognitive flexibility dependent on repeated LTP. Hence, inhibitory synaptic plasticity occurs in parallel with excitatory synaptic plasticity, with the ensuing interruption of the positive feedback cycle of LTP serving as a possible critical early step in preserving memory.

The ins and outs of inhibitory synaptic plasticity: Neuron types, molecular mechanisms and functional roles

GABAergic interneurons are highly diverse, and their synaptic outputs express various forms of plasticity. Compelling evidence indicates that activity-dependent changes of inhibitory synaptic transmission play a significant role in regulating neural circuits critically involved in learning and memory and circuit refinement. Here, we provide an updated overview of inhibitory synaptic plasticity with a focus on the hippocampus and neocortex. To illustrate the diversity of inhibitory interneurons, we discuss the case of two highly divergent interneuron types, parvalbumin-expressing basket cells and neurogliaform cells, which support unique roles on circuit dynamics. We also present recent progress on the molecular mechanisms underlying long-term, activity-dependent plasticity of fast inhibitory transmission. Lastly, we discuss the role of inhibitory synaptic plasticity in neuronal circuits' function. The emerging picture is that inhibitory synaptic transmission in the CNS is extremely diverse, undergoes various mechanistically distinct forms of plasticity and contributes to a much more refined computational role than initially thought. Both the remarkable diversity of inhibitory interneurons and the various forms of plasticity expressed by GABAergic synapses provide an amazingly rich inhibitory repertoire that is central to a variety of complex neural circuit functions, including memory.

Presynaptic GABAA Receptors Modulate Thalamocortical Inputs in Layer 4 of Rat V1

Fast inhibitory GABAergic transmission plays a fundamental role in neural circuits. Current theories of cortical function assume that fast GABAergic inhibition acts via GABAA receptors on postsynaptic neurons, while presynaptic effects of GABA depend on GABAB receptor activation. Manipulations of GABAA receptor activity in vivo produced different effects on cortical function, which were generally ascribed to the mode of action of a drug, more than its site of action. Here we show that in rodent primary visual cortex, α4-containing GABAA receptors can be located on subsets of glutamatergic and GABAergic presynaptic terminals and decrease synaptic transmission. Our data provide a novel mechanistic insight into the effects of changes in cortical inhibition; the ability to modulate inputs onto cortical circuits locally, via presynaptic regulation of release by GABAA receptors.

GABAB receptors: modulation of thalamocortical dynamics and synaptic plasticity

GABA_B-receptors (GABA_B-Rs) are metabotropic, G protein-coupled receptors for the neurotransmitter GABA. Their activation induces slow inhibitory control of the neuronal excitability mediated by pre- and postsynaptic inhibition. Presynaptically GABA_B-Rs reduce GABA and glutamate release inhibiting presynaptic Ca²⁺ channels in both inhibitory and excitatory synapses while postsynaptic GABA_B-Rs induce robust slow hyperpolarization by the activation of K⁺ channels. GABA_B-Rs are activated by non-synaptic or volume transmission, which requires high levels of GABA release, either by the simultaneous discharge of GABAergic interneurons or very intense discharges in the thalamus or by means of the activation of a neurogliaform interneurons in the cortex. The main receptor subunits GABA_B1a, GABA_B1b and GABA_B2 are strongly expressed in neurons and glial cells throughout the central nervous system and GABA_B-R activation is related to many neuronal processes such as the modulation of rhythmic activity in several brain regions. In the thalamus, GABA_B-Rs modulate the generation of the main thalamic rhythm, spindle waves. In the cerebral cortex, GABA_B-Rs also modulate the most prominent emergent oscillatory activity-slow oscillations-as well as faster oscillations like gamma frequency. Further, recent studies evaluating the complexity expressed by the cortical network, a parameter associated with consciousness levels, have found that GABA_B-Rs enhance this complexity, while their blockade decreases it. This review summarizes the current results on how the activation of GABA_B-Rs affects the interchange of information between brain areas by controlling rhythmicity as well as synaptic plasticity.

Inhibitory Plasticity: From Molecules to Computation and Beyond

Synaptic plasticity is the cellular and molecular counterpart of learning and memory and, since its first discovery, the analysis of the mechanisms underlying long-term changes of synaptic strength has been almost exclusively focused on excitatory connections. Conversely, inhibition was considered as a fixed controller of circuit excitability. Only recently, inhibitory networks were shown to be finely regulated by a wide number of mechanisms residing in their synaptic connections. Here, we review recent findings on the forms of inhibitory plasticity (IP) that have been discovered and characterized in different brain areas. In particular, we focus our attention on the molecular pathways involved in the induction and expression mechanisms leading to changes in synaptic efficacy, and we discuss, from the computational perspective, how IP can contribute to the emergence of functional properties of brain circuits.

Bifurcation analysis of the dynamics of interacting subnetworks of a spiking network

We analyze the collective dynamics of hierarchically structured networks of densely connected spiking neurons. These networks of sub-networks may represent interactions between cell assemblies or different nuclei in the brain. The dynamical activity pattern that results from these interactions depends on the strength of synaptic coupling between them. Importantly, the overall dynamics of a brain region in the absence of external input, so called ongoing brain activity, has been attributed to the dynamics of such interactions. In our study, two different network scenarios are considered: a system with one inhibitory and two excitatory subnetworks, and a network representation with three inhibitory subnetworks. To study the effect of synaptic strength on the global dynamics of the network, two parameters for relative couplings between these subnetworks are considered. For each case, a bifurcation analysis is performed and the results have been compared to large-scale network simulations. Our analysis shows that Generalized Lotka-Volterra (GLV) equations, well-known in predator-prey studies, yield a meaningful population-level description for the collective behavior of spiking neuronal interaction, which have a hierarchical structure. In particular, we observed a striking equivalence between the bifurcation diagrams of spiking neuronal networks and their corresponding GLV equations. This study gives new insight on the behavior of neuronal assemblies, and can potentially suggest new mechanisms for altering the dynamical patterns of spiking networks based on changing the synaptic strength between some groups of neurons.

A biophysical model of dynamic balancing of excitation and inhibition in fast oscillatory large-scale networks

Over long timescales, neuronal dynamics can be robust to quite large perturbations, such as changes in white matter connectivity and grey matter structure through processes including learning, aging, development and certain disease processes. One possible explanation is that robust dynamics are facilitated by homeostatic mechanisms that can dynamically rebalance brain networks. In this study, we simulate a cortical brain network using the Wilson-Cowan neural mass model with conduction delays and noise, and use inhibitory synaptic plasticity (ISP) to dynamically achieve a spatially local balance between excitation and inhibition. Using MEG data from 55 subjects we find that ISP enables us to simultaneously achieve high correlation with multiple measures of functional connectivity, including amplitude envelope correlation and phase locking. Further, we find that ISP successfully achieves local E/I balance, and can consistently predict the functional connectivity computed from real MEG data, for a much wider range of model parameters than is possible with a model without ISP.

Hebbian plasticity requires compensatory processes on multiple timescales

We review a body of theoretical and experimental research on Hebbian and homeostatic plasticity, starting from a puzzling observation: while homeostasis of synapses found in experiments is a slow compensatory process, most mathematical models of synaptic plasticity use rapid compensatory processes (RCPs). Even worse, with the slow homeostatic plasticity reported in experiments, simulations of existing plasticity models cannot maintain network stability unless further control mechanisms are implemented. To solve this paradox, we suggest that in addition to slow forms of homeostatic plasticity there are RCPs which stabilize synaptic plasticity on short timescales. These rapid processes may include heterosynaptic depression triggered by episodes of high postsynaptic firing rate. While slower forms of homeostatic plasticity are not sufficient to stabilize Hebbian plasticity, they are important for fine-tuning neural circuits. Taken together we suggest that learning and memory rely on an intricate interplay of diverse plasticity mechanisms on different timescales which jointly ensure stability and plasticity of neural circuits.This article is part of the themed issue 'Integrating Hebbian and homeostatic plasticity'.

Emerging Mechanisms Underlying Dynamics of GABAergic Synapses

Inhibitory circuits are diverse, yet with a poorly understood cell biology. Functional characterization of distinct inhibitory neuron subtypes has not been sufficient to explain how GABAergic neurotransmission sculpts principal cell activity in a relevant fashion. Our Mini-Symposium brings together several emerging mechanisms that modulate GABAergic neurotransmission dynamically from either the presynaptic or the postsynaptic site. The first two talks discuss novel developmental and neuronal subtype-specific contributions to the excitatory/inhibitory balance and circuit maturation. The next three talks examine how interactions between cellular pathways, lateral diffusion of proteins between synapses, and chloride transporter function at excitatory and inhibitory synapses and facilitate inhibitory synapse adaptations. Finally, we address functional differences within GABAergic interneurons to highlight the importance of diverse, flexible, and versatile inputs that shape network function. Together, the selection of topics demonstrates how developmental and activity-dependent mechanisms coordinate inhibition in relation to the excitatory inputs and vice versa.

Detailed Dendritic Excitatory/Inhibitory Balance through Heterosynaptic Spike-Timing-Dependent Plasticity

The balance between excitatory and inhibitory inputs is a key feature of cortical dynamics. Such a balance is arguably preserved in dendritic branches, yet its underlying mechanism and functional roles remain unknown. In this study, we developed computational models of heterosynaptic spike-timing-dependent plasticity (STDP) to show that the excitatory/inhibitory balance in dendritic branches is robustly achieved through heterosynaptic interactions between excitatory and inhibitory synapses. The model reproduces key features of experimental heterosynaptic STDP well, and provides analytical insights. Furthermore, heterosynaptic STDP explains how the maturation of inhibitory neurons modulates the selectivity of excitatory neurons for binocular matching in the critical period plasticity. The model also provides an alternative explanation for the potential mechanism underlying the somatic detailed balance that is commonly associated with inhibitory STDP. Our results propose heterosynaptic STDP as a critical factor in synaptic organization and the resultant dendritic computation.SIGNIFICANCE STATEMENT Recent experimental studies reveal that relative differences in spike timings experienced among neighboring glutamatergic and GABAergic synapses on a dendritic branch significantly influences changes in the efficiency of these synapses. This heterosynaptic form of spike-timing-dependent plasticity (STDP) is potentially important for shaping the synaptic organization and computation of neurons, but its functional role remains elusive. Through computational modeling at the parameter regime where previous experimental results are well reproduced, we show that heterosynaptic plasticity serves to finely balance excitatory and inhibitory inputs on the dendrite. Our results suggest a principle of GABA-driven neural circuit formation.

Versatility and Flexibility of Cortical Circuits

Cortical circuits are known to be plastic and adaptable, as shown by an impressive body of evidence demonstrating the ability of cortical circuits to adapt to changes in environmental stimuli, development, learning, and insults. In this review, we will discuss some of the features of cortical circuits that are thought to facilitate cortical circuit versatility and flexibility. Throughout life, cortical circuits can be extensively shaped and refined by experience while preserving their overall organization, suggesting that mechanisms are in place to favor change but also to stabilize some aspects of the circuit. First, we will describe the basic organization and some of the common features of cortical circuits. We will then discuss how this underlying cortical structure provides a substrate for the experience- and learning-dependent processes that contribute to cortical flexibility.

Defining a critical period for inhibitory circuits within the somatosensory cortex

Although experience-dependent changes in brain inhibitory circuits are thought to play a key role during the “critical period” of brain development, the nature and timing of these changes are poorly understood. We examined the role of sensory experience in sculpting an inhibitory circuit in the primary somatosensory cortex (S1) of mice by using optogenetics to map the connections between parvalbumin (PV) expressing interneurons and layer 2/3 pyramidal cells. Unilateral whisker deprivation decreased the strength and spatial range of inhibitory input provided to pyramidal neurons by PV interneurons in layers 2/3, 4 and 5. By varying the time when sensory input was removed, we determined that the critical period closes around postnatal day 14. This yields the first precise time course of critical period plasticity for an inhibitory circuit.

The space where aging acts: focus on the GABAergic synapse

As it was established that aging is not associated with massive neuronal loss, as was believed in the mid‐20th Century, scientific interest has addressed the influence of aging on particular neuronal subpopulations and their synaptic contacts, which constitute the substrate for neural plasticity. Inhibitory neurons represent the most complex and diverse group of neurons, showing distinct molecular and physiological characteristics and possessing a compelling ability to control the physiology of neural circuits. This review focuses on the aging of GABAergic neurons and synapses. Understanding how aging affects synapses of particular neuronal subpopulations may help explain the heterogeneity of aging‐related effects. We reviewed the literature concerning the effects of aging on the numbers of GABAergic neurons and synapses as well as aging‐related alterations in their presynaptic and postsynaptic components. Finally, we discussed the influence of those changes on the plasticity of the GABAergic system, highlighting our results concerning aging in mouse somatosensory cortex and linking them to plasticity impairments and brain disorders. We posit that aging‐induced impairments of the GABAergic system lead to an inhibitory/excitatory imbalance, thereby decreasing neuron's ability to respond with plastic changes to environmental and cellular challenges, leaving the brain more vulnerable to cognitive decline and damage by synaptopathic diseases.

GABAB receptor-dependent bidirectional regulation of critical period ocular dominance plasticity in cats

Gama amino butyric acid (GABA) inhibition plays an important role in the onset and offset of the critical period for ocular dominance (OD) plasticity in the primary visual cortex. Previous studies have focused on the involvement of GABA_A receptors, while the potential contribution of GABA_B receptors to OD plasticity has been neglected. In this study, the GABA_B receptor antagonist SCH50911 or agonist baclofen was infused into the primary visual cortex of cats concurrently with a period of monocular deprivation (MD). Using single-unit recordings we found that the OD shift induced by four days of MD during the critical period was impaired by infusion of the antagonist SCH50911, but enhanced by infusion of the agonist baclofen. In contrast, seven days of MD in adult cats did not induce any significant OD shift, even when combined with the infusion of SCH50911 or baclofen. Together, these findings indicate that an endogenous GABA_B receptor-mediated inhibition contributes to juvenile, but not adult, OD plasticity.

Key Metabolic Enzymes Underlying Astrocytic Upregulation of GABAergic Plasticity

GABAergic plasticity is recognized as a key mechanism of shaping the activity of the neuronal networks. However, its description is challenging because of numerous neuron-specific mechanisms. In particular, while essential role of glial cells in the excitatory plasticity is well established, their involvement in GABAergic plasticity only starts to emerge. To address this problem, we used two models: neuronal cell culture (NC) and astrocyte-neuronal co-culture (ANCC), where we chemically induced long-term potentiation at inhibitory synapses (iLTP). iLTP could be induced both in NC and ANCC but in ANCC its extent was larger. Importantly, this functional iLTP manifestation was accompanied by an increase in gephyrin puncta size. Furthermore, blocking astrocyte Krebs cycle with fluoroacetate (FA) in ANCC prevented enhancement of both mIPSC amplitude and gephyrin puncta size but this effect was not observed in NC, indicating a key role in neuron-astrocyte cross-talk. Blockade of monocarboxylate transport with α-Cyano-4-hydroxycinnamic acid (4CIN) abolished iLTP both in NC and ANCC and in the latter model prevented also enlargement of gephyrin puncta. Similarly, blockade of glycogen phosphorylase with BAYU6751 prevented enlargement of gephyrin puncta upon iLTP induction. Finally, block of glutamine synthetase with methionine sulfoxide (MSO) nearly abolished mIPSC increase in both NMDA stimulated cell groups but did not prevent enlargement of gephyrin puncta. In conclusion, we provide further evidence that GABAergic plasticity is strongly regulated by astrocytes and the underlying mechanisms involve key metabolic enzymes. Considering the strategic role of GABAergic interneurons, the plasticity described here indicates possible mechanism whereby metabolism regulates the network activity.

Neurophysiology and Regulation of the Balance Between Excitation and Inhibition in Neocortical Circuits

Brain function relies on the ability of neural networks to maintain stable levels of activity, while experiences sculpt them. In the neocortex, the balance between activity and stability relies on the coregulation of excitatory and inhibitory inputs onto principal neurons. Shifts of excitation or inhibition result in altered excitability impaired processing of incoming information. In many neurodevelopmental and neuropsychiatric disorders, the excitability of local circuits is altered, suggesting that their pathophysiology may involve shifts in synaptic excitation, inhibition, or both. Most studies focused on identifying the cellular and molecular mechanisms controlling network excitability to assess whether they may be altered in animal models of disease. The impact of changes in excitation/inhibition balance on local circuit and network computations is not clear. Here we report findings on the integration of excitatory and inhibitory inputs in healthy cortical circuits and discuss how shifts in excitation/inhibition balance may relate to pathological phenotypes.

Interactions between synaptic homeostatic mechanisms: an attempt to reconcile BCM theory, synaptic scaling, and changing excitation/inhibition balance

Homeostatic plasticity is proposed to be mediated by synaptic changes, such as synaptic scaling and shifts in the excitation/inhibition balance. These mechanisms are thought to be separate from the Bienenstock, Cooper, Munro (BCM) learning rule, where the threshold for the induction of long-term potentiation and long-term depression slides in response to changes in activity levels. Yet, both sets of mechanisms produce a homeostatic response of a relative increase (or decrease) in strength of excitatory synapses in response to overall activity-level changes. Here we review recent studies, with a focus on in vivo experiments, to re-examine the overlap and differences between these two mechanisms and we suggest how they may interact to facilitate firing-rate homeostasis, while maintaining functional properties of neurons.

Fifty shades of inhibition

Inhibitory circuits are essential for brain function. Our understanding of their synaptic organization has advanced extensively with the identification and classification of an impressive variety of neuron groups, receptor types, and patterns of connectivity. However, the conceptual discussion regarding the role of in neural circuits still revolves around the idea that its primary role is to regulate circuit excitability. Here, I will focus on recent findings from cortical circuits and argue that inhibitory circuits are central to the integration of incoming inputs and can promote sophisticated fine-scale control of local circuits. I propose that inhibitory circuits should not be viewed so much as brakes on principal neurons activity, but as primary contributors to a variety of neural network functions.

Releasing the Cortical Brake by Non-Invasive Electromagnetic Stimulation? rTMS Induces LTD of GABAergic Neurotransmission

Repetitive Transcranial Magnetic Stimulation (rTMS) is a non-invasive brain stimulation technique which modulates cortical excitability beyond the stimulation period. However, despite its clinical use rTMS-based therapies which prevent or reduce disabilities in a functionally significant and sustained manner are scarce. It remains unclear how rTMS-mediated changes in cortical excitability, which are not task- or input-specific, exert beneficial effects in some healthy subjects and patients. While experimental evidence exists that repetitive magnetic stimulation (rMS) is linked to the induction of long-term potentiation (LTP) of excitatory neurotransmission, less attention has been dedicated to rTMS-induced structural, functional and molecular adaptations at inhibitory synapses. In this review article we provide a concise overview on basic neuroscience research, which reveals an important role of local disinhibitory networks in promoting associative learning and memory. These studies suggest that a reduction in inhibitory neurotransmission facilitates the expression of associative plasticity in cortical networks under physiological conditions. Hence, it is interesting to speculate that rTMS may act by decreasing GABAergic neurotransmission onto cortical principal neurons. Indeed, evidence has been provided that rTMS is capable of modulating inhibitory networks. Consistent with this suggestion recent basic science work discloses that a 10 Hz rTMS protocol reduces GABAergic synaptic strength on principal neurons. These findings support a model in which rTMS-induced long-term depression (LTD) of GABAergic synaptic strength mediates changes in excitation/inhibition-balance of cortical networks, which may in turn facilitate (or restore) the ability of stimulated networks to express input- and task-specific associative synaptic plasticity.

GABAB receptor-mediated, layer-specific synaptic plasticity reorganizes gamma-frequency neocortical response to stimulation

Repeated presentations of sensory stimuli generate transient gamma-frequency (30-80 Hz) responses in neocortex that show plasticity in a task-dependent manner. Complex relationships between individual neuronal outputs and the mean, local field potential (population activity) accompany these changes, but little is known about the underlying mechanisms responsible. Here we show that transient stimulation of input layer 4 sufficient to generate gamma oscillations induced two different, lamina-specific plastic processes that correlated with lamina-specific changes in responses to further, repeated stimulation: Unit rates and recruitment showed overall enhancement in supragranular layers and suppression in infragranular layers associated with excitatory or inhibitory synaptic potentiation onto principal cells, respectively. Both synaptic processes were critically dependent on activation of GABAB receptors and, together, appeared to temporally segregate the cortical representation. These data suggest that adaptation to repetitive sensory input dramatically alters the spatiotemporal properties of the neocortical response in a manner that may both refine and minimize cortical output simultaneously.

Role of GABAA-Mediated Inhibition and Functional Assortment of Synapses onto Individual Layer 4 Neurons in Regulating Plasticity Expression in Visual Cortex

Layer 4 (L4) of primary visual cortex (V1) is the main recipient of thalamocortical fibers from the dorsal lateral geniculate nucleus (LGNd). Thus, it is considered the main entry point of visual information into the neocortex and the first anatomical opportunity for intracortical visual processing before information leaves L4 and reaches supra- and infragranular cortical layers. The strength of monosynaptic connections from individual L4 excitatory cells onto adjacent L4 cells (unitary connections) is highly malleable, demonstrating that the initial stage of intracortical synaptic transmission of thalamocortical information can be altered by previous activity. However, the inhibitory network within L4 of V1 may act as an internal gate for induction of excitatory synaptic plasticity, thus providing either high fidelity throughput to supragranular layers or transmittal of a modified signal subject to recent activity-dependent plasticity. To evaluate this possibility, we compared the induction of synaptic plasticity using classical extracellular stimulation protocols that recruit a combination of excitatory and inhibitory synapses with stimulation of a single excitatory neuron onto a L4 cell. In order to induce plasticity, we paired pre- and postsynaptic activity (with the onset of postsynaptic spiking leading the presynaptic activation by 10ms) using extracellular stimulation (ECS) in acute slices of primary visual cortex and comparing the outcomes with our previously published results in which an identical protocol was used to induce synaptic plasticity between individual pre- and postsynaptic L4 excitatory neurons. Our results indicate that pairing of ECS with spiking in a L4 neuron fails to induce plasticity in L4-L4 connections if synaptic inhibition is intact. However, application of a similar pairing protocol under GABAARs inhibition by bath application of 2μM bicuculline does induce robust synaptic plasticity, long term potentiation (LTP) or long term depression (LTD), similar to our results with pairing of pre- and postsynaptic activation between individual excitatory L4 neurons in which inhibitory connections are not activated. These results are consistent with the well-established observation that inhibition limits the capacity for induction of plasticity at excitatory synapses and that pre- and postsynaptic activation at a fixed time interval can result in a variable range of plasticity outcomes. However, in the current study by virtue of having two sets of experimental data, we have provided a new insight into these processes. By randomly mixing the assorting of individual L4 neurons according to the frequency distribution of the experimentally determined plasticity outcome distribution based on the calculated convergence of multiple individual L4 neurons onto a single postsynaptic L4 neuron, we were able to compare then actual ECS plasticity outcomes to those predicted by randomly mixing individual pairs of neurons. Interestingly, the observed plasticity profiles with ECS cannot account for the random assortment of plasticity behaviors of synaptic connections between individual cell pairs. These results suggest that connections impinging onto a single postsynaptic cell may be grouped according to plasticity states.

A Simple Network Architecture Accounts for Diverse Reward Time Responses in Primary Visual Cortex

Many actions performed by animals and humans depend on an ability to learn, estimate, and produce temporal intervals of behavioral relevance. Exemplifying such learning of cued expectancies is the observation of reward-timing activity in the primary visual cortex (V1) of rodents, wherein neural responses to visual cues come to predict the time of future reward as behaviorally experienced in the past. These reward-timing responses exhibit significant heterogeneity in at least three qualitatively distinct classes: sustained increase or sustained decrease in firing rate until the time of expected reward, and a class of cells that reach a peak in firing at the expected delay. We elaborate upon our existing model by including inhibitory and excitatory units while imposing simple connectivity rules to demonstrate what role these inhibitory elements and the simple architectures play in sculpting the response dynamics of the network. We find that simply adding inhibition is not sufficient for obtaining the different distinct response classes, and that a broad distribution of inhibitory projections is necessary for obtaining peak-type responses. Furthermore, although changes in connection strength that modulate the effects of inhibition onto excitatory units have a strong impact on the firing rate profile of these peaked responses, the network exhibits robustness in its overall ability to predict the expected time of reward. Finally, we demonstrate how the magnitude of expected reward can be encoded at the expected delay in the network and how peaked responses express this reward expectancy.

SIGNIFICANCE STATEMENT

Heterogeneity in single-neuron responses is a common feature of neuronal systems, although sometimes, in theoretical approaches, it is treated as a nuisance and seldom considered as conveying a different aspect of a signal. In this study, we focus on the heterogeneous responses in the primary visual cortex of rodents trained with a predictable delayed reward time. We describe under what conditions this heterogeneity can arise by self-organization, and what information it can convey. This study, while focusing on a specific system, provides insight onto how heterogeneity can arise in general while also shedding light onto mechanisms of reinforcement learning using realistic biological assumptions.

Roles of Presynaptic NMDA Receptors in Neurotransmission and Plasticity

Presynaptic NMDA receptors (preNMDARs) play pivotal roles in excitatory neurotransmission and synaptic plasticity. They facilitate presynaptic neurotransmitter release and modulate mechanisms controlling synaptic maturation and plasticity during formative periods of brain development. There is an increasing understanding of the roles of preNMDARs in experience-dependent synaptic and circuit-specific computation. In this review we summarize the latest understanding of compartment-specific expression and function of preNMDARs, and how they contribute to synapse-specific and circuit-level information processing.