Specialized inhibitory synaptic actions between nearby neocortical pyramidal neurons

Axo-axonic synapses: Diversity in neural circuit function

The chemical synapse is the principal form of contact between neurons of the central nervous system. These synapses are typically configured as presynaptic axon terminations onto postsynaptic dendrites or somata, giving rise to axo-dendritic and axo-somatic synapses, respectively. Beyond these common synapse configurations are less-studied, non-canonical synapse types that are prevalent throughout the brain and significantly contribute to neural circuit function. Among these are the axo-axonic synapses, which consist of an axon terminating on another axon or axon terminal. Here, we review evidence for axo-axonic synapse contributions to neural signaling in the mammalian nervous system and survey functional neural circuit motifs enabled by these synapses. We also detail how recent advances in microscopy, transgenics, and biological sensors may be used to identify and functionally assay axo-axonic synapses.

Dynamics of a Mutual Inhibition Circuit between Pyramidal Neurons Compared to Human Perceptual Competition

Neural competition plays an essential role in active selection processes of noisy and ambiguous input signals, and it is assumed to underlie emergent properties of brain functioning, such as perceptual organization and decision-making. Despite ample theoretical research on neural competition, experimental tools to allow neurophysiological investigation of competing neurons have not been available. We developed a "hybrid" system where real-life neurons and a computer-simulated neural circuit interacted. It enabled us to construct a mutual inhibition circuit between two real-life pyramidal neurons. We then asked what dynamics this minimal unit of neural competition exhibits and compared them with the known behavioral-level dynamics of neural competition. We found that the pair of neurons shows bistability when activated simultaneously by current injections. The addition of modeled synaptic noise and changes in the activation strength showed that the dynamics of the circuit are strikingly similar to the known properties of bistable visual perception: The distribution of dominance durations showed a right-skewed shape, and the changes of the activation strengths caused changes in dominance, dominance durations, and reversal rates as stated in the well-known empirical laws of bistable perception known as Levelt's propositions.SIGNIFICANCE STATEMENT Visual perception emerges as the result of neural systems actively organizing visual signals that involves selection processes of competing neurons. While the neural competition, realized by a "mutual inhibition" circuit has been examined in many theoretical studies, its properties have not been investigated in real neurons. We have developed a "hybrid" system where two real-life pyramidal neurons in a mouse brain slice interact through a computer-simulated mutual inhibition circuit. We found that simultaneous activation of the neurons leads to bistable activity. We investigated the effect of noise and the effect of changes in the activation strength on the dynamics. We observed that the pair of neurons exhibit dynamics strikingly similar to the known properties of bistable visual perception.

Cytoskeletal and synaptic polarity of LWamide-like+ ganglion neurons in the sea anemone Nematostella vectensis

The centralized nervous systems of bilaterian animals rely on directional signaling facilitated by polarized neurons with specialized axons and dendrites. It is not known whether axo-dendritic polarity is exclusive to bilaterians or was already present in early metazoans. We therefore examined neurite polarity in the starlet sea anemone Nematostella vectensis (Cnidaria). Cnidarians form a sister clade to bilaterians and share many neuronal building blocks characteristic of bilaterians, including channels, receptors and synaptic proteins, but their nervous systems comprise a comparatively simple net distributed throughout the body. We developed a tool kit of fluorescent polarity markers for live imaging analysis of polarity in an identified neuron type, large ganglion cells of the body column nerve net that express the LWamide-like neuropeptide. Microtubule polarity differs in bilaterian axons and dendrites, and this in part underlies polarized distribution of cargo to the two types of processes. However, in LWamide-like+ neurons, all neurites had axon-like microtubule polarity suggesting that they may have similar contents. Indeed, presynaptic and postsynaptic markers trafficked to all neurites and accumulated at varicosities where neurites from different neurons often crossed, suggesting the presence of bidirectional synaptic contacts. Furthermore, we could not identify a diffusion barrier in the plasma membrane of any of the neurites like the axon initial segment barrier that separates the axonal and somatodendritic compartments in bilaterian neurons. We conclude that at least one type of neuron in Nematostella vectensis lacks the axo-dendritic polarity characteristic of bilaterian neurons.

Design Principles for Central Pattern Generators With Preset Rhythms

This article is concerned with the design of synthetic central pattern generators (CPGs). Biological CPGs are neural circuits that determine a variety of rhythmic activities, including locomotion, in animals. A synthetic CPG is a network of dynamical elements (here called cells) properly coupled by various synapses to emulate rhythms produced by a biological CPG. We focus on CPGs for locomotion of quadrupeds and present our design approach, based on the principles of nonlinear dynamics, bifurcation theory, and parameter optimization. This approach lets us design the synthetic CPG with a set of desired rhythms and switch between them as the parameter representing the control actions from the brain is varied. The developed four-cell CPG can produce four distinct gaits: walk, trot, gallop, and bound, similar to the mouse locomotion. The robustness and adaptability of the network design principles are verified using different cell and synapse models.

An Indexing Theory for Working Memory Based on Fast Hebbian Plasticity

Working memory (WM) is a key component of human memory and cognition. Computational models have been used to study the underlying neural mechanisms, but neglected the important role of short-term memory (STM) and long-term memory (LTM) interactions for WM. Here, we investigate these using a novel multiarea spiking neural network model of prefrontal cortex (PFC) and two parietotemporal cortical areas based on macaque data. We propose a WM indexing theory that explains how PFC could associate, maintain, and update multimodal LTM representations. Our simulations demonstrate how simultaneous, brief multimodal memory cues could build a temporary joint memory representation as an “index” in PFC by means of fast Hebbian synaptic plasticity. This index can then reactivate spontaneously and thereby also the associated LTM representations. Cueing one LTM item rapidly pattern completes the associated uncued item via PFC. The PFC–STM network updates flexibly as new stimuli arrive, thereby gradually overwriting older representations.

Laminar Distribution of Neurochemically-Identified Interneurons and Cellular Co-expression of Molecular Markers in Epileptic Human Cortex

Inhibitory GABAergic interneurons are fundamental elements of cortical circuits and play critical roles in shaping network activity. Dysfunction of interneurons can lead to various brain disorders, including epilepsy, schizophrenia, and anxiety. Based on the electrophysiological properties, cell morphology, and molecular identity, interneurons could be classified into various subgroups. In this study, we investigated the density and laminar distribution of different interneuron types and the co-expression of molecular markers in epileptic human cortex. We found that parvalbumin (PV) and somatostatin (SST) neurons were distributed in all cortical layers except layer I, while tyrosine hydroxylase (TH) and neuropeptide Y (NPY) were abundant in the deep layers and white matter. Cholecystokinin (CCK) neurons showed a high density in layers IV and VI. Neurons with these markers constituted ~7.2% (PV), 2.6% (SST), 0.5% (TH), 0.5% (NPY), and 4.4% (CCK) of the gray-matter neuron population. Double- and triple-labeling revealed that NPY neurons were also SST-immunoreactive (97.7%), and TH neurons were more likely to express SST (34.2%) than PV (14.6%). A subpopulation of CCK neurons (28.0%) also expressed PV, but none contained SST. Together, these results revealed the density and distribution patterns of different interneuron populations and the overlap between molecular markers in epileptic human cortex.

A Spiking Working Memory Model Based on Hebbian Short-Term Potentiation

A dominant theory of working memory (WM), referred to as the persistent activity hypothesis, holds that recurrently connected neural networks, presumably located in the prefrontal cortex, encode and maintain WM memory items through sustained elevated activity. Reexamination of experimental data has shown that prefrontal cortex activity in single units during delay periods is much more variable than predicted by such a theory and associated computational models. Alternative models of WM maintenance based on synaptic plasticity, such as short-term nonassociative (non-Hebbian) synaptic facilitation, have been suggested but cannot account for encoding of novel associations. Here we test the hypothesis that a recently identified fast-expressing form of Hebbian synaptic plasticity (associative short-term potentiation) is a possible mechanism for WM encoding and maintenance. Our simulations using a spiking neural network model of cortex reproduce a range of cognitive memory effects in the classical multi-item WM task of encoding and immediate free recall of word lists. Memory reactivation in the model occurs in discrete oscillatory bursts rather than as sustained activity. We relate dynamic network activity as well as key synaptic characteristics to electrophysiological measurements. Our findings support the hypothesis that fast Hebbian short-term potentiation is a key WM mechanism.

SIGNIFICANCE STATEMENT Working memory (WM) is a key component of cognition. Hypotheses about the neural mechanism behind WM are currently under revision. Reflecting recent findings of fast Hebbian synaptic plasticity in cortex, we test whether a cortical spiking neural network model with such a mechanism can learn a multi-item WM task (word list learning). We show that our model can reproduce human cognitive phenomena and achieve comparable memory performance in both free and cued recall while being simultaneously compatible with experimental data on structure, connectivity, and neurophysiology of the underlying cortical tissue. These findings are directly relevant to the ongoing paradigm shift in the WM field.

Functional Organization of Flash-Induced V1 Offline Reactivation

The primary visual cortex exhibits a late, long response with a latency of >300 ms and an immediate early response that occurs ∼100 ms after a visual stimulus. The late response is thought to contribute to visual functions such as sensory perception, iconic memory, working memory, and forming connections between temporally separated stimuli. However, how the visual late response is generated and organized is not completely understood. In the mouse primary visual cortex in vivo, we isolated long-delayed responses by using a brief light-flash stimulus for which the stimulus late response occurred long after the stimulus offset and was not contaminated by the instantaneous response evoked by the stimulus. Using whole-cell patch-clamp recordings, we demonstrated that the late rebound response was shaped by a net-balanced increase in excitatory and inhibitory synaptic conductances, whereas transient imbalances were caused by intermittent inhibitory barrage. In contrast to the common assumption that the neocortical late response reflects a feedback signal from the downstream higher-order cortical areas, our pharmacological and optogenetic analyses demonstrated that the late responses likely have a thalamic origin. Therefore, the late component of a sensory-evoked cortical response should be interpreted with caution.

SIGNIFICANCE STATEMENT

The long-delayed responses of neocortical neurons are thought to arise from cortical feedback activity that is related to sensory perception and cognition. The mechanism of neocortical late responses was investigated using multiple electrophysiological techniques and the findings indicate that it actually arises from the thalamus. In addition, during the late response, excitation and inhibition are balanced, but inhibition is dominant in patterning action potentials.

Familiarization: A theory of repetition suppression predicts interference between overlapping cortical representations

Repetition suppression refers to a reduction in the cortical response to a novel stimulus that results from repeated presentation of the stimulus. We demonstrate repetition suppression in a well established computational model of cortical plasticity, according to which the relative strengths of lateral inhibitory interactions are modified by Hebbian learning. We present the model as an extension to the traditional account of repetition suppression offered by sharpening theory, which emphasises the contribution of afferent plasticity, by instead attributing the effect primarily to plasticity of intra-cortical circuitry. In support, repetition suppression is shown to emerge in simulations with plasticity enabled only in intra-cortical connections. We show in simulation how an extended 'inhibitory sharpening theory' can explain the disruption of repetition suppression reported in studies that include an intermediate phase of exposure to additional novel stimuli composed of features similar to those of the original stimulus. The model suggests a re-interpretation of repetition suppression as a manifestation of the process by which an initially distributed representation of a novel object becomes a more localist representation. Thus, inhibitory sharpening may constitute a more general process by which representation emerges from cortical re-organisation.

Multisensory Integration Uses a Real-Time Unisensory-Multisensory Transform

The manner in which the brain integrates different sensory inputs to facilitate perception and behavior has been the subject of numerous speculations. By examining multisensory neurons in cat superior colliculus, the present study demonstrated that two operational principles are sufficient to understand how this remarkable result is achieved: (1) unisensory signals are integrated continuously and in real time as soon as they arrive at their common target neuron and (2) the resultant multisensory computation is modified in shape and timing by a delayed, calibrating inhibition. These principles were tested for descriptive sufficiency by embedding them in a neurocomputational model and using it to predict a neuron's moment-by-moment multisensory response given only knowledge of its responses to the individual modality-specific component cues. The predictions proved to be highly accurate, reliable, and unbiased and were, in most cases, not statistically distinguishable from the neuron's actual instantaneous multisensory response at any phase throughout its entire duration. The model was also able to explain why different multisensory products are often observed in different neurons at different time points, as well as the higher-order properties of multisensory integration, such as the dependency of multisensory products on the temporal alignment of crossmodal cues. These observations not only reveal this fundamental integrative operation, but also identify quantitatively the multisensory transform used by each neuron. As a result, they provide a means of comparing the integrative profiles among neurons and evaluating how they are affected by changes in intrinsic or extrinsic factors.SIGNIFICANCE STATEMENT Multisensory integration is the process by which the brain combines information from multiple sensory sources (e.g., vision and audition) to maximize an organism's ability to identify and respond to environmental stimuli. The actual transformative process by which the neural products of multisensory integration are achieved is poorly understood. By focusing on the millisecond-by-millisecond differences between a neuron's unisensory component responses and its integrated multisensory response, it was found that this multisensory transform can be described by two basic principles: unisensory information is integrated in real time and the multisensory response is shaped by calibrating inhibition. It is now possible to use these principles to predict a neuron's multisensory response accurately armed only with knowledge of its unisensory responses.

Ionotropic glutamate receptors: Which ones, when, and where in the mammalian neocortex

A multitude of 18 iGluR receptor subunits, many of which are diversified by splicing and RNA editing, localize to >20 excitatory and inhibitory neocortical neuron types defined by physiology, morphology, and transcriptome in addition to various types of glial, endothelial, and blood cells. Here we have compiled the published expression of iGluR subunits in the areas and cell types of developing and adult cortex of rat, mouse, carnivore, bovine, monkey, and human as determined with antibody- and mRNA-based techniques. iGluRs are differentially expressed in the cortical areas and in the species, and all have a unique developmental pattern. Differences are quantitative rather than a mere absence/presence of expression. iGluR are too ubiquitously expressed and of limited use as markers for areas or layers. A focus has been the iGluR profile of cortical interneuron types. For instance, GluK1 and GluN3A are enriched in, but not specific for, interneurons; moreover, the interneurons expressing these subunits belong to different types. Adressing the types is still a major hurdle because type-specific markers are lacking, and the frequently used neuropeptide/CaBP signatures are subject to regulation by age and activity and vary as well between species and areas. RNA-seq reveals almost all subunits in the two morphofunctionally characterized interneuron types of adult cortical layer I, suggesting a fairly broad expression at the RNA level. It remains to be determined whether all proteins are synthesized, to which pre- or postsynaptic subdomains in a given neuron type they localize, and whether all are involved in synaptic transmission. J. Comp. Neurol. 525:976-1033, 2017. © 2016 Wiley Periodicals, Inc.

Large-scale functional network overlap is a general property of brain functional organization: Reconciling inconsistent fMRI findings from general-linear-model-based analyses

Functional magnetic resonance imaging (fMRI) studies regularly use univariate general-linear-model-based analyses (GLM). Their findings are often inconsistent across different studies, perhaps because of several fundamental brain properties including functional heterogeneity, balanced excitation and inhibition (E/I), and sparseness of neuronal activities. These properties stipulate heterogeneous neuronal activities in the same voxels and likely limit the sensitivity and specificity of GLM. This paper selectively reviews findings of histological and electrophysiological studies and fMRI spatial independent component analysis (sICA) and reports new findings by applying sICA to two existing datasets. The extant and new findings consistently demonstrate several novel features of brain functional organization not revealed by GLM. They include overlap of large-scale functional networks (FNs) and their concurrent opposite modulations, and no significant modulations in activity of most FNs across the whole brain during any task conditions. These novel features of brain functional organization are highly consistent with the brain's properties of functional heterogeneity, balanced E/I, and sparseness of neuronal activity, and may help reconcile inconsistent GLM findings.

Functional Relevance of Different Basal Ganglia Pathways Investigated in a Spiking Model with Reward Dependent Plasticity

The brain enables animals to behaviorally adapt in order to survive in a complex and dynamic environment, but how reward-oriented behaviors are achieved and computed by its underlying neural circuitry is an open question. To address this concern, we have developed a spiking model of the basal ganglia (BG) that learns to dis-inhibit the action leading to a reward despite ongoing changes in the reward schedule. The architecture of the network features the two pathways commonly described in BG, the direct (denoted D1) and the indirect (denoted D2) pathway, as well as a loop involving striatum and the dopaminergic system. The activity of these dopaminergic neurons conveys the reward prediction error (RPE), which determines the magnitude of synaptic plasticity within the different pathways. All plastic connections implement a versatile four-factor learning rule derived from Bayesian inference that depends upon pre- and post-synaptic activity, receptor type, and dopamine level. Synaptic weight updates occur in the D1 or D2 pathways depending on the sign of the RPE, and an efference copy informs upstream nuclei about the action selected. We demonstrate successful performance of the system in a multiple-choice learning task with a transiently changing reward schedule. We simulate lesioning of the various pathways and show that a condition without the D2 pathway fares worse than one without D1. Additionally, we simulate the degeneration observed in Parkinson's disease (PD) by decreasing the number of dopaminergic neurons during learning. The results suggest that the D1 pathway impairment in PD might have been overlooked. Furthermore, an analysis of the alterations in the synaptic weights shows that using the absolute reward value instead of the RPE leads to a larger change in D1.

Efficient codes and balanced networks

Recent years have seen a growing interest in inhibitory interneurons and their circuits. A striking property of cortical inhibition is how tightly it balances excitation. Inhibitory currents not only match excitatory currents on average, but track them on a millisecond time scale, whether they are caused by external stimuli or spontaneous fluctuations. We review, together with experimental evidence, recent theoretical approaches that investigate the advantages of such tight balance for coding and computation. These studies suggest a possible revision of the dominant view that neurons represent information with firing rates corrupted by Poisson noise. Instead, tight excitatory/inhibitory balance may be a signature of a highly cooperative code, orders of magnitude more precise than a Poisson rate code. Moreover, tight balance may provide a template that allows cortical neurons to construct high-dimensional population codes and learn complex functions of their inputs.

Spike-Based Bayesian-Hebbian Learning of Temporal Sequences

Many cognitive and motor functions are enabled by the temporal representation and processing of stimuli, but it remains an open issue how neocortical microcircuits can reliably encode and replay such sequences of information. To better understand this, a modular attractor memory network is proposed in which meta-stable sequential attractor transitions are learned through changes to synaptic weights and intrinsic excitabilities via the spike-based Bayesian Confidence Propagation Neural Network (BCPNN) learning rule. We find that the formation of distributed memories, embodied by increased periods of firing in pools of excitatory neurons, together with asymmetrical associations between these distinct network states, can be acquired through plasticity. The model’s feasibility is demonstrated using simulations of adaptive exponential integrate-and-fire model neurons (AdEx). We show that the learning and speed of sequence replay depends on a confluence of biophysically relevant parameters including stimulus duration, level of background noise, ratio of synaptic currents, and strengths of short-term depression and adaptation. Moreover, sequence elements are shown to flexibly participate multiple times in the sequence, suggesting that spiking attractor networks of this type can support an efficient combinatorial code. The model provides a principled approach towards understanding how multiple interacting plasticity mechanisms can coordinate hetero-associative learning in unison.

From one moment to the next, in an ever-changing world, and awash in a deluge of sensory data, the brain fluidly guides our actions throughout an astonishing variety of tasks. Processing this ongoing bombardment of information is a fundamental problem faced by its underlying neural circuits. Given that the structure of our actions along with the organization of the environment in which they are performed can be intuitively decomposed into sequences of simpler patterns, an encoding strategy reflecting the temporal nature of these patterns should offer an efficient approach for assembling more complex memories and behaviors. We present a model that demonstrates how activity could propagate through recurrent cortical microcircuits as a result of a learning rule based on neurobiologically plausible time courses and dynamics. The model predicts that the interaction between several learning and dynamical processes constitute a compound mnemonic engram that can flexibly generate sequential step-wise increases of activity within neural populations.

Chimera states in bursting neurons

We study the existence of chimera states in pulse-coupled networks of bursting Hindmarsh-Rose neurons with nonlocal, global, and local (nearest neighbor) couplings. Through a linear stability analysis, we discuss the behavior of the stability function in the incoherent (i.e., disorder), coherent, chimera, and multichimera states. Surprisingly, we find that chimera and multichimera states occur even using local nearest neighbor interaction in a network of identical bursting neurons alone. This is in contrast with the existence of chimera states in populations of nonlocally or globally coupled oscillators. A chemical synaptic coupling function is used which plays a key role in the emergence of chimera states in bursting neurons. The existence of chimera, multichimera, coherent, and disordered states is confirmed by means of the recently introduced statistical measures and mean phase velocity.

Neuronal polarity: an evolutionary perspective

Polarized distribution of signaling molecules to axons and dendrites facilitates directional information flow in complex vertebrate nervous systems. The topic we address here is when the key aspects of neuronal polarity evolved. All neurons have a central cell body with thin processes that extend from it to cover long distances, and they also all rely on voltage-gated ion channels to propagate signals along their length. The most familiar neurons, those in vertebrates, have additional cellular features that allow them to send directional signals efficiently. In these neurons, dendrites typically receive signals and axons send signals. It has been suggested that many of the distinct features of axons and dendrites, including the axon initial segment, are found only in vertebrates. However, it is now becoming clear that two key cytoskeletal features that underlie polarized sorting, a specialized region at the base of the axon and polarized microtubules, are found in invertebrate neurons as well. It thus seems likely that all bilaterians generate axons and dendrites in the same way. As a next step, it will be extremely interesting to determine whether the nerve nets of cnidarians and ctenophores also contain polarized neurons with true axons and dendrites, or whether polarity evolved in concert with the more centralized nervous systems found in bilaterians.

Implications of cortical balanced excitation and inhibition, functional heterogeneity, and sparseness of neuronal activity in fMRI

Blood-oxygenation-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) studies often report inconsistent findings, probably due to brain properties such as balanced excitation and inhibition and functional heterogeneity. These properties indicate that different neurons in the same voxels may show variable activities including concurrent activation and deactivation, that the relationships between BOLD signal and neural activity (i.e., neurovascular coupling) are complex, and that increased BOLD signal may reflect reduced deactivation, increased activation, or both. The traditional general-linear-model-based-analysis (GLM-BA) is a univariate approach, cannot separate different components of BOLD signal mixtures from the same voxels, and may contribute to inconsistent findings of fMRI. Spatial independent component analysis (sICA) is a multivariate approach, can separate the BOLD signal mixture from each voxel into different source signals and measure each separately, and thus may reconcile previous conflicting findings generated by GLM-BA. We propose that methods capable of separating mixed signals such as sICA should be regularly used for more accurately and completely extracting information embedded in fMRI datasets.

Anatomy and physiology of the thick-tufted layer 5 pyramidal neuron

The thick-tufted layer 5 (TTL5) pyramidal neuron is one of the most extensively studied neuron types in the mammalian neocortex and has become a benchmark for understanding information processing in excitatory neurons. By virtue of having the widest local axonal and dendritic arborization, the TTL5 neuron encompasses various local neocortical neurons and thereby defines the dimensions of neocortical microcircuitry. The TTL5 neuron integrates input across all neocortical layers and is the principal output pathway funneling information flow to subcortical structures. Several studies over the past decades have investigated the anatomy, physiology, synaptology, and pathophysiology of the TTL5 neuron. This review summarizes key discoveries and identifies potential avenues of research to facilitate an integrated and unifying understanding on the role of a central neuron in the neocortex.

Synergistic effect of repulsive inhibition in synchronization of excitatory networks

We show that the addition of pairwise repulsive inhibition to excitatory networks of bursting neurons induces synchrony, in contrast to one's expectations. Through stability analysis, we reveal the mechanism underlying this purely synergistic phenomenon and demonstrate that it originates from the transition between different types of bursting, caused by excitatory-inhibitory synaptic coupling. This effect is generic and observed in different models of bursting neurons and fast synaptic interactions. We also find a universal scaling law for the synchronization stability condition for large networks in terms of the number of excitatory and inhibitory inputs each neuron receives, regardless of the network size and topology. This general law is in sharp contrast with linearly coupled networks with positive (attractive) and negative (repulsive) coupling where the placement and structure of negative connections heavily affect synchronization.

Reduction in Ventral Midbrain NMDA Receptors Reveals Two Opposite Modulatory Roles for Glutamate on Reward

Glutamate is a major component of the reward circuitry and recent clinical studies suggest that new molecules that would target glutamate neurotransmission are most likely to constitute more effective medications for mood disorders. It is well known that activation of N-methyl-D-aspartate glutamate receptors (NMDARs) initiates dopamine burst firing, a mode associated with reward signaling; but NMDARs also contribute to the maintenance of an inhibitory drive to dopamine neurons. Such opposite modulatory functions imply that different subtypes of NMDARs are expressed on different ventral midbrain (VM) neurons and/or afferent inputs to dopamine neurons. By using the small interfering RNA (siRNA) technique, we studied the effects of VM downregulation of NMDAR subunits GluN1, GluN2A, and GluN2D on reward induced by dorsal raphe electrical stimulation. Reward thresholds were measured before and 24 h after each of three consecutive daily bilateral microinjections of siRNA for the targeted receptor subunit(s) or non-active RNA sequence. After the last measurement, reward thresholds were reassessed following a bilateral microinjection of the preferred GluN2A-NMDA antagonist, (2R,4S)-4-(3-Phosphopropyl)-2-piperidinecarboxylic acid (PPPA). Western-blot analysis showed that siRNAs reduced GluN1- and GluN2A-containing receptors whereas behavioral tests showed that only a reduction in GluN1 produced reward attenuation. Despite NMDAR reduction, reward-enhancing effect of PPPA remained unchanged. We conclude that VM glutamate relays the reward signal initiated by dorsal raphe electrical stimulation by acting on NMDARs devoid of GluN2A/2D subunits and exerts an inhibition on this reward signal by acting on GluN2A-containing NMDARs most likely located on afferent terminals.

What, if anything, are topological maps for?

What, if anything, is the functional significance of spatial patterning in cortical feature maps? We ask this question of four major theories of cortical map formation: self-organizing maps, wiring optimization, place coding, and reaction-diffusion. We argue that (i) self-organizing maps yield spatial patterning only as a by-product of efficient mechanisms for developing environmentally appropriate distributions of feature preferences, (ii) wiring optimization assumes rather than explains a map-like organization, (iii) place-coding mechanisms can at best explain only a subset of maps in functional terms, and (iv) reaction-diffusion models suggest two factors in the evolution of maps, the first based on efficient development of feature distributions, and the second based on generating feature-specific long-range recurrent cortical circuitry. None of these explanations for the existence of topological maps requires spatial patterning in maps to be useful. Thus despite these useful frameworks for understanding how maps form and how they are wired, the possibility that patterns are merely epiphenomena in the evolution of mammalian neocortex cannot be rejected. The article is intended as a nontechnical introduction to the assumptions and predictions of these four important classes of models, along with other possible functional explanations for maps.

A biologically plausible learning rule for the Infomax on recurrent neural networks

A fundamental issue in neuroscience is to understand how neuronal circuits in the cerebral cortex play their functional roles through their characteristic firing activity. Several characteristics of spontaneous and sensory-evoked cortical activity have been reproduced by Infomax learning of neural networks in computational studies. There are, however, still few models of the underlying learning mechanisms that allow cortical circuits to maximize information and produce the characteristics of spontaneous and sensory-evoked cortical activity. In the present article, we derive a biologically plausible learning rule for the maximization of information retained through time in dynamics of simple recurrent neural networks. Applying the derived learning rule in a numerical simulation, we reproduce the characteristics of spontaneous and sensory-evoked cortical activity: cell-assembly-like repeats of precise firing sequences, neuronal avalanches, spontaneous replays of learned firing sequences and orientation selectivity observed in the primary visual cortex. We further discuss the similarity between the derived learning rule and the spike timing-dependent plasticity of cortical neurons.

Synaptic and nonsynaptic plasticity approximating probabilistic inference

Learning and memory operations in neural circuits are believed to involve molecular cascades of synaptic and nonsynaptic changes that lead to a diverse repertoire of dynamical phenomena at higher levels of processing. Hebbian and homeostatic plasticity, neuromodulation, and intrinsic excitability all conspire to form and maintain memories. But it is still unclear how these seemingly redundant mechanisms could jointly orchestrate learning in a more unified system. To this end, a Hebbian learning rule for spiking neurons inspired by Bayesian statistics is proposed. In this model, synaptic weights and intrinsic currents are adapted on-line upon arrival of single spikes, which initiate a cascade of temporally interacting memory traces that locally estimate probabilities associated with relative neuronal activation levels. Trace dynamics enable synaptic learning to readily demonstrate a spike-timing dependence, stably return to a set-point over long time scales, and remain competitive despite this stability. Beyond unsupervised learning, linking the traces with an external plasticity-modulating signal enables spike-based reinforcement learning. At the postsynaptic neuron, the traces are represented by an activity-dependent ion channel that is shown to regulate the input received by a postsynaptic cell and generate intrinsic graded persistent firing levels. We show how spike-based Hebbian-Bayesian learning can be performed in a simulated inference task using integrate-and-fire (IAF) neurons that are Poisson-firing and background-driven, similar to the preferred regime of cortical neurons. Our results support the view that neurons can represent information in the form of probability distributions, and that probabilistic inference could be a functional by-product of coupled synaptic and nonsynaptic mechanisms operating over several timescales. The model provides a biophysical realization of Bayesian computation by reconciling several observed neural phenomena whose functional effects are only partially understood in concert.

Effects of short-latency afferent inhibition on short-interval intracortical inhibition

Peripheral nerve stimulation inhibits the motor cortex, and the process has been termed short-latency afferent inhibition (SAI) at interstimulus intervals (ISIs) of ∼20 ms. The objective of the present study was to test how SAI interacts with short-interval intracortical inhibition (SICI) under different stimulation conditions. We studied 20 healthy volunteers. Surface electromyogram was recorded from the first dorsal interosseous muscle. Using paired- and triple-pulse paradigms, we investigated how SAI interacts with SICI under these different conditions. The effects of different conditioning stimulus (CS) intensities (0.6-0.9 active motor threshold), SAI latencies (23 and 25 ms), and ISIs (2 and 3 ms) for SICI were examined in rest and active conditions. SAI had inhibitory interactions with SICI at different CS intensities for rest or active SICI, at SAI latencies of 23 and 25 ms. This interaction occurred at weak CS intensities for SICI when there was no inhibition, and SICI became facilitatory in the presence of SAI. This can be explained by SICI inhibiting SAI and not by saturation of inhibition. The interaction between SAI and SICI was greater for SICI at ISI of 3 ms than for ISI of 2 ms, suggesting that different circuits may be activated at these ISIs. We conclude that SAI and SICI have inhibitory interactions that are influenced by factors such as ISI and muscle activities, which should be considered in design and interpretation of cortical interaction studies.

The cell-type specific cortical microcircuit: relating structure and activity in a full-scale spiking network model

In the past decade, the cell-type specific connectivity and activity of local cortical networks have been characterized experimentally to some detail. In parallel, modeling has been established as a tool to relate network structure to activity dynamics. While available comprehensive connectivity maps ( Thomson, West, et al. 2002; Binzegger et al. 2004) have been used in various computational studies, prominent features of the simulated activity such as the spontaneous firing rates do not match the experimental findings. Here, we analyze the properties of these maps to compile an integrated connectivity map, which additionally incorporates insights on the specific selection of target types. Based on this integrated map, we build a full-scale spiking network model of the local cortical microcircuit. The simulated spontaneous activity is asynchronous irregular and cell-type specific firing rates are in agreement with in vivo recordings in awake animals, including the low rate of layer 2/3 excitatory cells. The interplay of excitation and inhibition captures the flow of activity through cortical layers after transient thalamic stimulation. In conclusion, the integration of a large body of the available connectivity data enables us to expose the dynamical consequences of the cortical microcircuitry.

Circuit mechanisms revealed by spike-timing correlations in macaque area MT

We recorded simultaneously from pairs of motion-sensitive neurons in the middle temporal cortex (MT) of macaque monkeys and used cross-correlations in the timing of spikes between neurons to gain insights into cortical circuitry. We characterized the time course and stimulus dependency of the cross-correlogram (CCG) for each pair of neurons and of the auto-correlogram (ACG) of the individual neurons. For some neuron pairs, the CCG showed negative flanks that emerged next to the central peak during stimulus-driven responses. Similar negative flanks appeared in the ACG of many neurons. Negative flanks were most prevalent and deepest when the neurons were driven to high rates by visual stimuli that moved in the neurons' preferred directions. The temporal development of the negative flanks in the CCG coincided with a parallel, modest reduction of the noise correlation between the spike counts of the neurons. Computational analysis of a model cortical circuit suggested that negative flanks in the CCG arise from the excitation-triggered mutual cross-inhibition between pairs of excitatory neurons. Intracortical recurrent inhibition and afterhyperpolarization caused by intrinsic outward currents, such as the calcium-activated potassium current of small conductance, can both contribute to the negative flanks in the ACG. In the model circuit, stronger intracortical inhibition helped to maintain the temporal precision between the spike trains of pairs of neurons and led to weaker noise correlations. Our results suggest a neural circuit architecture that can leverage activity-dependent intracortical inhibition to adaptively modulate both the synchrony of spike timing and the correlations in response variability.

Does dynamical synchronization among neurons facilitate learning and enhance task performance?

Synchronization among groups of neurons is an interesting yet mysterious mechanism in the brain. We propose and demonstrate that the adjustable timing of neural activities can produce profound effect on learning and task implementation. On one hand, learning of more complex patterns becomes possible because of the enhanced capability of classification. On the other hand, implementation of a complex task is aided through active maintenance and control of multiple rules and items. This sheds light on the development of new intelligent system, as well as the cause of impaired learning and task performance in patients.

Extracellular glutamate: functional compartments operate in different concentration ranges

Extracellular glutamate of glial origin modulates glial and neuronal glutamate release and synaptic plasticity. Estimates of the tonic basal concentration of extracellular glutamate range over three orders of magnitude (0.02-20 μM) depending on the technology employed to make the measurement. Based upon binding constants for glutamate receptors and transporters, this range of concentrations translates into distinct physiological and pathophysiological roles for extracellular glutamate. Here we speculate that the difference in glutamate measurements can be explained if there is patterned membrane surface expression of glutamate release and transporter sites creating extracellular subcompartments that vary in glutamate concentration and are preferentially sampled by different technologies.

NMDA receptor activation enhances inhibitory GABAergic transmission onto hippocampal pyramidal neurons via presynaptic and postsynaptic mechanisms

N-methyl-d-aspartate (NMDA) receptors (NMDARs) are implicated in synaptic plasticity and modulation of glutamatergic excitatory transmission. Effect of NMDAR activation on inhibitory GABAergic transmission remains largely unknown. Here, we report that a brief application of NMDA could induce two distinct actions in CA1 pyramidal neurons in mouse hippocampal slices: 1) an inward current attributed to activation of postsynaptic NMDARs; and 2) fast phasic synaptic currents, namely spontaneous inhibitory postsynaptic currents (sIPSCs), mediated by GABAA receptors in pyramidal neurons. The mean amplitude of sIPSCs was also increased by NMDA. This profound increase in the sIPSC frequency and amplitude was markedly suppressed by the sodium channel blocker TTX, whereas the frequency and mean amplitude of miniature IPSCs were not significantly affected by NMDA, suggesting that NMDA elicits repetitive firing in GABAergic interneurons, thereby leading to GABA release from multiple synaptic sites of single GABAergic axons. We found that the NMDAR open-channel blocker MK-801 injected into recorded pyramidal neurons suppressed the NMDA-induced increase of sIPSCs, which raises the possibility that the firing of interneurons may not be the sole factor and certain retrograde messengers may also be involved in the NMDA-mediated enhancement of GABAergic transmission. Our results from pharmacological tests suggest that the nitric oxide signaling pathway is mobilized by NMDAR activation in CA1 pyramidal neurons, which in turn retrogradely facilitates GABA release from the presynaptic terminals. Thus NMDARs at glutamatergic synapses on both CA1 pyramidal neurons and interneurons appear to exert feedback and feedforward inhibition for determining the spike timing of the hippocampal microcircuit.

Neocortical layer 4 as a pluripotent function linearizer

A highly effective kernel-based strategy used in machine learning is to transform the input space into a new "feature" space where nonlinear problems become linear and more readily solvable with efficient linear techniques. We propose that a similar "problem-linearization" strategy is used by the neocortical input layer 4 to reduce the difficulty of learning nonlinear relations between the afferent inputs to a cortical column and its to-be-learned upper layer outputs. The key to this strategy is the presence of broadly tuned feed-forward inhibition in layer 4: it turns local layer 4 domains into functional analogs of radial basis function networks, which are known for their universal function approximation capabilities. With the use of a computational model of layer 4 with feed-forward inhibition and Hebbian afferent connections, self-organized on natural images to closely match structural and functional properties of layer 4 of the cat primary visual cortex, we show that such layer-4-like networks have a strong intrinsic tendency to perform input transforms that automatically linearize a broad repertoire of potential nonlinear functions over the afferent inputs. This capacity for pluripotent function linearization, which is highly robust to variations in network parameters, suggests that layer 4 might contribute importantly to sensory information processing as a pluripotent function linearizer, performing such a transform of afferent inputs to a cortical column that makes it possible for neurons in the upper layers of the column to learn and perform their complex functions using primarily linear operations.

Fast activation of feedforward inhibitory neurons from thalamic input and its relevance to the regulation of spike sequences in the barrel cortex

Thalamocortical afferents innervate both excitatory and inhibitory cells, the latter in turn producing disynaptic feedforward inhibition, thus creating fast excitation-inhibition sequences in the cortical cells. Since this inhibition is disynaptic, the time lag of the excitation-inhibition sequence could be approximately 2-3 ms, while it is often as short as only slightly above 1 ms; the mechanism and function of such fast IPSPs are not fully understood. Here we show that thalamic activation of inhibitory neurons precedes that of excitatory neurons, due to increased conduction velocity of thalamic axons innervating inhibitory cells. Developmentally, such latency differences were seen only after the end of the second postnatal week, prior to the completion of myelination of the thalamocortical afferent. Furthermore, destroying myelination failed to extinguish the latency difference. Instead, axons innervating inhibitory cells had consistently lower threshold, indicating they had larger diameter, which is likely to underlie the differential conduction velocity. Since faster activation of GABAergic neurons from the thalamus can not only curtail monosynaptic EPSPs but also make disynaptic ISPSs precede disynaptic EPSPs, such suppression theoretically enables a temporal separation of thalamically driven mono- and disynaptic EPSPs, resulting in spike sequences of 'L4 leading L2/3'. By recording L4 and L2/3 cells simultaneously, we found that suppression of IPSPs could lead to deterioration of spike sequences. Thus, from the end of the second postnatal week, by activating GABAergic neurons prior to excitatory neurons from the thalamus, fast feedforward disynaptic suppression on postsynaptic cells may play a role in establishing the spike sequences of 'L4 leading L2/3 cells'.

Cognitive impairment in pain through amygdala-driven prefrontal cortical deactivation

Cognitive deficits such as impaired decision-making can be a consequence of persistent pain. Normal functions of the intact amygdala and prefrontal cortex are required for emotion-based decision-making that relies on the ability to assess risk, attribute value, and identify advantageous strategies. We tested the hypothesis that pain-related cognitive deficits result from amygdala-driven impairment of medial prefrontal cortical (mPFC) function. To do this, we used electrophysiological single-unit recordings in vivo, patch clamp in brain slices, and various behavioral assays to show that increased neuronal activity in the amygdala in an animal model of arthritis pain was accompanied by decreased mPFC activation and impaired decision-making. Furthermore, pharmacologic inhibition (with a corticotropin-releasing factor 1 receptor antagonist) of pain-related hyperactivity in the basolateral amygdala (BLA), but not central amygdala (CeA), reversed deactivation of mPFC pyramidal cells and improved decision-making deficits. Pain-related cortical deactivation resulted from a shift of balance between inhibitory and excitatory synaptic transmission. Direct excitatory transmission to mPFC pyramidal cells did not change in the pain model, whereas polysynaptic inhibitory transmission increased. GABAergic transmission was reduced by non-NMDA receptor antagonists, suggesting that synaptic inhibition was glutamate driven. The results are consistent with a model of BLA-driven feedforward inhibition of mPFC neurons. In contrast to the differential effects of BLA versus CeA hyperactivity on cortical-cognitive functions, both amygdala nuclei modulate emotional-affective pain behavior. Thus, this study shows that the amygdala contributes not only to emotional-affective but also cognitive effects of pain. The novel amygdalo-cortical pain mechanism has important implications for our understanding of amygdala functions and amygdalo-cortical interactions.

Modeling the emergence of whisker direction maps in rat barrel cortex

Based on measuring responses to rat whiskers as they are mechanically stimulated, one recent study suggests that barrel-related areas in layer 2/3 rat primary somatosensory cortex (S1) contain a pinwheel map of whisker motion directions. Because this map is reminiscent of topographic organization for visual direction in primary visual cortex (V1) of higher mammals, we asked whether the S1 pinwheels could be explained by an input-driven developmental process as is often suggested for V1. We developed a computational model to capture how whisker stimuli are conveyed to supragranular S1, and simulate lateral cortical interactions using an established self-organizing algorithm. Inputs to the model each represent the deflection of a subset of 25 whiskers as they are contacted by a moving stimulus object. The subset of deflected whiskers corresponds with the shape of the stimulus, and the deflection direction corresponds with the movement direction of the stimulus. If these two features of the inputs are correlated during the training of the model, a somatotopically aligned map of direction emerges for each whisker in S1. Predictions of the model that are immediately testable include (1) that somatotopic pinwheel maps of whisker direction exist in adult layer 2/3 barrel cortex for every large whisker on the rat's face, even peripheral whiskers; and (2) in the adult, neurons with similar directional tuning are interconnected by a network of horizontal connections, spanning distances of many whisker representations. We also propose specific experiments for testing the predictions of the model by manipulating patterns of whisker inputs experienced during early development. The results suggest that similar intracortical mechanisms guide the development of primate V1 and rat S1.

Diminished perisomatic GABAergic terminals on cortical neurons adjacent to amyloid plaques

One of the main pathological hallmarks of Alzheimer's disease (AD) is the accumulation of plaques in the cerebral cortex, which may appear either in the neuropil or in direct association with neuronal somata. Since different axonal systems innervate the dendritic (mostly glutamatergic) and perisomatic (mostly GABAergic) regions of neurons, the accumulation of plaques in the neuropil or associated with the soma might produce different alterations to synaptic circuits. We have used a variety of conventional light, confocal and electron microscopy techniques to study their relationship with neuronal somata in the cerebral cortex from AD patients and APP/PS1 transgenic mice. The main finding was that the membrane surfaces of neurons (mainly pyramidal cells) in contact with plaques lack GABAergic perisomatic synapses. Since these perisomatic synapses are thought to exert a strong influence on the output of pyramidal cells, their loss may lead to the hyperactivity of the neurons in contact with plaques. These results suggest that plaques modify circuits in a more selective manner than previously thought.

Frequency-dependent disynaptic inhibition in the pyramidal network: a ubiquitous pathway in the developing rat neocortex

The general structure of the mammalian neocortex is remarkably similar across different cortical areas. Despite certain cytoarchitectural specializations and deviations from the general blueprint, the principal organization of the neocortex is relatively uniform. It is not known, however, to what extent stereotypic synaptic pathways resemble each other between cortical areas, and how far they might reflect possible functional uniformity or specialization. Here, we show that frequency-dependent disynaptic inhibition (FDDI) is a generic circuit motif that is present in all neocortical areas we investigated (primary somatosensory, auditory and motor cortex, secondary visual cortex and medial prefrontal cortex of the developing rat). We did find, however, area-specific differences in occurrence and kinetics of FDDI and the short-term dynamics of monosynaptic connections between pyramidal cells (PCs). Connectivity between PCs, both monosynaptic and via FDDI, is higher in primary cortices. The long-term effectiveness of FDDI is likely to be limited by an activity-dependent attenuation of the PC-interneuron synaptic transmission. Our results suggest that the basic construction of neocortical synaptic pathways follows principles that are independent of modality or hierarchical order within the neocortex.

Neocortical disynaptic inhibition requires somatodendritic integration in interneurons

In his theory of functional polarity, Ramon y Cajal first identified the soma and dendrites as the principal recipient compartments of a neuron and the axon as its main output structure. Despite notable exceptions in other parts of the nervous system (Schoppa and Urban, 2003; Wässle, 2004; Howard et al., 2005), this route of signal propagation has been shown to underlie the functional properties of most neocortical circuits studied so far. Recent evidence, however, suggests that neocortical excitatory cells may trigger the release of the inhibitory neurotransmitter GABA by directly depolarizing the axon terminals of inhibitory interneurons, thus bypassing their somatodendritic compartments (Ren et al., 2007). By using a combination of optical and electrophysiological approaches, we find that synaptically released glutamate fails to trigger GABA release through a direct action on GABAergic terminals under physiological conditions. Rather, our evidence suggests that glutamate triggers GABA release only after somatodendritic depolarization and action potential generation at GABAergic interneurons. These data indicate that neocortical inhibition is recruited by classical somatodendritic integration rather than direct activation of interneuron axon terminals.

Correlations in spiking neuronal networks with distance dependent connections

Can the topology of a recurrent spiking network be inferred from observed activity dynamics? Which statistical parameters of network connectivity can be extracted from firing rates, correlations and related measurable quantities? To approach these questions, we analyze distance dependent correlations of the activity in small-world networks of neurons with current-based synapses derived from a simple ring topology. We find that in particular the distribution of correlation coefficients of subthreshold activity can tell apart random networks from networks with distance dependent connectivity. Such distributions can be estimated by sampling from random pairs. We also demonstrate the crucial role of the weight distribution, most notably the compliance with Dales principle, for the activity dynamics in recurrent networks of different types.

Robust short-latency perisomatic inhibition onto neocortical pyramidal cells detected by laser-scanning photostimulation

Inhibitory connectivity onto neocortical pyramidal cells was mapped using LSPS (laser-scanning photostimulation/glutamate uncaging). The average onset latency of IPSCs was shorter than that of EPSCs recorded in the same cells, indicating a specific mechanism for rapid network recruitment of inhibition. The majority of strong inhibitory synaptic inputs originated within 300 mum of the recorded cell's soma, had onset latencies between 4 and 10 ms, and high amplitude [short-latency IPSCs (slIPSCs)]. slIPSCs were GABA(A) receptor- mediated chloride currents that were evoked in an all-or-none manner. We tested whether slIPSCs resulted from somatic depolarization of presynaptic interneurons or from direct excitation of inhibitory presynaptic terminals via kainate receptors. Our evidence supports the former hypothesis: (1) slIPSCs had similar sensitivity to kainate and AMPA receptor blockers as electrically evoked EPSCs. (2) slIPSCs frequently had an notched rising phase suggestive of summated IPSCs resulting from repetitive firing of presynaptic neurons. (3) Latencies and interevent intervals were consistent with spike latencies and interspike intervals in fast-spiking (FS) interneurons. (4) slIPSCs were frequently evoked at spots where the recorded cell was also excited directly, but approximately 15% of spots from which slIPSCs were evoked did not overlap with the recorded neuron's cell body. We propose that slIPSCs from FS interneurons represent a pool of powerful inhibitory signals that can be recruited by local excitation. Because of their magnitude, progressive recruitment, and short latency, slIPSCs are a effective mechanism of regulating excitability in neocortical circuits.

Proximity of excitatory and inhibitory axon terminals adjacent to pyramidal cell bodies provides a putative basis for nonsynaptic interactions

Although pyramidal cells are the main excitatory neurons in the cerebral cortex, it has recently been reported that they can evoke inhibitory postsynaptic currents in neighboring pyramidal neurons. These inhibitory effects were proposed to be mediated by putative axo-axonic excitatory synapses between the axon terminals of pyramidal cells and perisomatic inhibitory axon terminals [Ren M, Yoshimura Y, Takada N, Horibe S, Komatsu Y (2007) Science 316:758-761]. However, the existence of this type of axo-axonic synapse was not found using serial section electron microscopy. Instead, we observed that inhibitory axon terminals synapsing on pyramidal cell bodies were frequently apposed by terminals that established excitatory synapses with neighbouring dendrites. We propose that a spillover of glutamate from these excitatory synapses can activate the adjacent inhibitory axo-somatic terminals.

Polysynaptic subcircuits in the neocortex: spatial and temporal diversity

Inhibitory pathways in the neocortex display a variety of temporal and spatial patterns, maintaining a dynamic balance with excitatory synaptic activity. Recent studies have revealed prevalent polysynaptic subcircuits within the neocortical microcircuitry. These subcircuits involve excitatory and inhibitory connections that are activated by neurons both in supragranular and infragranular cortical layers and mediated by different mechanisms. Interestingly, in these subcircuits inhibition is induced by discharge of pyramidal cells, and excitation is caused by specific types of GABAergic interneurons. The different polysynaptic subcircuits are discussed with respect to their spatial and temporal properties and their possible functional role in cortical processing.

Synaptic cross talk between perisomatic-targeting interneuron classes expressing cholecystokinin and parvalbumin in hippocampus

Cholescystokinin (CCK)- or parvalbumin (PV)-containing interneurons are the major perisomatic-targeting interneurons in the cerebral cortex, including hippocampus, and are thought to form mutually exclusive networks. We used several techniques to test the alternative hypothesis that CCK and PV cells are coupled by chemical synapses. Triple immunofluorescence confocal microscopy revealed numerous axosomatic, axodendritic, and axoaxonic contacts stained for CCK, PV, and the presynaptic marker synaptophysin. The existence of mutual CCK and PV synapses was supported by dual EM immunolabeling. Paired whole-cell recordings detected unitary GABA(A)ergic synaptic transmission between identified CCK and PV cells, and single CCK cells could transiently inhibit action potential firing of synaptically coupled PV cells. We conclude that the major hippocampal perisomatic-targeting interneurons communicate synaptically. This communication should affect neuronal network activity, including neuronal oscillations, in which the CCK and PV cells have well established roles. The prevalence of CCK and PV networks in other brain regions suggests that internetwork interactions could be generally important.

Efficient coding correlates with spatial frequency tuning in a model of V1 receptive field organization

Efficient coding has been proposed to play an essential role in early visual processing. While several approaches used an objective function to optimize a particular aspect of efficient coding, such as the minimization of mutual information or the maximization of sparseness, we here explore how different estimates of efficient coding in a model with nonlinear dynamics and Hebbian learning determine the similarity of model receptive fields to V1 data with respect to spatial tuning. Our simulation results indicate that most measures of efficient coding correlate with the similarity of model receptive field data to V1 data, that is, optimizing the estimate of efficient coding increases the similarity of the model data to experimental data. However, the degree of the correlation varies with the different estimates of efficient coding, and in particular, the variance in the firing pattern of each cell does not predict a similarity of model and experimental data.

Impaired GABAergic transmission disrupts normal homeostatic plasticity in rat cortical networks

In the cortex, homeostatic plasticity appears to be a key process for maintaining neuronal network activity in a functional range. This phenomenon depends on close interactions between excitatory and inhibitory circuits. We previously showed that application of a high frequency of stimulation (HFS) protocol in layer 2/3 induces parallel potentiation of excitatory and inhibitory inputs on layer 5 pyramidal neurons, leading to an unchanged excitation/inhibition (E/I) balance. These coordinated long-term potentiations of excitation and inhibition correspond to homeostatic plasticity of the neuronal networks. We showed here, on the rat visual cortex, that blockade (with gabazine) or overactivation (with 4,5,6,7-tetrahydroisoxazolo[5,4-c]pyridin-3-ol) of GABA(A) receptors enhanced the E/I balance and prevented the potentiation of excitatory and inhibitory inputs after an HFS protocol. These impairements of the GABAergic transmission led to a long-term depression-like effect after an HFS protocol. We also observed that the blockade of inhibition reduced excitation (by 60%), and conversely, the blockade of excitation decreased inhibition (by 90%). These results support the idea that inhibitory interneurons are critical for recurrent interactions underlying homeostatic plasticity in cortical networks.

Correlations and Population Dynamics in Cortical Networks

The function of cortical networks depends on the collective interplay between neurons and neuronal populations, which is reflected in the correlation of signals that can be recorded at different levels. To correctly interpret these observations it is important to understand the origin of neuronal correlations. Here we study how cells in large recurrent networks of excitatory and inhibitory neurons interact and how the associated correlations affect stationary states of idle network activity. We demonstrate that the structure of the connectivity matrix of such networks induces considerable correlations between synaptic currents as well as between subthreshold membrane potentials, provided Dale's principle is respected. If, in contrast, synaptic weights are randomly distributed, input correlations can vanish, even for densely connected networks. Although correlations are strongly attenuated when proceeding from membrane potentials to action potentials (spikes), the resulting weak correlations in the spike output can cause substantial fluctuations in the population activity, even in highly diluted networks. We show that simple mean-field models that take the structure of the coupling matrix into account can adequately describe the power spectra of the population activity. The consequences of Dale's principle on correlations and rate fluctuations are discussed in the light of recent experimental findings.

Calcium release via activation of presynaptic IP3 receptors contributes to kainate-induced IPSC facilitation in rat neocortex

We examined the mechanisms of kainate (KA) induced modulation of GABA release in rat prefrontal cortex. Pharmacologically isolated IPSCs were recorded from visually identified layer II/III pyramidal cells using whole-cell patch clamp techniques. KA produced an increase in evoked IPSC amplitude at low nanomolar concentrations (100-500 nM). The frequency but not the amplitude of miniature (m) IPSCs was also increased. The GluR5 subunit selective agonist (RS)-2-amino-3-(3-hydroxy-5-tert-butylisoxazol-4-yl) propanoic acid (ATPA) caused an increase in mIPSC frequency whereas (3S,4aR,6S,8aR)-6-(4-carboxyphenyl)methyl-1,2,3,4,4a,5,6,7,8,8a-decahydroisoquinoline-3-carboxylic acid (LY382884), a selective GluR5 subunit antagonist, inhibited this facilitation. Philanthotoxin-433 (PhTx) blocked the effect of KA, indicating involvement of Ca(2+)-permeable GluR5 receptors. No IPSC facilitation was seen when Ca(2+) was omitted from the bathing solution. Facilitation was observed when slices were preincubated in ruthenium red or high concentrations of ryanodine, but was inhibited with application of thapsigargin. The IP3 receptor (IP3R) antagonists diphenylboric acid 2-amino-ethyl ester (2-APB) (15 microM) and Xestospongin C (XeC) blocked IPSC facilitation. These results show that activation of KA receptors (KARs) on GABAergic nerve terminals results is linked to intracellular Ca(2+) release via activation of IP3, but not ryanodine, receptors. This represents a new mechanism of presynaptic modulation whereby Ca(2+) entry through Ca(2+)-permeable GluR5 subunit containing KARs activates IP3Rs receptors leading to an increase in GABA release.

Presynaptic glutamate receptors: physiological functions and mechanisms of action

Glutamate acts on postsynaptic glutamate receptors to mediate excitatory communication between neurons. The discovery that additional presynaptic glutamate receptors can modulate neurotransmitter release has added complexity to the way we view glutamatergic synaptic transmission. Here we review evidence of a physiological role for presynaptic glutamate receptors in neurotransmitter release. We compare the physiological roles of ionotropic and metabotropic glutamate receptors in short- and long-term regulation of synaptic transmission. Furthermore, we discuss the physiological conditions that are necessary for their activation, the source of the glutamate that activates them, their mechanisms of action and their involvement in higher brain function.