Stimulus feature selectivity in excitatory and inhibitory neurons in primary visual cortex

Diverse effects of stimulus history in waking mouse auditory cortex

Responses to auditory stimuli are often strongly influenced by recent stimulus history. For example, in a paradigm called forward suppression, brief sounds can suppress the perception of, and the neural responses to, a subsequent sound, with the magnitude of this suppression depending on both the spectral and temporal distances between the sounds. As a step towards understanding the mechanisms that generate these adaptive representations in awake animals, we quantitatively characterize responses to two-tone sequences in the auditory cortex of waking mice. We find that cortical responses in a forward suppression paradigm are more diverse in waking mice than previously appreciated, that these responses vary between cells with different firing characteristics and waveform shapes, but that the variability in these responses is not substantially related to cortical depth or columnar location. Moreover, responses to the first tone in the sequence are often not linearly related to the suppression of the second tone response, suggesting that spike-frequency adaptation of cortical cells is not a large contributor to forward suppression or its variability. Instead, we use a simple multilayered model to show that cell-to-cell differences in the balance of intracortical inhibition and excitation will naturally produce such a diversity of forward interactions. We propose that diverse inhibitory connectivity allows the cortex to encode spectro-temporally fluctuating stimuli in multiple parallel ways.NEW & NOTEWORTHY Behavioral and neural responses to auditory stimuli are profoundly influenced by recent sounds, yet how this occurs is not known. Here, the authors show in the auditory cortex of awake mice that the quality of history-dependent effects is diverse and related to cell type, response latency, firing rates, and receptive field bandwidth. In a cortical model, differences in excitatory-inhibitory balance can produce this diversity, providing the cortex with multiple ways of representing temporally complex information.

Focal Local Field Potential Signature of the Single-Axon Monosynaptic Thalamocortical Connection

A resurgence has taken place in recent years in the use of the extracellularly recorded local field potential (LFP) to investigate neural network activity. To probe monosynaptic thalamic activation of cortical postsynaptic target cells, so called spike-trigger-averaged LFP (stLFP) signatures have been measured. In these experiments, the cortical LFP is measured by multielectrodes covering several cortical lamina and averaged on spontaneous spikes of thalamocortical (TC) cells. Using a well established forward-modeling scheme, we investigated the biophysical origin of this stLFP signature with simultaneous synaptic activation of cortical layer-4 neurons, mimicking the effect of a single afferent spike from a single TC neuron. Constrained by previously measured intracellular responses of the main postsynaptic target cell types and with biologically plausible assumptions regarding the spatial distribution of thalamic synaptic inputs into layer 4, the model predicted characteristic contributions to monosynaptic stLFP signatures both for the regular-spiking (RS) excitatory neurons and the fast-spiking (FS) inhibitory interneurons. In particular, the FS cells generated stLFP signatures of shorter temporal duration than the RS cells. Added together, a sum of the stLFP signatures of these two principal synaptic targets of TC cells were observed to resemble experimentally measured stLFP signatures. Outside the volume targeted by TC afferents, the resulting postsynaptic LFP signals were found to be sharply attenuated. This implies that such stLFP signatures provide a very local measure of TC synaptic activation, and that newly developed inverse current-source density (CSD)-estimation methods are needed for precise assessment of the underlying spatiotemporal CSD profiles.SIGNIFICANCE STATEMENT Despite its long history and prevalent use, the proper interpretation of the extracellularly recorded local field potential (LFP) is still not fully established. Here we investigate by biophysical modeling the origin of the focal LFP signature of the single-axon monosynaptic thalamocortical connection as measured by spike-trigger-averaging of cortical LFPs on spontaneous spikes of thalamocortical neurons. We find that this LFP signature is well accounted for by a model assuming thalamic projections to two cortical layer-4 cell populations: one excitatory (putatively regular-spiking cells) and one inhibitory (putatively fast-spiking cells). The LFP signature is observed to decay sharply outside the cortical region receiving the thalamocortical projection, implying that it indeed provides a very local measure of thalamocortical synaptic activation.

Fractional-order leaky integrate-and-fire model with long-term memory and power law dynamics

Pyramidal neurons produce different spiking patterns to process information, communicate with each other and transform information. These spiking patterns have complex and multiple time scale dynamics that have been described with the fractional-order leaky integrate-and-Fire (FLIF) model. Models with fractional (non-integer) order differentiation that generalize power law dynamics can be used to describe complex temporal voltage dynamics. The main characteristic of FLIF model is that it depends on all past values of the voltage that causes long-term memory. The model produces spikes with high interspike interval variability and displays several spiking properties such as upward spike-frequency adaptation and long spike latency in response to a constant stimulus. We show that the subthreshold voltage and the firing rate of the fractional-order model make transitions from exponential to power law dynamics when the fractional order α decreases from 1 to smaller values. The firing rate displays different types of spike timing adaptation caused by changes on initial values. We also show that the voltage-memory trace and fractional coefficient are the causes of these different types of spiking properties. The voltage-memory trace that represents the long-term memory has a feedback regulatory mechanism and affects spiking activity. The results suggest that fractional-order models might be appropriate for understanding multiple time scale neuronal dynamics. Overall, a neuron with fractional dynamics displays history dependent activities that might be very useful and powerful for effective information processing.

Intracellular, In Vivo, Dynamics of Thalamocortical Synapses in Visual Cortex

Seminal studies of the thalamocortical circuit in the visual system of the cat have been central to our understanding of sensory encoding. However, thalamocortical synaptic properties remain poorly understood. We used paired recordings, in the lateral geniculate nucleus (LGN) and primary visual cortex (V1), to provide the first in vivo characterization of sensory-driven thalamocortical potentials in V1. The amplitudes of EPSPs we characterized were smaller than those previously reported in vitro Consistent with prior findings, connected LGN-V1 pairs were only found when their receptive fields (RFs) overlapped, and the probability of connection increased steeply with degree of RF overlap and response similarity. However, surprisingly, we found no relationship between EPSP amplitudes and the similarity of RFs or responses, suggesting different connectivity models for intracortical and thalamocortical circuits. Putative excitatory regular-spiking (RS) and inhibitory fast-spiking (FS) V1 cells had similar EPSP characteristics, showing that in the visual system, feedforward excitation and inhibition are driven with equal strength by the thalamus. Similar to observations in the somatosensory cortex, FS V1 cells received less specific input from LGN. Finally, orientation tuning in V1 was not inherited from single presynaptic LGN cells, suggesting that it must emerge exclusively from the combined input of all presynaptic LGN cells. Our results help to decipher early visual encoding circuits and have immediate utility in providing physiological constraints to computational models of the visual system.SIGNIFICANCE STATEMENT To understand how the brain encodes the visual environment, we must understand the transfer of visual signals between various regions of the brain. Therefore, understanding synaptic dynamics is critical to our understanding of sensory encoding. This study provides the first characterization of visually evoked synaptic potentials between the visual thalamus and visual cortex in an intact animal. To record these potentials, we simultaneously recorded the extracellular potential of presynaptic thalamic cells and the intracellular potential of postsynaptic cortical cells in input layers of primary visual cortex. Our characterization of synaptic potentials in vivo disagreed with prior findings in vitro This study will increase our understanding of thalamocortical circuits and will improve computational models of visual encoding.

Inhibitory suppression of heterogeneously tuned excitation enhances spatial coding in CA1 place cells

Place cells in the CA1 region of the hippocampus express location-specific firing despite receiving a steady barrage of heterogeneously tuned excitatory inputs that should compromise output dynamic range and timing. We examined the role of synaptic inhibition in countering the deleterious effects of off-target excitation. Intracellular recordings in behaving mice demonstrate that bimodal excitation drives place cells, while unimodal excitation drives weaker or no spatial tuning in interneurons. Optogenetic hyperpolarization of interneurons had spatially uniform effects on place cell membrane potential dynamics, substantially reducing spatial selectivity. These data and a computational model suggest that spatially uniform inhibitory conductance enhances rate coding in place cells by suppressing out-of-field excitation and by limiting dendritic amplification. Similarly, we observed that inhibitory suppression of phasic noise generated by out-of-field excitation enhances temporal coding by expanding the range of theta phase precession. Thus, spatially uniform inhibition allows proficient and flexible coding in hippocampal CA1 by suppressing heterogeneously tuned excitation.

Functional synchrony and stimulus selectivity of visual cortical units: Comparison between cats and mice

In spite of the fact that the functional organization of primary visual cortices (V1) differs across species, the dynamic of orientation selectivity is highly structured within neuronal populations. In fact, neurons functionally connect each other in an organized Hebbian process, wherein their wiring and firing are intimately related. Moreover, neuronal ensembles have been suggested to be strongly implicated in sensory processing. Within these ensembles, neurons may be sharply or broadly tuned in relation to the stimulus. Therefore, it is important to determine the relationship between the response selectivity of neurons and their functional connectivity pattern across species. In the present investigation, we sought to compare the stimulus-evoked functional connectivity between the broadly tuned and the sharply tuned neurons in two species exhibiting different cortical organization for orientation selectivity: cats (columnar-organized) and mice (salt-and-pepper organization). In addition, we examined the distribution of connectivity weights within cell-assemblies in the visual cortex during visual adaptation. First, we report that the sharply tuned neurons exhibited higher synchrony index than the broadly tuned cells in the cat visual cortex. On the contrary, in mice, the broadly tuned cells displayed higher connectivity index. Second, a significant correlation was found between the connectivity strength and the difference of preferred orientations of neurons for both species. Finally, we observed a systematic adjustment of the connectivity weights within neuronal ensembles in mouse primary visual cortex similarly to the cat V1.

Functional dissection of inhibitory microcircuits in the visual cortex

Cerebral cortex contains various types of GABAergic neurons exerting local inhibition. Although the number of GABAergic inhibitory neurons is much smaller than glutamatergic excitatory neurons, they show greater diversity in their morphological and physiological properties. Genetic markers for distinct sub-classes of GABAergic neurons have been identified, and technical advances achieved in the past few decades have brought about a demonstration of a unique function of each sub-class of GABAergic neurons in the cortex. In particular, visual processing in the cortex requires inhibitory function of various GABAergic neurons. Here, we summarize current understandings on the function of inhibitory neurons in the cortex, especially focusing on their roles in visual processing.

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.

Push-Pull Receptive Field Organization and Synaptic Depression: Mechanisms for Reliably Encoding Naturalistic Stimuli in V1

Neurons in the primary visual cortex are known for responding vigorously but with high variability to classical stimuli such as drifting bars or gratings. By contrast, natural scenes are encoded more efficiently by sparse and temporal precise spiking responses. We used a conductance-based model of the visual system in higher mammals to investigate how two specific features of the thalamo-cortical pathway, namely push-pull receptive field organization and fast synaptic depression, can contribute to this contextual reshaping of V1 responses. By comparing cortical dynamics evoked respectively by natural vs. artificial stimuli in a comprehensive parametric space analysis, we demonstrate that the reliability and sparseness of the spiking responses during natural vision is not a mere consequence of the increased bandwidth in the sensory input spectrum. Rather, it results from the combined impacts of fast synaptic depression and push-pull inhibition, the later acting for natural scenes as a form of “effective” feed-forward inhibition as demonstrated in other sensory systems. Thus, the combination of feedforward-like inhibition with fast thalamo-cortical synaptic depression by simple cells receiving a direct structured input from thalamus composes a generic computational mechanism for generating a sparse and reliable encoding of natural sensory events.

How inhibitory circuits in the thalamus serve vision

Inhibitory neurons dominate the intrinsic circuits in the visual thalamus. Interneurons in the lateral geniculate nucleus innervate relay cells and each other densely to provide powerful inhibition. The visual sector of the overlying thalamic reticular nucleus receives input from relay cells and supplies feedback inhibition to them in return. Together, these two inhibitory circuits influence all information transmitted from the retina to the primary visual cortex. By contrast, relay cells make few local connections. This review explores the role of thalamic inhibition from the dual perspectives of feature detection and information theory. For example, we describe how inhibition sharpens tuning for spatial and temporal features of the stimulus and how it might enhance image perception. We also discuss how inhibitory circuits help to reduce redundancy in signals sent downstream and, at the same time, are adapted to maximize the amount of information conveyed to the cortex.

Neuronal Firing Rate Homeostasis Is Inhibited by Sleep and Promoted by Wake

Homeostatic mechanisms stabilize neural circuit function by keeping firing rates within a set-point range, but whether this process is gated by brain state is unknown. Here, we monitored firing rate homeostasis in individual visual cortical neurons in freely behaving rats as they cycled between sleep and wake states. When neuronal firing rates were perturbed by visual deprivation, they gradually returned to a precise, cell-autonomous set point during periods of active wake, with lengthening of the wake period enhancing firing rate rebound. Unexpectedly, this resetting of neuronal firing was suppressed during sleep. This raises the possibility that memory consolidation or other sleep-dependent processes are vulnerable to interference from homeostatic plasticity mechanisms. PAPERCLIP.

Effect of Contrast on Visual Spatial Summation in Different Cell Categories in Cat Primary Visual Cortex

Multiple cell classes have been found in the primary visual cortex, but the relationship between cell types and spatial summation has seldom been studied. Parvalbumin-expressing inhibitory interneurons can be distinguished from pyramidal neurons based on their briefer action potential durations. In this study, we classified V1 cells into fast-spiking units (FSUs) and regular-spiking units (RSUs) and then examined spatial summation at high and low contrast. Our results revealed that the excitatory classical receptive field and the suppressive non-classical receptive field expanded at low contrast for both FSUs and RSUs, but the expansion was more marked for the RSUs than for the FSUs. For most V1 neurons, surround suppression varied as the contrast changed from high to low. However, FSUs exhibited no significant difference in the strength of suppression between high and low contrast, although the overall suppression decreased significantly at low contrast for the RSUs. Our results suggest that the modulation of spatial summation by stimulus contrast differs across populations of neurons in the cat primary visual cortex.

Electrophysiological and firing properties of neurons: Categorizing soloists and choristers in primary visual cortex

Visual processing in the cortex involves various aspects of neuronal properties such as morphological, electrophysiological and molecular. In particular, the neural firing pattern is an important indicator of dynamic circuitry within a neuronal population. Indeed, in microcircuits, neurons act as soloists or choristers wherein the characteristical activity of a 'soloist' differs from the firing pattern of a 'chorister'. Both cell types correlate their respective firing rate with the global populational activity in a unique way. In the present study, we sought to examine the relationship between the spike shape (thin spike neurons and broad spike neurons) of cortical neurons recorded from V1, their firing levels and their propensity to act as soloists or choristers. We found that thin spike neurons, which exhibited higher levels of firing, generally correlate their activity with the neuronal population (choristers). On the other hand, broad spike neurons showed lower levels of firing and demonstrated weak correlations with the assembly (soloists). A major consequence of the present study is: estimating the correlation of neural spike trains with their neighboring population is a predictive indicator of spike waveforms and firing level. Indeed, we found a continuum distribution of coupling strength ranging from weak correlation-strength (attributed to low-firing neurons) to high correlation-strength (attributed to high-firing neurons). The tendency to exhibit high- or low-firing is conducive to the spike shape of neurons. Our results offer new insights into visual processing by showing how high-firing rate neurons (mostly thin spike neurons) could modulate the neuronal responses within cell-assemblies.

Subcortical orientation biases explain orientation selectivity of visual cortical cells

The primary visual cortex of carnivores and primates shows an orderly progression of domains of neurons that are selective to a particular orientation of visual stimuli such as bars and gratings. We recorded from single-thalamic afferent fibers that terminate in these domains to address the issue whether the orientation sensitivity of these fibers could form the basis of the remarkable orientation selectivity exhibited by most cortical cells. We first performed optical imaging of intrinsic signals to obtain a map of orientation domains on the dorsal aspect of the anaesthetized cat's area 17. After confirming using electrophysiological recordings the orientation preferences of single neurons within one or two domains in each animal, we pharmacologically silenced the cortex to leave only the afferent terminals active. The inactivation of cortical neurons was achieved by the superfusion of either kainic acid or muscimol. Responses of single geniculate afferents were then recorded by the use of high impedance electrodes. We found that the orientation preferences of the afferents matched closely with those of the cells in the orientation domains that they terminated in (Pearson's r = 0.633, n = 22, P = 0.002). This suggests a possible subcortical origin for cortical orientation selectivity.

Acute alcohol exposure impairs neural representation of visual motion speed in the visual cortex area posteromedial lateral suprasylvian cortex of cats

BACKGROUND

Psychophysical and behavioral studies have demonstrated that perception of motion can be impaired by acute alcohol exposure. The neural activities of posteromedial lateral suprasylvian cortex (PMLS) of cats are directly linked to the perception of visual motion speed. To date, there have been no studies on the effects of acute alcohol exposure in vivo upon the representation of speed in PMLS neurons.

METHODS

Alcohol was administered intravenously as a 20% (v/v) saline solution via a syringe at a dose levels of 0.5, 1, or 2 g/kg to generate a series of blood alcohol concentrations. Using extracellular single-unit recording technique, we recorded the speed-tuning properties of PMLS neurons that responded to random-dot patterns before and after alcohol administration, and simultaneously monitored the concentration of ethanol by detecting the breath alcohol concentration using a breath analyzer.

RESULTS

After acute alcohol treatment, PMLS cells preferred lower speeds. A broadened speed-tuning bandwidth of PMLS cells was also observed after acute alcohol administration. Additionally, response modulation and discriminative capacity for speed of visual motion in the PMLS cells were significantly impaired after acute alcohol exposure. Concurrently, PMLS cells after acute alcohol exposure showed decreased spontaneous activity, peak responses, and signal-to-noise ratios.

CONCLUSIONS

There is a significant functional degradation in the neural representation of visual motion speed in PMLS of cats after acute alcohol exposure. These neural changes may contribute to the alcohol-related deficits in visual motion perception observed in behavioral studies.

Coding principles of the canonical cortical microcircuit in the avian brain

Mammalian neocortex is characterized by a layered architecture and a common or "canonical" microcircuit governing information flow among layers. This microcircuit is thought to underlie the computations required for complex behavior. Despite the absence of a six-layered cortex, birds are capable of complex cognition and behavior. In addition, the avian auditory pallium is composed of adjacent information-processing regions with genetically identified neuron types and projections among regions comparable with those found in the neocortex. Here, we show that the avian auditory pallium exhibits the same information-processing principles that define the canonical cortical microcircuit, long thought to have evolved only in mammals. These results suggest that the canonical cortical microcircuit evolved in a common ancestor of mammals and birds and provide a physiological explanation for the evolution of neural processes that give rise to complex behavior in the absence of cortical lamination.

Target-specific properties of thalamocortical synapses onto layer 4 of mouse primary visual cortex

In primary sensory cortices, thalamocortical (TC) inputs can directly activate excitatory and inhibitory neurons. In vivo experiments in the main input layer (L4) of primary visual cortex (V1) have shown that excitatory and inhibitory neurons have different tuning properties. The different functional properties may arise from distinct intrinsic properties of L4 neurons, but could also depend on cell type-specific properties of the synaptic inputs from the lateral geniculate nucleus of the thalamus (LGN) onto L4 neurons. While anatomical studies identified LGN inputs onto both excitatory and inhibitory neurons in V1, their synaptic properties have not been investigated. Here we used an optogenetic approach to selectively activate LGN terminal fields in acute coronal slices containing V1, and recorded monosynaptic currents from excitatory and inhibitory neurons in L4. LGN afferents made monosynaptic connections with pyramidal (Pyr) and fast-spiking (FS) neurons. TC EPSCs on FS neurons were larger and showed steeper short-term depression in response to repetitive stimulation than those on Pyr neurons. LGN inputs onto Pyr and FS neurons also differed in postsynaptic receptor composition and organization of presynaptic release sites. Together, our results demonstrate that LGN input onto L4 neurons in mouse V1 have target-specific presynaptic and postsynaptic properties. Distinct mechanisms of activation of feedforward excitatory and inhibitory neurons in the main input layer of V1 are likely to endow neurons with different response properties to incoming visual stimuli.

Temporal and spatial limits of pattern motion sensitivity in macaque MT neurons

Many neurons in visual cortical area MT signal the direction of motion of complex visual patterns, such as plaids composed of two superimposed drifting gratings. To compute the direction of pattern motion, MT neurons combine component motion signals over time and space. To determine the spatial and temporal limits of signal integration, we measured the responses of single MT neurons to a novel set of "pseudoplaid" stimuli in which the component gratings were alternated in time or space. As the temporal or spatial separation of the component gratings increased, neuronal selectivity for the direction of pattern motion decreased. Using descriptive models of signal integration, we inferred the temporal and spatial structure of the mechanisms that compute pattern direction selectivity. The median time constant for integration was roughly 10 ms, a timescale characteristic of integration by single cortical pyramidal neurons. The median spatial integration field was roughly one-third of the MT receptive field diameter, suggesting that the spatial limits are set by stages of processing in earlier areas of visual cortex where receptive fields are smaller than in MT. Interestingly, pattern direction-selective neurons had shorter temporal integration times than component direction-selective neurons but similar spatial integration windows. We conclude that pattern motion can only be signaled by MT neurons when the component motion signals co-occur within relatively narrow spatial and temporal limits. We interpret these results in the framework of recent hierarchical models of MT.

Developmental self-construction and -configuration of functional neocortical neuronal networks

The prenatal development of neural circuits must provide sufficient configuration to support at least a set of core postnatal behaviors. Although knowledge of various genetic and cellular aspects of development is accumulating rapidly, there is less systematic understanding of how these various processes play together in order to construct such functional networks. Here we make some steps toward such understanding by demonstrating through detailed simulations how a competitive co-operative (‘winner-take-all’, WTA) network architecture can arise by development from a single precursor cell. This precursor is granted a simplified gene regulatory network that directs cell mitosis, differentiation, migration, neurite outgrowth and synaptogenesis. Once initial axonal connection patterns are established, their synaptic weights undergo homeostatic unsupervised learning that is shaped by wave-like input patterns. We demonstrate how this autonomous genetically directed developmental sequence can give rise to self-calibrated WTA networks, and compare our simulation results with biological data.

Models of learning in artificial neural networks generally assume that the neurons and approximate network are given, and then learning tunes the synaptic weights. By contrast, we address the question of how an entire functional neuronal network containing many differentiated neurons and connections can develop from only a single progenitor cell. We chose a winner-take-all network as the developmental target, because it is a computationally powerful circuit, and a candidate motif of neocortical networks. The key aspect of this challenge is that the developmental mechanisms must be locally autonomous as in Biology: They cannot depend on global knowledge or supervision. We have explored this developmental process by simulating in physical detail the fundamental biological behaviors, such as cell proliferation, neurite growth and synapse formation that give rise to the structural connectivity observed in the superficial layers of the neocortex. These differentiated, approximately connected neurons then adapt their synaptic weights homeostatically to obtain a uniform electrical signaling activity before going on to organize themselves according to the fundamental correlations embedded in a noisy wave-like input signal. In this way the precursor expands itself through development and unsupervised learning into winner-take-all functionality and orientation selectivity in a biologically plausible manner.

Inhibition facilitates direction selectivity in a noisy cortical environment

In a broad class of models, direction selectivity in primary visual cortical neurons arises from the linear summation of spatially offset and temporally lagged inputs combined with a spike threshold. Here, we characterize the robustness of this class of models to input noise and background activity that is uncorrelated with the visual stimulus. When only excitatory inputs were considered, moderate levels of noise substantially degraded direction selectivity. By contrast, the inclusion of inhibition produced a direction-selective neuron even at high noise levels. Moreover, if inhibitory inputs were tuned, mirroring excitatory inputs but lagging by a fixed delay, they promoted a highly direction-selective response by suppressing all excitatory inputs in the null direction while minimally affecting excitatory inputs in the preferred direction. Additionally, tuned inhibition strongly reduced trial-by-trial variability, such that the neuron produced a consistent direction-selective response to multiple presentation of the same stimulus. This work illustrates how inhibition could be used by cortical circuits to reliably extract information on a single-trial basis from feed-forward inputs in a noisy, high-background context.

Pyramidal cells make specific connections onto smooth (GABAergic) neurons in mouse visual cortex

Light and electron microscopy of the primary visual cortex of mice indicates that pyramidal neurons connect preferentially to inhibitory neurons.

One of the hallmarks of neocortical circuits is the predominance of recurrent excitation between pyramidal neurons, which is balanced by recurrent inhibition from smooth GABAergic neurons. It has been previously described that in layer 2/3 of primary visual cortex (V1) of cat and monkey, pyramidal cells filled with horseradish peroxidase connect approximately in proportion to the spiny (excitatory, 95% and 81%, respectively) and smooth (GABAergic, 5% and 19%, respectively) dendrites found in the neuropil. By contrast, a recent ultrastructural study of V1 in a single mouse found that smooth neurons formed 51% of the targets of the superficial layer pyramidal cells. This suggests that either the neuropil of this particular mouse V1 had a dramatically different composition to that of V1 in cat and monkey, or that smooth neurons were specifically targeted by the pyramidal cells in that mouse. We tested these hypotheses by examining similar cells filled with biocytin in a sample of five mice. We found that the average composition of the neuropil in V1 of these mice was similar to that described for cat and monkey V1, but that the superficial layer pyramidal cells do form proportionately more synapses with smooth dendrites than the equivalent neurons in cat or monkey. These distributions may underlie the distinct differences in functional architecture of V1 between rodent and higher mammals.

The mammalian visual cortex, which is part of the cerebral cortex, contains 50 to 100 thousands of neurons per cubic millimetre, most of which are excitatory (85%) and the minority, inhibitory (15%). Unlike neurons in the retina, neurons in the visual cortex are preferentially activated by lines or edges of a particular orientation. This is termed a neuron's “orientation preference.” In the visual cortex of higher mammals like cats and monkeys, neurons that share an orientation preference are clustered in functional columns. However, in rodents like mice, orientation preferences are randomly distributed. In this study, we investigate whether the differences between columnar and non-columnar cortex is correlated with differences in the connectivity patterns between excitatory and inhibitory neurons. Using light and electron microscopy, we mapped the connectivity of pyramidal neurons—the primary excitatory neurons—in the superficial layers of the primary visual cortex (V1) of mice. Our results show that the ratio of excitatory-inhibitory neurons in mouse V1 is similar to that of cat or monkey V1, but in mouse V1 local pyramidal neurons target proportionately many more inhibitory neurons compared to what other studies found in cat or monkey. This difference may indicate the significance of inhibition in maintaining orientation selectivity in the non-columnar visual cortex of rodents like mice and is a distinct difference in the architecture of V1 between mice and higher mammals.

Regulating Cortical Oscillations in an Inhibition-Stabilized Network

Understanding the anatomical and functional architecture of the brain is essential for designing neurally inspired intelligent systems. Theoretical and empirical studies suggest a role for narrowband oscillations in shaping the functional architecture of the brain through their role in coding and communication of information. Such oscillations are ubiquitous signals in the electrical activity recorded from the brain. In the cortex, oscillations detected in the gamma range (30-80 Hz) are modulated by behavioral states and sensory features in complex ways. How is this regulation achieved? Although several underlying principles for the genesis of these oscillations have been proposed, a unifying account for their regulation has remained elusive. In a network of excitatory and inhibitory neurons operating in an inhibition-stabilized regime, we show that strongly superlinear responses of inhibitory neurons facilitate bidirectional regulation of oscillation frequency and power. In such a network, the balance of drives to the excitatory and inhibitory populations determines how the power and frequency of oscillations are modulated. The model accounts for the puzzling increase in their frequency with the salience of visual stimuli, and a decrease with their size. Oscillations in our model grow stronger as the mean firing level is reduced, accounting for the size dependence of visually evoked gamma rhythms, and suggesting a role for oscillations in improving the signal-to-noise ratio (SNR) of signals in the brain. Empirically testing such predictions is still challenging, and implementing the proposed coding and communication strategies in neuromorphic systems could assist in our understanding of the biological system.

Neuronal spike timing adaptation described with a fractional leaky integrate-and-fire model

The voltage trace of neuronal activities can follow multiple timescale dynamics that arise from correlated membrane conductances. Such processes can result in power-law behavior in which the membrane voltage cannot be characterized with a single time constant. The emergent effect of these membrane correlations is a non-Markovian process that can be modeled with a fractional derivative. A fractional derivative is a non-local process in which the value of the variable is determined by integrating a temporal weighted voltage trace, also called the memory trace. Here we developed and analyzed a fractional leaky integrate-and-fire model in which the exponent of the fractional derivative can vary from 0 to 1, with 1 representing the normal derivative. As the exponent of the fractional derivative decreases, the weights of the voltage trace increase. Thus, the value of the voltage is increasingly correlated with the trajectory of the voltage in the past. By varying only the fractional exponent, our model can reproduce upward and downward spike adaptations found experimentally in neocortical pyramidal cells and tectal neurons in vitro. The model also produces spikes with longer first-spike latency and high inter-spike variability with power-law distribution. We further analyze spike adaptation and the responses to noisy and oscillatory input. The fractional model generates reliable spike patterns in response to noisy input. Overall, the spiking activity of the fractional leaky integrate-and-fire model deviates from the spiking activity of the Markovian model and reflects the temporal accumulated intrinsic membrane dynamics that affect the response of the neuron to external stimulation.

Spike adaptation is a property of most neurons. When spike time adaptation occurs over multiple time scales, the dynamics can be described by a power-law. We study the computational properties of a leaky integrate-and-fire model with power-law adaptation. Instead of explicitly modeling the adaptation process by the contribution of slowly changing conductances, we use a fractional temporal derivative framework. The exponent of the fractional derivative represents the degree of adaptation of the membrane voltage, where 1 is the normal leaky integrator while values less than 1 produce increasing correlations in the voltage trace. The temporal correlation is interpreted as a memory trace that depends on the value of the fractional derivative. We identify the memory trace in the fractional model as the sum of the instantaneous differentiation weighted by a function that depends on the fractional exponent, and it provides non-local information to the incoming stimulus. The spiking dynamics of the fractional leaky integrate-and-fire model show memory dependence that can result in downward or upward spike adaptation. Our model provides a framework for understanding how long-range membrane voltage correlations affect spiking dynamics and information integration in neurons.

GABAergic synapses: their plasticity and role in sensory cortex

The mammalian neocortex is composed of a variety of cell types organized in a highly interconnected circuit. GABAergic neurons account for only about 20% of cortical neurons. However, they show widespread connectivity and a high degree of diversity in morphology, location, electrophysiological properties and gene expression. In addition, distinct populations of inhibitory neurons have different sensory response properties, capacities for plasticity and sensitivities to changes in sensory experience. In this review we summarize experimental evidence regarding the properties of GABAergic neurons in primary sensory cortex. We will discuss how distinct GABAergic neurons and different forms of GABAergic inhibitory plasticity may contribute to shaping sensory cortical circuit activity and function.

Firing rate homeostasis in visual cortex of freely behaving rodents

It has been postulated that homeostatic mechanisms maintain stable circuit function by keeping neuronal firing within a set point range, but such firing rate homeostasis has never been demonstrated in vivo. Here we use chronic multielectrode recordings to monitor firing rates in visual cortex of freely behaving rats during chronic monocular visual deprivation (MD). Firing rates in V1 were suppressed over the first 2 day of MD but then rebounded to baseline over the next 2-3 days despite continued MD. This drop and rebound in firing was accompanied by bidirectional changes in mEPSC amplitude measured ex vivo. The rebound in firing was independent of sleep-wake state but was cell type specific, as putative FS and regular spiking neurons responded to MD with different time courses. These data establish that homeostatic mechanisms within the intact CNS act to stabilize neuronal firing rates in the face of sustained sensory perturbations.

Cortical connectivity and sensory coding

The sensory cortex contains a wide array of neuronal types, which are connected together into complex but partially stereotyped circuits. Sensory stimuli trigger cascades of electrical activity through these circuits, causing specific features of sensory scenes to be encoded in the firing patterns of cortical populations. Recent research is beginning to reveal how the connectivity of individual neurons relates to the sensory features they encode, how differences in the connectivity patterns of different cortical cell classes enable them to encode information using different strategies, and how feedback connections from higher-order cortex allow sensory information to be integrated with behavioural context.

Parvalbumin-expressing inhibitory interneurons in auditory cortex are well-tuned for frequency

In the auditory cortex, synaptic inhibition is known to be involved in shaping receptive fields, enhancing temporal precision, and regulating gain. Cortical inhibition is provided by local GABAergic interneurons, which comprise 10-20% of the cortical population and can be separated into numerous subclasses. The morphological and physiological diversity of interneurons suggests that these different subclasses have unique roles in sound processing; however, these roles are yet unknown. Understanding the receptive field properties of distinct inhibitory cell types will be critical to elucidating their computational function in cortical circuits. Here we characterized the tuning and response properties of parvalbumin-positive (PV+) interneurons, the largest inhibitory subclass. We used channelrhodopsin-2 (ChR2) as an optogenetic tag to identify PV+ and PV- neurons in vivo in transgenic mice. In contrast to PV+ neurons in mouse visual cortex, which are broadly tuned for orientation, we found that auditory cortical PV+ neurons were well tuned for frequency, although very tightly tuned PV+ cells were uncommon. This suggests that PV+ neurons play a minor role in shaping frequency tuning, and is consistent with the idea that PV+ neurons nonselectively pool input from the local network. PV+ interneurons had shallower response gain and were less intensity-tuned than PV- neurons, suggesting that PV+ neurons provide dynamic gain control and shape intensity tuning in auditory cortex. PV+ neurons also had markedly faster response latencies than PV- neurons, consistent with a computational role in enhancing the temporal precision of cortical responses.

Layer 4 in primary visual cortex of the awake rabbit: contrasting properties of simple cells and putative feedforward inhibitory interneurons

Extracellular recordings were obtained from two cell classes in layer 4 of the awake rabbit primary visual cortex (V1): putative inhibitory interneurons [suspected inhibitory interneurons (SINs)] and putative excitatory cells with simple receptive fields. SINs were identified solely by their characteristic response to electrical stimulation of the lateral geniculate nucleus (LGN, 3+ spikes at >600 Hz), and simple cells were identified solely by receptive field structure, requiring spatially separate ON and/or OFF subfields. Notably, no cells met both criteria, and we studied 62 simple cells and 33 SINs. Fourteen cells met neither criterion. These layer 4 populations were markedly distinct. Thus, SINs were far less linear (F1/F0 < 1), more broadly tuned to stimulus orientation, direction, spatial and temporal frequency, more sensitive to contrast, had much higher spontaneous and stimulus-driven activity, and always had spatially overlapping ON/OFF receptive subfields. SINs responded to drifting gratings with increased firing rates (F0) for all orientations and directions. However, some SINs showed a weaker modulated (F1) response sharply tuned to orientation and/or direction. SINs responded at shorter latencies than simple cells to stationary stimuli, and the responses of both populations could be sustained or transient. Transient simple cells were more sensitive to contrast than sustained simple cells and their visual responses were more frequently suppressed by high contrasts. Finally, cross-correlation between LGN and SIN spike trains confirmed a fast and precisely timed monosynaptic connectivity, supporting the notion that SINs are well suited to provide a fast feedforward inhibition onto targeted cortical populations.

Cortical inhibition reduces information redundancy at presentation of communication sounds in the primary auditory cortex

In all sensory modalities, intracortical inhibition shapes the functional properties of cortical neurons but also influences the responses to natural stimuli. Studies performed in various species have revealed that auditory cortex neurons respond to conspecific vocalizations by temporal spike patterns displaying a high trial-to-trial reliability, which might result from precise timing between excitation and inhibition. Studying the guinea pig auditory cortex, we show that partial blockage of GABAA receptors by gabazine (GBZ) application (10 μm, a concentration that promotes expansion of cortical receptive fields) increased the evoked firing rate and the spike-timing reliability during presentation of communication sounds (conspecific and heterospecific vocalizations), whereas GABAB receptor antagonists [10 μm saclofen; 10-50 μm CGP55845 (p-3-aminopropyl-p-diethoxymethyl phosphoric acid)] had nonsignificant effects. Computing mutual information (MI) from the responses to vocalizations using either the evoked firing rate or the temporal spike patterns revealed that GBZ application increased the MI derived from the activity of single cortical site but did not change the MI derived from population activity. In addition, quantification of information redundancy showed that GBZ significantly increased redundancy at the population level. This result suggests that a potential role of intracortical inhibition is to reduce information redundancy during the processing of natural stimuli.

Tracing inputs to inhibitory or excitatory neurons of mouse and cat visual cortex with a targeted rabies virus

BACKGROUND

Cortical inhibition plays a critical role in controlling and modulating cortical excitation, and a more detailed understanding of the neuronal circuits contributing to each will provide more insight into their roles in complex cortical computations. Traditional neuronal tracers lack a means for easily distinguishing between circuits of inhibitory and excitatory neurons. To overcome this limitation, we have developed a technique for retrogradely labeling inputs to local clusters of inhibitory or excitatory neurons, but not both, using neurotropic adenoassociated and lentiviral vectors, cell-type-specific promoters, and a modified rabies virus.

RESULTS

Applied to primary visual cortex (V1) in mouse, the cell-type-specific tracing technique labeled thousands of presynaptically connected neurons and revealed that the dominant source of input to inhibitory and excitatory neurons is local in origin. Neurons in other visual areas are also labeled; the percentage of these intercortical inputs to excitatory neurons is somewhat higher (~20%) than to inhibitory neurons (<10%), suggesting that intercortical connections have less direct control over inhibition. The inputs to inhibitory neurons were also traced in cat V1, and when aligned with the orientation preference map revealed for the first time that long-range inputs to inhibitory neurons are well tuned to orientation.

CONCLUSIONS

These novel findings for inhibitory and excitatory circuits in the visual cortex demonstrate the efficacy of our new technique and its ability to work across species, including larger-brained mammals such as the cat. This paves the way for a better understanding of the roles of specific cell types in higher-order perceptual and cognitive processes.

Synaptic mechanisms underlying functional dichotomy between intrinsic-bursting and regular-spiking neurons in auditory cortical layer 5

Corticofugal projections from the primary auditory cortex (A1) have been shown to play a role in modulating subcortical processing. However, functional properties of the corticofugal neurons and their synaptic circuitry mechanisms remain unclear. In this study, we performed in vivo whole-cell recordings from layer 5 (L5) pyramidal neurons in the rat A1 and found two distinct neuronal classes according to their functional properties. Intrinsic-bursting (IB) neurons, the L5 corticofugal neurons, exhibited early and rather unselective spike responses to a wide range of frequencies. The exceptionally broad spectral tuning of IB neurons was attributable to their broad excitatory inputs with long temporal durations and inhibitory inputs being more narrowly tuned than excitatory inputs. This uncommon pattern of excitatory-inhibitory interplay was attributed initially to a broad thalamocortical convergence onto IB neurons, which also receive temporally prolonged intracortical excitatory input as well as feedforward inhibitory input at least partially from more narrowly tuned fast-spiking inhibitory neurons. In contrast, regular-spiking neurons, which are mainly corticocortical, exhibited sharp frequency tuning similar to L4 pyramidal cells, underlying which are well-matched purely intracortical excitation and inhibition. The functional dichotomy among L5 pyramidal neurons suggests two distinct processing streams. The spectrally and temporally broad synaptic integration in IB neurons may ensure robust feedback signals to facilitate subcortical function and plasticity in a general manner.

Computing complex visual features with retinal spike times

Neurons in sensory systems can represent information not only by their firing rate, but also by the precise timing of individual spikes. For example, certain retinal ganglion cells, first identified in the salamander, encode the spatial structure of a new image by their first-spike latencies. Here we explore how this temporal code can be used by downstream neural circuits for computing complex features of the image that are not available from the signals of individual ganglion cells. To this end, we feed the experimentally observed spike trains from a population of retinal ganglion cells to an integrate-and-fire model of post-synaptic integration. The synaptic weights of this integration are tuned according to the recently introduced tempotron learning rule. We find that this model neuron can perform complex visual detection tasks in a single synaptic stage that would require multiple stages for neurons operating instead on neural spike counts. Furthermore, the model computes rapidly, using only a single spike per afferent, and can signal its decision in turn by just a single spike. Extending these analyses to large ensembles of simulated retinal signals, we show that the model can detect the orientation of a visual pattern independent of its phase, an operation thought to be one of the primitives in early visual processing. We analyze how these computations work and compare the performance of this model to other schemes for reading out spike-timing information. These results demonstrate that the retina formats spatial information into temporal spike sequences in a way that favors computation in the time domain. Moreover, complex image analysis can be achieved already by a simple integrate-and-fire model neuron, emphasizing the power and plausibility of rapid neural computing with spike times.

Mechanisms of neuronal computation in mammalian visual cortex

Orientation selectivity in the primary visual cortex (V1) is a receptive field property that is at once simple enough to make it amenable to experimental and theoretical approaches and yet complex enough to represent a significant transformation in the representation of the visual image. As a result, V1 has become an area of choice for studying cortical computation and its underlying mechanisms. Here we consider the receptive field properties of the simple cells in cat V1--the cells that receive direct input from thalamic relay cells--and explore how these properties, many of which are highly nonlinear, arise. We have found that many receptive field properties of V1 simple cells fall directly out of Hubel and Wiesel's feedforward model when the model incorporates realistic neuronal and synaptic mechanisms, including threshold, synaptic depression, response variability, and the membrane time constant.

Local circuit inhibition in the cerebral cortex as the source of gain control and untuned suppression

Theoretical considerations have led to the concept that the cerebral cortex is operating in a balanced state in which synaptic excitation is approximately balanced by synaptic inhibition from the local cortical circuit. This paper is about the functional consequences of the balanced state in sensory cortex. One consequence is gain control: there is experimental evidence and theoretical support for the idea that local circuit inhibition acts as a local automatic gain control throughout the cortex. Second, inhibition increases cortical feature selectivity: many studies of different sensory cortical areas have reported that suppressive mechanisms contribute to feature selectivity. Synaptic inhibition from the local microcircuit should be untuned (or broadly tuned) for stimulus features because of the microarchitecture of the cortical microcircuit. Untuned inhibition probably is the source of Untuned Suppression that enhances feature selectivity. We studied inhibition's function in our experiments, guided by a neuronal network model, on orientation selectivity in the primary visual cortex, V1, of the Macaque monkey. Our results revealed that Untuned Suppression, generated by local circuit inhibition, is crucial for the generation of highly orientation-selective cells in V1 cortex.

Visual orientation and directional selectivity through thalamic synchrony

Thalamic neurons respond to visual scenes by generating synchronous spike trains on the timescale of 10-20 ms that are very effective at driving cortical targets. Here we demonstrate that this synchronous activity contains unexpectedly rich information about fundamental properties of visual stimuli. We report that the occurrence of synchronous firing of cat thalamic cells with highly overlapping receptive fields is strongly sensitive to the orientation and the direction of motion of the visual stimulus. We show that this stimulus selectivity is robust, remaining relatively unchanged under different contrasts and temporal frequencies (stimulus velocities). A computational analysis based on an integrate-and-fire model of the direct thalamic input to a layer 4 cortical cell reveals a strong correlation between the degree of thalamic synchrony and the nonlinear relationship between cortical membrane potential and the resultant firing rate. Together, these findings suggest a novel population code in the synchronous firing of neurons in the early visual pathway that could serve as the substrate for establishing cortical representations of the visual scene.

Stimulus selectivity and response latency in putative inhibitory and excitatory neurons of the primate inferior temporal cortex

The cerebral cortex is composed of many distinct classes of neurons. Numerous studies have demonstrated corresponding differences in neuronal properties across cell types, but these comparisons have largely been limited to conditions outside of awake, behaving animals. Thus the functional role of the various cell types is not well understood. Here, we investigate differences in the functional properties of two widespread and broad classes of cells in inferior temporal cortex of macaque monkeys: inhibitory interneurons and excitatory projection cells. Cells were classified as putative inhibitory or putative excitatory neurons on the basis of their extracellular waveform characteristics (e.g., spike duration). Consistent with previous intracellular recordings in cortical slices, putative inhibitory neurons had higher spontaneous firing rates and higher stimulus-evoked firing rates than putative excitatory neurons. Additionally, putative excitatory neurons were more susceptible to spike waveform adaptation following very short interspike intervals. Finally, we compared two functional properties of each neuron's stimulus-evoked response: stimulus selectivity and response latency. First, putative excitatory neurons showed stronger stimulus selectivity compared with putative inhibitory neurons. Second, putative inhibitory neurons had shorter response latencies compared with putative excitatory neurons. Selectivity differences were maintained and latency differences were enhanced during a visual search task emulating more natural viewing conditions. Our results suggest that short-latency inhibitory responses are likely to sculpt visual processing in excitatory neurons, yielding a sparser visual representation.

Effects of long-term visual experience on responses of distinct classes of single units in inferior temporal cortex

Primates can learn to recognize a virtually limitless number of visual objects. A candidate neural substrate for this adult plasticity is the inferior temporal cortex (ITC). Using a large stimulus set, we explored the impact that long-term experience has on the response properties of two classes of neurons in ITC: broad-spiking (putative excitatory) cells and narrow-spiking (putative inhibitory) cells. We found that experience increased maximum responses of putative excitatory neurons but had the opposite effect on maximum responses of putative inhibitory neurons, an observation that helps to reconcile contradictory reports regarding the presence and direction of this effect. In addition, we found that experience reduced the average stimulus-evoked response in both cell classes, but this decrease was much more pronounced in putative inhibitory units. This latter finding supports a potentially critical role of inhibitory neurons in detecting and initiating the cascade of events underlying adult neural plasticity in ITC.

Inferring the role of inhibition in auditory processing of complex natural stimuli

Intracellular studies have revealed the importance of cotuned excitatory and inhibitory inputs to neurons in auditory cortex, but typical spectrotemporal receptive field models of neuronal processing cannot account for this overlapping tuning. Here, we apply a new nonlinear modeling framework to extracellular data recorded from primary auditory cortex (A1) that enables us to explore how the interplay of excitation and inhibition contributes to the processing of complex natural sounds. The resulting description produces more accurate predictions of observed spike trains than the linear spectrotemporal model, and the properties of excitation and inhibition inferred by the model are furthermore consistent with previous intracellular observations. It can also describe several nonlinear properties of A1 that are not captured by linear models, including intensity tuning and selectivity to sound onsets and offsets. These results thus offer a broader picture of the computational role of excitation and inhibition in A1 and support the hypothesis that their interactions play an important role in the processing of natural auditory stimuli.

Parvalbumin-expressing interneurons linearly transform cortical responses to visual stimuli

The response of cortical neurons to a sensory stimulus is shaped by the network in which they are embedded. Here we establish a role of parvalbumin (PV)-expressing cells, a large class of inhibitory neurons that target the soma and perisomatic compartments of pyramidal cells, in controlling cortical responses. By bidirectionally manipulating PV cell activity in visual cortex we show that these neurons strongly modulate layer 2/3 pyramidal cell spiking responses to visual stimuli while only modestly affecting their tuning properties. PV cells' impact on pyramidal cells is captured by a linear transformation, both additive and multiplicative, with a threshold. These results indicate that PV cells are ideally suited to modulate cortical gain and establish a causal relationship between a select neuron type and specific computations performed by the cortex during sensory processing.

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.

Sensitivity to temporal modulation rate and spectral bandwidth in the human auditory system: fMRI evidence

Hierarchical models of auditory processing often posit that optimal stimuli, i.e., those eliciting a maximal neural response, will increase in bandwidth and decrease in modulation rate as one ascends the auditory neuraxis. Here, we tested how bandwidth and modulation rate interact at several loci along the human central auditory pathway using functional MRI in a cardiac-gated, sparse acquisition design. Participants listened passively to both narrowband (NB) and broadband (BB) carriers (1/4- or 4-octave pink noise), which were jittered about a mean sinusoidal amplitude modulation rate of 0, 3, 29, or 57 Hz. The jittering was introduced to minimize stimulus-specific adaptation. The results revealed a clear difference between spectral bandwidth and temporal modulation rate: sensitivity to bandwidth (BB > NB) decreased from subcortical structures to nonprimary auditory cortex, whereas sensitivity to slow modulation rates was largest in nonprimary auditory cortex and largely absent in subcortical structures. Furthermore, there was no parametric interaction between bandwidth and modulation rate. These results challenge simple hierarchical models, in that BB stimuli evoked stronger responses in primary auditory cortex (and subcortical structures) rather than nonprimary cortex. Furthermore, the strong preference for slow modulation rates in nonprimary cortex demonstrates the compelling global sensitivity of auditory cortex to modulation rates that are dominant in the principal signals that we process, e.g., speech.

How inhibition shapes cortical activity

Cortical processing reflects the interplay of synaptic excitation and synaptic inhibition. Rapidly accumulating evidence is highlighting the crucial role of inhibition in shaping spontaneous and sensory-evoked cortical activity and thus underscores how a better knowledge of inhibitory circuits is necessary for our understanding of cortical function. We discuss current views of how inhibition regulates the function of cortical neurons and point to a number of important open questions.

Flexible, foldable, actively multiplexed, high-density electrode array for mapping brain activity in vivo

Arrays of electrodes for recording and stimulating the brain are used throughout clinical medicine and basic neuroscience research, yet are unable to sample large areas of the brain while maintaining high spatial resolution because of the need to individually wire each passive sensor at the electrode-tissue interface. To overcome this constraint, we developed new devices that integrate ultrathin and flexible silicon nanomembrane transistors into the electrode array, enabling new dense arrays of thousands of amplified and multiplexed sensors that are connected using fewer wires. We used this system to record spatial properties of cat brain activity in vivo, including sleep spindles, single-trial visual evoked responses and electrographic seizures. We found that seizures may manifest as recurrent spiral waves that propagate in the neocortex. The developments reported here herald a new generation of diagnostic and therapeutic brain-machine interface devices.

Temporal precision in the visual pathway through the interplay of excitation and stimulus-driven suppression

Visual neurons can respond with extremely precise temporal patterning to visual stimuli that change on much slower time scales. Here, we investigate how the precise timing of cat thalamic spike trains-which can have timing as precise as 1 ms-is related to the stimulus, in the context of both artificial noise and natural visual stimuli. Using a nonlinear modeling framework applied to extracellular data, we demonstrate that the precise timing of thalamic spike trains can be explained by the interplay between an excitatory input and a delayed suppressive input that resembles inhibition, such that neuronal responses only occur in brief windows where excitation exceeds suppression. The resulting description of thalamic computation resembles earlier models of contrast adaptation, suggesting a more general role for mechanisms of contrast adaptation in visual processing. Thus, we describe a more complex computation underlying thalamic responses to artificial and natural stimuli that has implications for understanding how visual information is represented in the early stages of visual processing.

Synchronous chaos and broad band gamma rhythm in a minimal multi-layer model of primary visual cortex

Visually induced neuronal activity in V1 displays a marked gamma-band component which is modulated by stimulus properties. It has been argued that synchronized oscillations contribute to these gamma-band activity. However, analysis of Local Field Potentials (LFPs) across different experiments reveals considerable diversity in the degree of oscillatory behavior of this induced activity. Contrast-dependent power enhancements can indeed occur over a broad band in the gamma frequency range and spectral peaks may not arise at all. Furthermore, even when oscillations are observed, they undergo temporal decorrelation over very few cycles. This is not easily accounted for in previous network modeling of gamma oscillations. We argue here that interactions between cortical layers can be responsible for this fast decorrelation. We study a model of a V1 hypercolumn, embedding a simplified description of the multi-layered structure of the cortex. When the stimulus contrast is low, the induced activity is only weakly synchronous and the network resonates transiently without developing collective oscillations. When the contrast is high, on the other hand, the induced activity undergoes synchronous oscillations with an irregular spatiotemporal structure expressing a synchronous chaotic state. As a consequence the population activity undergoes fast temporal decorrelation, with concomitant rapid damping of the oscillations in LFPs autocorrelograms and peak broadening in LFPs power spectra. We show that the strength of the inter-layer coupling crucially affects this spatiotemporal structure. We predict that layer VI inactivation should induce global changes in the spectral properties of induced LFPs, reflecting their slower temporal decorrelation in the absence of inter-layer feedback. Finally, we argue that the mechanism underlying the emergence of synchronous chaos in our model is in fact very general. It stems from the fact that gamma oscillations induced by local delayed inhibition tend to develop chaos when coupled by sufficiently strong excitation.

Visual stimulation elicits neuronal responses in visual cortex. When the contrast of the used stimuli increases, the power of this induced activity is boosted over a broad frequency range (30–100 Hz), called the “gamma band.” It would be tempting to hypothesize that this phenomenon is due to the emergence of oscillations in which many neurons fire collectively in a rhythmic way. However, previous models trying to explain contrast-related power enhancements using synchronous oscillations failed to reproduce the observed spectra because they originated unrealistically sharp spectral peaks. The aim of our study is to reconcile synchronous oscillations with broad-band power spectra. We argue here that, thanks to the interaction between neuronal populations at different depths in the cortical tissue, the induced oscillatory responses are synchronous, but, at the same time, chaotic. The chaotic nature of the dynamics makes it possible to have broad-band power spectra together with synchrony. Our modeling study allows us formulating qualitative experimental predictions that provide a potential test for our theory. We predict that if the interactions between cortical layers are suppressed, for instance by inactivating neurons in deep layers, the induced responses might become more regular and narrow isolated peaks might develop in their power spectra.

Dissecting local circuits in vivo: integrated optogenetic and electrophysiology approaches for exploring inhibitory regulation of cortical activity

Local cortical circuit activity in vivo comprises a complex and flexible series of interactions between excitatory and inhibitory neurons. Our understanding of the functional interactions between these different neural populations has been limited by the difficulty of identifying and selectively manipulating the diverse and sparsely represented inhibitory interneuron classes in the intact brain. The integration of recently developed optical tools with traditional electrophysiological techniques provides a powerful window into the role of inhibition in regulating the activity of excitatory neurons. In particular, optogenetic targeting of specific cell classes reveals the distinct impacts of local inhibitory populations on other neurons in the surrounding local network. In addition to providing the ability to activate or suppress spiking in target cells, optogenetic activation identifies extracellularly recorded neurons by class, even when naturally occurring spike rates are extremely low. However, there are several important limitations on the use of these tools and the interpretation of resulting data. The purpose of this article is to outline the uses and limitations of optogenetic tools, along with current methods for achieving cell type-specific expression, and to highlight the advantages of an experimental approach combining optogenetics and electrophysiology to explore the role of inhibition in active networks. To illustrate the efficacy of these combined approaches, I present data comparing targeted manipulations of cortical fast-spiking, parvalbumin-expressing and low threshold-spiking, somatostatin-expressing interneurons in vivo.

What can mice tell us about how vision works?

Understanding the neural basis of visual perception is a long-standing fundamental goal of neuroscience. Historically, most vision studies were carried out on humans, macaques and cats. Over the past 5 years, however, a growing number of researchers have begun using mice to parse the mechanisms underlying visual processing; the rationale is that, despite having relatively poor acuity, mice are unmatched in terms of the variety and sophistication of tools available to label, monitor and manipulate specific cell types and circuits. In this review, we discuss recent advances in understanding the mouse visual system at the anatomical, receptive field and perceptual level, focusing on the opportunities and constraints those features provide toward the goal of understanding how vision works.