Thalamocortical control of feed-forward inhibition in awake somatosensory 'barrel' cortex

Intracortical inhibition plays a role in shaping sensory cortical receptive fields and is mediated by both feed-forward and feedback mechanisms. Feed-forward inhibition is the faster of the two processes, being generated by inhibitory interneurons driven by monosynaptic thalamocortical (TC) input. In principle, feed-forward inhibition can prevent targeted cortical neurons from ever reaching threshold when TC input is weak. To do so, however, inhibitory interneurons must respond to TC input at low thresholds and generate spikes very quickly. A powerful feed-forward inhibition would sharpen the tuning characteristics of targeted cortical neurons, and interneurons with sensitive and broadly tuned receptive fields could mediate this process. Suspected inhibitory interneurons (SINs) with precisely these properties are found in layer 4 of the somatosensory (S1) 'barrel' cortex of rodents and rabbits. These interneurons lack the directional selectivity seen in most cortical spiny neurons and in ventrobasal TC afferents, but are much more sensitive than cortical spiny neurons to low-amplitude whisker displacements. This paper is concerned with the activation of S1 SINs by TC impulses, and with the consequences of this activation. Multiple TC neurons and multiple S1 SINs were simultaneously studied in awake rabbits, and cross-correlation methods were used to examine functional connectivity. The results demonstrate a potent, temporally precise, dynamic and highly convergent/divergent functional input from ventrobasal TC neurons to SINs of the topographically aligned S1 barrel. Whereas the extensive pooling of convergent TC inputs onto SINs generates sensitive and broadly tuned inhibitory receptive fields, the potent TC divergence onto many SINs generates sharply synchronous activity among these elements. This TC feed-forward inhibitory network is well suited to provide a fast, potent, sensitive and broadly tuned inhibition of targeted spiny neurons that will suppress spike generation following all but the most optimal feed-forward excitatory inputs.     

Neuronal replacement in microcircuits of the adult olfactory system

The local-circuit inhibitory interneurons containing gamma-aminobutyric acid (GABA) are continuously replaced in the adult olfactory bulb. Here, we describe how the production of new GABAergic interneurons is adapted to experience-induced plasticity. In particular, we discuss how such an adaptation is sensitive to the level of sensory inputs and how, in turn, neurogenesis may adjust the neural network functioning to optimize processing of sensory information. Finally, this review brings together recently described properties of interneurons as well as emerging principles of their functions that indicate a much more complex role for these cells than just that of gatekeepers providing inhibition.     

Efficient sparse coding in early sensory processing: lessons from signal recovery

Sensory representations are not only sparse, but often overcomplete: coding units significantly outnumber the input units. For models of neural coding this overcompleteness poses a computational challenge for shaping the signal processing channels as well as for using the large and sparse representations in an efficient way. We argue that higher level overcompleteness becomes computationally tractable by imposing sparsity on synaptic activity and we also show that such structural sparsity can be facilitated by statistics based decomposition of the stimuli into typical and atypical parts prior to sparse coding. Typical parts represent large-scale correlations, thus they can be significantly compressed. Atypical parts, on the other hand, represent local features and are the subjects of actual sparse coding. When applied on natural images, our decomposition based sparse coding model can efficiently form overcomplete codes and both center-surround and oriented filters are obtained similar to those observed in the retina and the primary visual cortex, respectively. Therefore we hypothesize that the proposed computational architecture can be seen as a coherent functional model of the first stages of sensory coding in early vision.     

Constrained brain volume in an efficient coding model explains the fraction of excitatory and inhibitory neurons in sensory cortices

The number of neurons in mammalian cortex varies by multiple orders of magnitude across different species. In contrast, the ratio of excitatory to inhibitory neurons (E:I ratio) varies in a much smaller range, from 3:1 to 9:1 and remains roughly constant for different sensory areas within a species. Despite this structure being important for understanding the function of neural circuits, the reason for this consistency is not yet understood. While recent models of vision based on the efficient coding hypothesis show that increasing the number of both excitatory and inhibitory cells improves stimulus representation, the two cannot increase simultaneously due to constraints on brain volume. In this work, we implement an efficient coding model of vision under a constraint on the volume (using number of neurons as a surrogate) while varying the E:I ratio. We show that the performance of the model is optimal at biologically observed E:I ratios under several metrics. We argue that this happens due to trade-offs between the computational accuracy and the representation capacity for natural stimuli. Further, we make experimentally testable predictions that 1) the optimal E:I ratio should be higher for species with a higher sparsity in the neural activity and 2) the character of inhibitory synaptic distributions and firing rates should change depending on E:I ratio. Our findings, which are supported by our new preliminary analyses of publicly available data, provide the first quantitative and testable hypothesis based on optimal coding models for the distribution of excitatory and inhibitory neural types in the mammalian sensory cortices.     

Linking dynamics of the inhibitory network to the input structure

Networks of inhibitory interneurons are found in many distinct classes of biological systems. Inhibitory interneurons govern the dynamics of principal cells and are likely to be critically involved in the coding of information. In this theoretical study, we describe the dynamics of a generic inhibitory network in terms of low-dimensional, simplified rate models. We study the relationship between the structure of external input applied to the network and the patterns of activity arising in response to that stimulation. We found that even a minimal inhibitory network can generate a great diversity of spatio-temporal patterning including complex bursting regimes with non-trivial ratios of burst firing. Despite the complexity of these dynamics, the network’s response patterns can be predicted from the rankings of the magnitudes of external inputs to the inhibitory neurons. This type of invariant dynamics is robust to noise and stable in densely connected networks with strong inhibitory coupling. Our study predicts that the response dynamics generated by an inhibitory network may provide critical insights about the temporal structure of the sensory input it receives.     

Are V1 Simple Cells Optimized for Visual Occlusions? A Comparative Study

Simple cells in primary visual cortex were famously found to respond to low-level image components such as edges. Sparse coding and independent component analysis (ICA) emerged as the standard computational models for simple cell coding because they linked their receptive fields to the statistics of visual stimuli. However, a salient feature of image statistics, occlusions of image components, is not considered by these models. Here we ask if occlusions have an effect on the predicted shapes of simple cell receptive fields. We use a comparative approach to answer this question and investigate two models for simple cells: a standard linear model and an occlusive model. For both models we simultaneously estimate optimal receptive fields, sparsity and stimulus noise. The two models are identical except for their component superposition assumption. We find the image encoding and receptive fields predicted by the models to differ significantly. While both models predict many Gabor-like fields, the occlusive model predicts a much sparser encoding and high percentages of ‘globular’ receptive fields. This relatively new center-surround type of simple cell response is observed since reverse correlation is used in experimental studies. While high percentages of ‘globular’ fields can be obtained using specific choices of sparsity and overcompleteness in linear sparse coding, no or only low proportions are reported in the vast majority of studies on linear models (including all ICA models). Likewise, for the here investigated linear model and optimal sparsity, only low proportions of ‘globular’ fields are observed. In comparison, the occlusive model robustly infers high proportions and can match the experimentally observed high proportions of ‘globular’ fields well. Our computational study, therefore, suggests that ‘globular’ fields may be evidence for an optimal encoding of visual occlusions in primary visual cortex.     

Feed-Forward versus Feedback Inhibition in a Basic Olfactory Circuit

Inhibitory interneurons play critical roles in shaping the firing patterns of principal neurons in many brain systems. Despite difference in the anatomy or functions of neuronal circuits containing inhibition, two basic motifs repeatedly emerge: feed-forward and feedback. In the locust, it was proposed that a subset of lateral horn interneurons (LHNs), provide feed-forward inhibition onto Kenyon cells (KCs) to maintain their sparse firing—a property critical for olfactory learning and memory. But recently it was established that a single inhibitory cell, the giant GABAergic neuron (GGN), is the main and perhaps sole source of inhibition in the mushroom body, and that inhibition from this cell is mediated by a feedback (FB) loop including KCs and the GGN. To clarify basic differences in the effects of feedback vs. feed-forward inhibition in circuit dynamics we here use a model of the locust olfactory system. We found both inhibitory motifs were able to maintain sparse KCs responses and provide optimal odor discrimination. However, we further found that only FB inhibition could create a phase response consistent with data recorded in vivo. These findings describe general rules for feed-forward versus feedback inhibition and suggest GGN is potentially capable of providing the primary source of inhibition to the KCs. A better understanding of how inhibitory motifs impact post-synaptic neuronal activity could be used to reveal unknown inhibitory structures within biological networks.     

Optimizing interneuron circuits for compartment-specific feedback inhibition

Cortical circuits process information by rich recurrent interactions between excitatory neurons and inhibitory interneurons. One of the prime functions of interneurons is to stabilize the circuit by feedback inhibition, but the level of specificity on which inhibitory feedback operates is not fully resolved. We hypothesized that inhibitory circuits could enable separate feedback control loops for different synaptic input streams, by means of specific feedback inhibition to different neuronal compartments. To investigate this hypothesis, we adopted an optimization approach. Leveraging recent advances in training spiking network models, we optimized the connectivity and short-term plasticity of interneuron circuits for compartment-specific feedback inhibition onto pyramidal neurons. Over the course of the optimization, the interneurons diversified into two classes that resembled parvalbumin (PV) and somatostatin (SST) expressing interneurons. Using simulations and mathematical analyses, we show that the resulting circuit can be understood as a neural decoder that inverts the nonlinear biophysical computations performed within the pyramidal cells. Our model provides a proof of concept for studying structure-function relations in cortical circuits by a combination of gradient-based optimization and biologically plausible phenomenological models.     

Interneurons unbound

Local-circuit, gamma-aminobutyric acid-releasing inhibitory interneurons of the hippocampus and cortex have traditionally been considered as the regulators of principal neuron activity--the yin to the excitatory yang. Recent evidence indicates that, in addition to that role, their network connectivity and the properties of their intrinsic voltage-gated currents are finely tuned to permit inhibitory interneurons to generate and control the rhythmic output of large populations of both principal cells and other populations of inhibitory interneurons. This review brings together recently described properties and emerging principles of interneuron function that indicate a much more complex role for these cells than just providers of inhibition.     

Automatic Adaptation to Fast Input Changes in a Time-Invariant Neural Circuit

Neurons must faithfully encode signals that can vary over many orders of magnitude despite having only limited dynamic ranges. For a correlated signal, this dynamic range constraint can be relieved by subtracting away components of the signal that can be predicted from the past, a strategy known as predictive coding, that relies on learning the input statistics. However, the statistics of input natural signals can also vary over very short time scales e.g., following saccades across a visual scene. To maintain a reduced transmission cost to signals with rapidly varying statistics, neuronal circuits implementing predictive coding must also rapidly adapt their properties. Experimentally, in different sensory modalities, sensory neurons have shown such adaptations within 100 ms of an input change. Here, we show first that linear neurons connected in a feedback inhibitory circuit can implement predictive coding. We then show that adding a rectification nonlinearity to such a feedback inhibitory circuit allows it to automatically adapt and approximate the performance of an optimal linear predictive coding network, over a wide range of inputs, while keeping its underlying temporal and synaptic properties unchanged. We demonstrate that the resulting changes to the linearized temporal filters of this nonlinear network match the fast adaptations observed experimentally in different sensory modalities, in different vertebrate species. Therefore, the nonlinear feedback inhibitory network can provide automatic adaptation to fast varying signals, maintaining the dynamic range necessary for accurate neuronal transmission of natural inputs.     

Olfactory networks: from sensation to perception

Olfactory networks, comprised of sensory neurons and interneurons, detect and process changes in the chemical environment to drive animal behavior. Recent studies combining genetics with behavioral analyses and imaging in worms, flies and mice have revealed new insights into the mechanisms of olfaction. In this discussion, we focus on three interesting findings. First, sensory neuron responses to odor are modulated by neuropeptides. This modulation might serve to extend the range of responses of the sensory neurons and also to integrate internal state information into the chemosensory circuit. Second, genetic tracing studies in mice and flies have shown that the first layer of connections in chemosensory circuits from olfactory epithelium to the glomeruli are stereotyped, while the subsequent connections to higher order sensory processing regions are not. Distributed connectivity to the higher order sensory processing regions has profound implications for how odors are represented in those regions. Third, recent work has revealed that odors are surprisingly sparsely represented in the piriform cortex. The sparse coding in the higher brain centers implies a much greater role for experience and learning in mediating responses to olfactory cues. Analyzing olfactory network function in various species provides us with fascinating clues about how sensory information is acquired, processed and represented at multiple levels within the nervous system.     

The neuronal targets for GABAergic reticulospinal inhibition that stops swimming in hatchling frog tadpoles

In most animals locomotion can be started and stopped by specific sensory cues. We are using a simple vertebrate, the hatchling Xenopus tadpole, to study a neuronal pathway that turns off locomotion. In the tadpole, swimming stops when the head contacts solid objects or the water's surface meniscus. The primary sensory neurons are in the trigeminal ganglion and directly excite inhibitory reticulospinal neurons in the hindbrain. These project axons into the spinal cord and release GABA to inhibit spinal neurons and stop swimming. We ask whether there is specificity in the types of spinal neuron inhibited. We used single-neuron recording to determine which classes of spinal neurons receive inhibition when the head skin is pressed. Ventral motoneurons and premotor interneurons involved in generating the swimming rhythm receive reliable GABAergic inhibition. More dorsal inhibitory premotor interneurons are inhibited less reliably and some are excited. Dorsal sensory pathway interneurons that start swimming following a touch to the trunk skin do not appear to receive such inhibition. There is therefore specificity in the formation of descending inhibitory connections so that more ventral neurons producing swimming are most strongly inhibited.     

Learning from inhibition: Functional roles of hippocampal CA1 inhibition in spatial learning and memory

Hippocampal inhibitory interneurons exert a powerful influence on learning and memory. Inhibitory interneurons are known to play a major role in many diseases that affect memory, and to strongly influence brain functions required for memory-related tasks. While previous studies involving genetic, optogenetic, and pharmacological manipulations have shown that hippocampal interneurons play essential roles in spatial and episodic learning and memory, exactly how interneurons affect local circuit computations during spatial navigation is not well understood. Given the significant anatomical, morphological, and functional heterogeneity in hippocampal interneurons, one may suspect cell-type specific roles in circuit computations. Here, we review emerging evidence of CA1 hippocampal interneurons’ role in local circuit computations that support spatial learning and memory and discuss open questions about CA1 interneurons in spatial learning.     

Sparse Coding Models Can Exhibit Decreasing Sparseness while Learning Sparse Codes for Natural Images

The sparse coding hypothesis has enjoyed much success in predicting response properties of simple cells in primary visual cortex (V1) based solely on the statistics of natural scenes. In typical sparse coding models, model neuron activities and receptive fields are optimized to accurately represent input stimuli using the least amount of neural activity. As these networks develop to represent a given class of stimulus, the receptive fields are refined so that they capture the most important stimulus features. Intuitively, this is expected to result in sparser network activity over time. Recent experiments, however, show that stimulus-evoked activity in ferret V1 becomes less sparse during development, presenting an apparent challenge to the sparse coding hypothesis. Here we demonstrate that some sparse coding models, such as those employing homeostatic mechanisms on neural firing rates, can exhibit decreasing sparseness during learning, while still achieving good agreement with mature V1 receptive field shapes and a reasonably sparse mature network state. We conclude that observed developmental trends do not rule out sparseness as a principle of neural coding per se: a mature network can perform sparse coding even if sparseness decreases somewhat during development. To make comparisons between model and physiological receptive fields, we introduce a new nonparametric method for comparing receptive field shapes using image registration techniques.     

Dynamical coding of sensory information with competitive networks.

Based on experiments with the locust olfactory system, we demonstrate that model sensory neural networks with lateral inhibition can generate stimulus specific identity-temporal patterns in the form of stimulus-dependent switching among small and dynamically changing neural ensembles (each ensemble being a group of synchronized projection neurons). Networks produce this switching mode of dynamical activity when lateral inhibitory connections are strongly non-symmetric. Such coding uses 'winner-less competitive' (WLC) dynamics. In contrast to the well known winner-take-all competitive (WTA) networks and Hopfield nets, winner-less competition represents sensory information dynamically. Such dynamics are reproducible, robust against intrinsic noise and sensitive to changes in the sensory input. We demonstrate the validity of sensory coding with WLC networks using two different formulations of the dynamics, namely the average and spiking dynamics of projection neurons (PN).     

Synchronous firing by specific pairs of cercal giant interneurons in crickets encodes wind direction

One prominent stimulus to evoke an escape response in crickets is the detection of air movement, such as would result from an attacking predator. Wind is detected by the cercal sensory system that consists of hundreds of sensory cells at the base of filiform hairs. These sensory cells relay information to about a dozen cercal giant and non-giant interneurons. The response of cercal sensory cells depends both, on the intensity and the direction of the wind. Spike trains of cercal giant interneurons then convey the information about wind direction and intensity to the central nervous system. Extracellular recording of multiple cercal giant interneurons shows that certain interneuron pairs fire synchronously if a wind comes from a particular direction. We demonstrate here that directional tuning curves of synchronously firing pairs of interneurons are sharper than those of single interneurons. Moreover, the sum total of all synchronously firing pairs eventually covers all wind directions. The sharpness of the tuning curves in synchronously firing pairs results from excitatory and inhibitory input from the cercal sensory neurons. Our results suggest, that synchronous firing of specific pairs of cercal giant interneurons encodes the wind direction. This was further supported by behavioral analyses.     

Quantitative Organization of GABAergic Synapses in the Molecular Layer of the Mouse Cerebellar Cortex

In the cerebellar cortex, interneurons of the molecular layer (stellate and basket cells) provide GABAergic input to Purkinje cells, as well as to each other and possibly to other interneurons. GABAergic inhibition in the molecular layer has mainly been investigated at the interneuron to Purkinje cell synapse. In this study, we used complementary subtractive strategies to quantitatively assess the ratio of GABAergic synapses on Purkinje cell dendrites versus those on interneurons. We generated a mouse model in which the GABA_A receptor α1 subunit (GABA_ARα1) was selectively removed from Purkinje cells using the Cre/loxP system. Deletion of the α1 subunit resulted in a complete loss of GABA_AR aggregates from Purkinje cells, allowing us to determine the density of GABA_AR clusters in interneurons. In a complementary approach, we determined the density of GABA synapses impinging on Purkinje cells using α-dystroglycan as a specific marker of inhibitory postsynaptic sites. Combining these inverse approaches, we found that synapses received by interneurons represent approximately 40% of all GABAergic synapses in the molecular layer. Notably, this proportion was stable during postnatal development, indicating synchronized synaptogenesis. Based on the pure quantity of GABAergic synapses onto interneurons, we propose that mutual inhibition must play an important, yet largely neglected, computational role in the cerebellar cortex.     

A specialized subclass of interneurons mediates dopaminergic facilitation of amygdala function

The amygdala is under inhibitory control from the cortex through the activation of local GABAergic interneurons. This inhibition is greatly diminished during heightened emotional states due to dopamine release. However, dopamine excites most amygdala interneurons, suggesting that this dopaminergic gate may be mediated by an unknown subpopulation of interneurons. We hypothesized that this gate is mediated by paracapsular intercalated cells, a subset of interneurons that are innervated by both cortical and mesolimbic dopaminergic afferents. Using transgenic mice that express GFP in GABAergic interneurons, we show that paracapsular cells form a network surrounding the basolateral complex of the amygdala. We found that they provide feedforward inhibition into the basolateral and the central amygdala. Dopamine hyperpolarized paracapsular cells through D1 receptors and substantially suppressed their excitability, resulting in a disinhibition of the basolateral and central nuclei. Suppression of the paracapsular system by dopamine provides a compelling neural mechanism for the increased affective behavior observed during stress or other hyperdopaminergic states.     

Emergence of orientation-selective inhibition in the primary visual cortex: a Bayes–Markov computational model

The recent consensus is that virtually all aspects of response selectivity exhibited by the primary visual cortex are either created or sharpened by cortical inhibitory interneurons. Experimental studies have shown that there are cortical inhibitory cells that are driven by geniculate cells and that, like their cortical excitatory counterparts, are orientation selective, though less sharply tuned. The main goal of this article is to demonstrate how orientation-selective inhibition might be created by the circuitry of the primary visual cortex (striate cortex, V1) from its nonoriented geniculate inputs. To fulfill this goal, first, a Bayes–Markov computational model is developed for the V1 area dedicated to foveal vision. The developed model consists of three parts: (i) a two-layered hierarchical Markov random field that is assumed to generate the activity patterns of the geniculate and cortical inhibitory cells, (ii) a Bayesian computational goal that is formulated based on the maximum a posteriori (MAP) estimation principle, and (iii) an iterative, deterministic, parallel algorithm that leads the cortical circuitry to achieve its assigned computational goal. The developed model is not fully LGN driven and it is not implementable by the neural machinery of V1. The model, then, is transformed into a fully LGN-driven and physiologically plausible form. Computer simulation is used to demonstrate the performance of the developed models.