Orientation tuning of input conductance, excitation, and inhibition in cat primary visual cortex

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.

Tuned thalamic excitation is amplified by visual cortical circuits

Cortical neurons in thalamic recipient layers receive excitation from the thalamus and the cortex. The relative contribution of these two sources of excitation to sensory tuning is poorly understood. We optogenetically silenced the visual cortex of mice to isolate thalamic excitation onto layer 4 neurons during visual stimulation. Thalamic excitation contributed to a third of the total excitation and was organized in spatially offset, yet overlapping, ON and OFF receptive fields. This receptive field structure predicted the orientation tuning of thalamic excitation. Finally, both thalamic and total excitation were similarly tuned to orientation and direction and had the same temporal phase relationship to the visual stimulus. Our results indicate that tuning of thalamic excitation is unlikely to be imparted by direction- or orientation-selective thalamic neurons and that a principal role of cortical circuits is to amplify tuned thalamic excitation.

Attention-dependent reductions in burstiness and action-potential height in macaque area V4

Attention improves the encoding of visual stimuli. One mechanism that is implicated in facilitating sensory encoding is the firing of action potentials in bursts. We tested the hypothesis that when spatial attention is directed to a stimulus, this causes an increase in burst firing to the attended stimulus. To the contrary, we found an attention-dependent reduction in burstiness among putative pyramidal neurons in macaque area V4. We accounted for this using a conductance-based Hodgkin-Huxley style model in which attentional modulation stems from scaling excitation and inhibition. The model exhibited attention-dependent increases in firing rate and made the surprising and correct prediction that when attention is directed into a neuron’s receptive field, this reduces action potential height. The model thus provided a unified explanation for three distinct forms of attentional modulation, two of them novel, and implicates scaling of the responses of excitatory and inhibitory input populations in mediating attention.

Distribution of correlated spiking events in a population-based approach for Integrate-and-Fire networks

Randomly connected populations of spiking neurons display a rich variety of dynamics. However, much of the current modeling and theoretical work has focused on two dynamical extremes: on one hand homogeneous dynamics characterized by weak correlations between neurons, and on the other hand total synchrony characterized by large populations firing in unison. In this paper we address the conceptual issue of how to mathematically characterize the partially synchronous "multiple firing events" (MFEs) which manifest in between these two dynamical extremes. We further develop a geometric method for obtaining the distribution of magnitudes of these MFEs by recasting the cascading firing event process as a first-passage time problem, and deriving an analytical approximation of the first passage time density valid for large neuron populations. Thus, we establish a direct link between the voltage distributions of excitatory and inhibitory neurons and the number of neurons firing in an MFE that can be easily integrated into population-based computational methods, thereby bridging the gap between homogeneous firing regimes and total synchrony.

Synchronisation hubs in the visual cortex may arise from strong rhythmic inhibition during gamma oscillations

Neurons in the visual cortex exhibit heterogeneity in feature selectivity and the tendency to generate action potentials synchronously with other nearby neurons. By examining visual responses from cat area 17 we found that, during gamma oscillations, there was a positive correlation between each unit's sharpness of orientation tuning, strength of oscillations, and propensity towards synchronisation with other units. Using a computational model, we demonstrated that heterogeneity in the strength of rhythmic inhibitory inputs can account for the correlations between these three properties. Neurons subject to strong inhibition tend to oscillate strongly in response to both optimal and suboptimal stimuli and synchronise promiscuously with other neurons, even if they have different orientation preferences. Moreover, these strongly inhibited neurons can exhibit sharp orientation selectivity provided that the inhibition they receive is broadly tuned relative to their excitatory inputs. These results predict that the strength and orientation tuning of synaptic inhibition are heterogeneous across area 17 neurons, which could have important implications for these neurons' sensory processing capabilities. Furthermore, although our experimental recordings were conducted in the visual cortex, our model and simulation results can apply more generally to any brain region with analogous neuron types in which heterogeneity in the strength of rhythmic inhibition can arise during gamma oscillations.

Inhibitory interneurons decorrelate excitatory cells to drive sparse code formation in a spiking model of V1

Sparse coding models of natural scenes can account for several physiological properties of primary visual cortex (V1), including the shapes of simple cell receptive fields (RFs) and the highly kurtotic firing rates of V1 neurons. Current spiking network models of pattern learning and sparse coding require direct inhibitory connections between the excitatory simple cells, in conflict with the physiological distinction between excitatory (glutamatergic) and inhibitory (GABAergic) neurons (Dale's Law). At the same time, the computational role of inhibitory neurons in cortical microcircuit function has yet to be fully explained. Here we show that adding a separate population of inhibitory neurons to a spiking model of V1 provides conformance to Dale's Law, proposes a computational role for at least one class of interneurons, and accounts for certain observed physiological properties in V1. When trained on natural images, this excitatory-inhibitory spiking circuit learns a sparse code with Gabor-like RFs as found in V1 using only local synaptic plasticity rules. The inhibitory neurons enable sparse code formation by suppressing predictable spikes, which actively decorrelates the excitatory population. The model predicts that only a small number of inhibitory cells is required relative to excitatory cells and that excitatory and inhibitory input should be correlated, in agreement with experimental findings in visual cortex. We also introduce a novel local learning rule that measures stimulus-dependent correlations between neurons to support "explaining away" mechanisms in neural coding.

Analysis of the stabilized supralinear network

We study a rate-model neural network composed of excitatory and inhibitory neurons in which neuronal input-output functions are power laws with a power greater than 1, as observed in primary visual cortex. This supralinear input-output function leads to supralinear summation of network responses to multiple inputs for weak inputs. We show that for stronger inputs, which would drive the excitatory subnetwork to instability, the network will dynamically stabilize provided feedback inhibition is sufficiently strong. For a wide range of network and stimulus parameters, this dynamic stabilization yields a transition from supralinear to sublinear summation of network responses to multiple inputs. We compare this to the dynamic stabilization in the balanced network, which yields only linear behavior. We more exhaustively analyze the two-dimensional case of one excitatory and one inhibitory population. We show that in this case, dynamic stabilization will occur whenever the determinant of the weight matrix is positive and the inhibitory time constant is sufficiently small, and analyze the conditions for supersaturation, or decrease of firing rates with increasing stimulus contrast (which represents increasing input firing rates). In work to be presented elsewhere, we have found that this transition from supralinear to sublinear summation can explain a wide variety of nonlinearities in cerebral cortical processing.

Emergent dynamics in a model of visual cortex

This paper proposes that the network dynamics of the mammalian visual cortex are highly structured and strongly shaped by temporally localized barrages of excitatory and inhibitory firing we call ‘multiple-firing events’ (MFEs). Our proposal is based on careful study of a network of spiking neurons built to reflect the coarse physiology of a small patch of layer 2/3 of V1. When appropriately benchmarked this network is capable of reproducing the qualitative features of a range of phenomena observed in the real visual cortex, including spontaneous background patterns, orientation-specific responses, surround suppression and gamma-band oscillations. Detailed investigation into the relevant regimes reveals causal relationships among dynamical events driven by a strong competition between the excitatory and inhibitory populations. It suggests that along with firing rates, MFE characteristics can be a powerful signature of a regime. Testable predictions based on model observations and dynamical analysis are proposed.

No consistent relationship between gamma power and peak frequency in macaque primary visual cortex

Neural activity in the gamma frequency range ("gamma") is elevated during active cognitive states. Gamma has been proposed to play an important role in cortical function, although this is debated. Understanding what function gamma might fulfill requires a better understanding of its properties and the mechanisms that generate it. Gamma is characterized by its spectral power and peak frequency, and variations in both parameters have been associated with changes in behavioral performance. Modeling studies suggest these properties are co-modulated, but this has not been established. To test the relationship between these properties, we measured local field potentials (LFPs) and neuronal spiking responses in primary visual cortex of anesthetized monkeys, for drifting sinusoidal gratings of different sizes, contrasts, orientations and masked with different levels of noise. We find that there is no fixed relationship between LFP gamma power and peak frequency, and neither is related to the strength of spiking activity. We propose a simple model that can account for the complex stimulus dependence we observe, and suggest that separate mechanisms determine gamma power and peak frequency.

Generation of intensity selectivity by differential synaptic tuning: fast-saturating excitation but slow-saturating inhibition

Intensity defines one fundamental aspect of sensory information and is specifically represented in each sensory modality. Interestingly, only in the central auditory system are intensity-selective neurons evolved. These neurons are characterized by nonmonotonic response-level functions. The synaptic circuitry mechanisms underlying the generation of intensity selectivity from nonselective auditory nerve inputs remain largely unclear. Here, we performed in vivo whole-cell recordings from pyramidal neurons in the rat dorsal cochlear nucleus (DCN), where intensity selectivity first emerges along the auditory neuraxis. Our results revealed that intensity-selective cells received fast-saturating excitation but slow-saturating inhibition with intensity increments, whereas in intensity-nonselective cells excitation and inhibition were similarly slow-saturating. The differential intensity tuning profiles of the monotonic excitation and inhibition qualitatively determined the intensity selectivity of output responses. In addition, the selectivity was further strengthened by significantly lower excitation/inhibition ratios at high-intensity levels compared with intensity-nonselective neurons. Our results demonstrate that intensity selectivity in the DCN is generated by extracting the difference between tuning profiles of nonselective excitatory and inhibitory inputs, which we propose can be achieved through a differential circuit mediated by feedforward inhibition.

Local inhibition modulates learning-dependent song encoding in the songbird auditory cortex

Changes in inhibition during development are well documented, but the role of inhibition in adult learning-related plasticity is not understood. In songbirds, vocal recognition learning alters the neural representation of songs across the auditory forebrain, including the caudomedial nidopallium (NCM), a region analogous to mammalian secondary auditory cortices. Here, we block local inhibition with the iontophoretic application of gabazine, while simultaneously measuring song-evoked spiking activity in NCM of European starlings trained to recognize sets of conspecific songs. We find that local inhibition differentially suppresses the responses to learned and unfamiliar songs and enhances spike-rate differences between learned categories of songs. These learning-dependent response patterns emerge, in part, through inhibitory modulation of selectivity for song components and the masking of responses to specific acoustic features without altering spectrotemporal tuning. The results describe a novel form of inhibitory modulation of the encoding of learned categories and demonstrate that inhibition plays a central role in shaping the responses of neurons to learned, natural signals.

Dynamics of spiking neurons: between homogeneity and synchrony

Randomly connected networks of neurons driven by Poisson inputs are often assumed to produce "homogeneous" dynamics, characterized by largely independent firing and approximable by diffusion processes. At the same time, it is well known that such networks can fire synchronously. Between these two much studied scenarios lies a vastly complex dynamical landscape that is relatively unexplored. In this paper, we discuss a phenomenon which commonly manifests in these intermediate regimes, namely brief spurts of spiking activity which we call multiple firing events (MFE). These events do not depend on structured network architecture nor on structured input; they are an emergent property of the system. We came upon them in an earlier modeling paper, in which we discovered, through a careful benchmarking process, that MFEs are the single most important dynamical mechanism behind many of the V1 phenomena we were able to replicate. In this paper we explain in a simpler setting how MFEs come about, as well as their potential dynamic consequences. Although the mechanism underlying MFEs cannot easily be captured by current population dynamics models, this phenomena should not be ignored during analysis; there is a growing body of evidence that such collaborative activity may be a key towards unlocking the possible functional properties of many neuronal networks.

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.

Division and subtraction by distinct cortical inhibitory networks in vivo

Brain circuits process information through specialized neuronal subclasses interacting within a network. Revealing their interplay requires activating specific cells while monitoring others in a functioning circuit. Here we use a new platform for two-way light-based circuit interrogation in visual cortex in vivo to show the computational implications of modulating different subclasses of inhibitory neurons during sensory processing. We find that soma-targeting, parvalbumin-expressing (PV) neurons principally divide responses but preserve stimulus selectivity, whereas dendrite-targeting, somatostatin-expressing (SOM) neurons principally subtract from excitatory responses and sharpen selectivity. Visualized in vivo cell-attached recordings show that division by PV neurons alters response gain, whereas subtraction by SOM neurons shifts response levels. Finally, stimulating identified neurons while scanning many target cells reveals that single PV and SOM neurons functionally impact only specific subsets of neurons in their projection fields. These findings provide direct evidence that inhibitory neuronal subclasses have distinct and complementary roles in cortical computations.

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.

In vivo voltage-dependent influences on summation of synaptic potentials in neurons of the lateral nucleus of the amygdala

The amygdala has a fundamental role in driving affective behaviors in response to sensory cues. To accomplish this, neurons of the lateral nucleus (LAT) must integrate a large number of synaptic inputs. A wide range of factors influence synaptic integration, including membrane potential, voltage-gated ion channels and GABAergic inhibition. However, little is known about how these factors modulate integration of synaptic inputs in LAT neurons in vivo. The purpose of this study was to determine the voltage-dependent factors that modify in vivo integration of synaptic inputs in the soma of LAT neurons. In vivo intracellular recordings from anesthetized rats were used to measure post-synaptic potentials (PSPs) and clusters of PSPs across a range of membrane potentials. These studies found that the relationship between membrane potential and PSP clusters was sublinear, due to a reduction of cluster amplitude and area at depolarized membrane potentials. In combination with intracellular delivery of pharmacological agents, it was found that the voltage-dependent suppression of PSP clusters was sensitive to tetraethylammonium (TEA), but not cesium or a blocker of fast GABAergic inhibition. These findings indicate that integration of PSPs in LAT neurons in vivo is strongly modified by somatic membrane potential, likely through voltage-dependent TEA-sensitive potassium channels. Conditions that lead to a shift in membrane potential, or a modulation of the number or function of these ion channels will lead to a more uniform capacity for integration across voltages, and perhaps greatly facilitate amygdala-dependent behaviors.

Feedforward origins of response variability underlying contrast invariant orientation tuning in cat visual cortex

Contrast invariant orientation tuning in simple cells of the visual cortex depends critically on contrast dependent trial-to-trial variability in their membrane potential responses. This observation raises the question of whether this variability originates from within the cortical circuit or the feedforward inputs from the lateral geniculate nucleus (LGN). To distinguish between these two sources of variability, we first measured membrane potential responses while inactivating the surrounding cortex, and found that response variability was nearly unaffected. We then studied variability in the LGN, including contrast dependence, and the trial-to-trial correlation in responses between nearby neurons. Variability decreased significantly with contrast, whereas correlation changed little. When these experimentally measured parameters of variability were applied to a feedforward model of simple cells that included realistic mechanisms of synaptic integration, contrast-dependent, orientation independent variability emerged in the membrane potential responses. Analogous mechanisms might contribute to the stimulus dependence and propagation of variability throughout the neocortex.

A calibration-free electrode compensation method

In a single-electrode current-clamp recording, the measured potential includes both the response of the membrane and that of the measuring electrode. The electrode response is traditionally removed using bridge balance, where the response of an ideal resistor representing the electrode is subtracted from the measurement. Because the electrode is not an ideal resistor, this procedure produces capacitive transients in response to fast or discontinuous currents. More sophisticated methods exist, but they all require a preliminary calibration phase, to estimate the properties of the electrode. If these properties change after calibration, the measurements are corrupted. We propose a compensation method that does not require preliminary calibration. Measurements are compensated offline by fitting a model of the neuron and electrode to the trace and subtracting the predicted electrode response. The error criterion is designed to avoid the distortion of compensated traces by spikes. The technique allows electrode properties to be tracked over time and can be extended to arbitrary models of electrode and neuron. We demonstrate the method using biophysical models and whole cell recordings in cortical and brain-stem neurons.

Surround suppression and sparse coding in visual and barrel cortices

During natural vision the entire retina is stimulated. Likewise, during natural tactile behaviors, spatially extensive regions of the somatosensory surface are co-activated. The large spatial extent of naturalistic stimulation means that surround suppression, a phenomenon whose neural mechanisms remain a matter of debate, must arise during natural behavior. To identify common neural motifs that might instantiate surround suppression across modalities, we review models of surround suppression and compare the evidence supporting the competing ideas that surround suppression has either cortical or sub-cortical origins in visual and barrel cortex. In the visual system there is general agreement lateral inhibitory mechanisms contribute to surround suppression, but little direct experimental evidence that intracortical inhibition plays a major role. Two intracellular recording studies of V1, one using naturalistic stimuli (Haider et al., 2010), the other sinusoidal gratings (Ozeki et al., 2009), sought to identify the causes of reduced activity in V1 with increasing stimulus size, a hallmark of surround suppression. The former attributed this effect to increased inhibition, the latter to largely balanced withdrawal of excitation and inhibition. In rodent primary somatosensory barrel cortex, multi-whisker responses are generally weaker than single whisker responses, suggesting multi-whisker stimulation engages similar surround suppressive mechanisms. The origins of suppression in S1 remain elusive: studies have implicated brainstem lateral/internuclear interactions and both thalamic and cortical inhibition. Although the anatomical organization and instantiation of surround suppression in the visual and somatosensory systems differ, we consider the idea that one common function of surround suppression, in both modalities, is to remove the statistical redundancies associated with natural stimuli by increasing the sparseness or selectivity of sensory responses.

Broadening of cortical inhibition mediates developmental sharpening of orientation selectivity

Orientation selectivity (OS) of visual cortical neurons is progressively sharpened during development. However, synaptic circuit mechanisms underlying the OS sharpening remain unclear. In the current study, in vivo whole-cell voltage-clamp recordings from layer 4 excitatory neurons in the developing mouse primary visual cortex revealed changes of orientation tuning profiles of their excitatory and inhibitory inputs during a post-eye-opening period when OS of their spiking responses becomes sharpened. In addition to a parallel strengthening of excitation and inhibition during this developmental period, the orientation tuning of excitatory inputs keeps relatively constant, whereas the tuning of inhibitory inputs is broadened, and becomes significantly broader than that of excitatory inputs. Neuron modeling and dynamic-clamp recording demonstrated that this developmental broadening of the inhibitory tuning is sufficient for sharpening OS. Depriving visual experience by dark rearing impedes the normal developmental strengthening of excitation, but a similar broadening of inhibitory tuning, likely caused by a nonselective strengthening of inhibitory connections, results in the apparently normal OS sharpening in excitatory neurons. Our results thus provide the first demonstration that an inhibitory synaptic mechanism can primarily mediate the functional refinement of cortical neurons.

The generation of direction selectivity in the auditory system

Both human speech and animal vocal signals contain frequency-modulated (FM) sounds. Although central auditory neurons that selectively respond to the direction of frequency modulation are known, the synaptic mechanisms underlying the generation of direction selectivity (DS) remain elusive. Here we show the emergence of DS neurons in the inferior colliculus by mapping the three major subcortical auditory nuclei. Cell-attached recordings reveal a highly reliable and precise firing of DS neurons to FM sweeps in a preferred direction. By using in vivo whole-cell current-clamp and voltage-clamp recordings, we found that the synaptic inputs to DS neurons are not direction selective, but temporally reversed excitatory and inhibitory synaptic inputs are evoked in response to opposing directions of FM sweeps. The construction of such temporal asymmetry, resulting DS, and its topography can be attributed to the spectral disparity of the excitatory and the inhibitory synaptic tonal receptive fields.

A multi-stage model for fundamental functional properties in primary visual cortex

Many neurons in mammalian primary visual cortex have properties such as sharp tuning for contour orientation, strong selectivity for motion direction, and insensitivity to stimulus polarity, that are not shared with their sub-cortical counterparts. Successful models have been developed for a number of these properties but in one case, direction selectivity, there is no consensus about underlying mechanisms. We here define a model that accounts for many of the empirical observations concerning direction selectivity. The model describes a single column of cat primary visual cortex and comprises a series of processing stages. Each neuron in the first cortical stage receives input from a small number of on-centre and off-centre relay cells in the lateral geniculate nucleus. Consistent with recent physiological evidence, the off-centre inputs to cortex precede the on-centre inputs by a small (∼4 ms) interval, and it is this difference that confers direction selectivity on model neurons. We show that the resulting model successfully matches the following empirical data: the proportion of cells that are direction selective; tilted spatiotemporal receptive fields; phase advance in the response to a stationary contrast-reversing grating stepped across the receptive field. The model also accounts for several other fundamental properties. Receptive fields have elongated subregions, orientation selectivity is strong, and the distribution of orientation tuning bandwidth across neurons is similar to that seen in the laboratory. Finally, neurons in the first stage have properties corresponding to simple cells, and more complex-like cells emerge in later stages. The results therefore show that a simple feed-forward model can account for a number of the fundamental properties of primary visual cortex.

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.

Estimating three synaptic conductances in a stochastic neural model

We present a method for the reconstruction of three stimulus-evoked time-varying synaptic input conductances from voltage recordings. Our approach is based on exploiting the stochastic nature of synaptic conductances and membrane voltage. Starting with the assumption that the variances of the conductances are known, we use a stochastic differential equation to model dynamics of membrane potential and derive equations for first and second moments that can be solved to find conductances. We successfully apply the new reconstruction method to simulated data. We also explore the robustness of the method as the assumptions of the underlying model are relaxed. We vary the noise levels, the reversal potentials, the number of stimulus repetitions, and the accuracy of conductance variance estimation to quantify the robustness of reconstruction. These studies pave the way for the application of the method to experimental data.

Untuned suppression makes a major contribution to the enhancement of orientation selectivity in macaque v1

One of the functions of the cerebral cortex is to increase the selectivity for stimulus features. Finding more about the mechanisms of increased cortical selectivity is important for understanding how the cortex works. Up to now, studies in multiple cortical areas have reported that suppressive mechanisms are involved in feature selectivity. However, the magnitude of the contribution of suppression to tuning selectivity is not yet determined. We use orientation selectivity in macaque primary visual cortex, V1, as an archetypal example of cortical feature selectivity and develop a method to estimate the magnitude of the contribution of suppression to orientation selectivity. The results show that untuned suppression, one form of cortical suppression, decreases the orthogonal-to-preferred response ratio (O/P ratio) of V1 cells from an average of 0.38 to 0.26. Untuned suppression has an especially large effect on orientation selectivity for highly selective cells (O/P < 0.2). Therefore, untuned suppression is crucial for the generation of highly orientation-selective cells in V1 cortex.

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.

Recurrent circuitry dynamically shapes the activation of piriform cortex

In the piriform cortex, individual odorants activate a unique ensemble of neurons that are distributed without discernable spatial order. Piriform neurons receive convergent excitatory inputs from random collections of olfactory bulb glomeruli. Pyramidal cells also make extensive recurrent connections with other excitatory and inhibitory neurons. We introduced channelrhodopsin into the piriform cortex to characterize these intrinsic circuits and to examine their contribution to activity driven by afferent bulbar inputs. We demonstrated that individual pyramidal cells are sparsely interconnected by thousands of excitatory synaptic connections that extend, largely undiminished, across the piriform cortex, forming a large excitatory network that can dominate the bulbar input. Pyramidal cells also activate inhibitory interneurons that mediate strong, local feedback inhibition that scales with excitation. This recurrent network can enhance or suppress bulbar input, depending on whether the input arrives before or after the cortex is activated. This circuitry may shape the ensembles of piriform cells that encode odorant identity.

Normalization as a canonical neural computation

There is increasing evidence that the brain relies on a set of canonical neural computations, repeating them across brain regions and modalities to apply similar operations to different problems. A promising candidate for such a computation is normalization, in which the responses of neurons are divided by a common factor that typically includes the summed activity of a pool of neurons. Normalization was developed to explain responses in the primary visual cortex and is now thought to operate throughout the visual system, and in many other sensory modalities and brain regions. Normalization may underlie operations such as the representation of odours, the modulatory effects of visual attention, the encoding of value and the integration of multisensory information. Its presence in such a diversity of neural systems in multiple species, from invertebrates to mammals, suggests that it serves as a canonical neural computation.

APPLICATION OF SICoNNETS TO HANDWRITTEN DIGIT RECOGNITION

In this paper, we apply a new neural network model, namely shunting inhibitory convolutional neural networks, or SICoNNets for short, to the problem of handwritten digit recognition. This type of networks has a generic and flexible architecture, where the processing is based on the physiologically plausible mechanism of shunting inhibition. A hybrid first-order training method, called QRProp, is developed based on the three training algorithms Rprop, Quickprop, and SuperSAB. The MNIST database is used to train and evaluate the performance of SICoNNets in handwritten digit recognition. A network with 24 feature maps and 2722 free parameters achieves a recognition accuracy of 97.3%.

Inferring synaptic inputs given a noisy voltage trace via sequential Monte Carlo methods

We discuss methods for optimally inferring the synaptic inputs to an electrotonically compact neuron, given intracellular voltage-clamp or current-clamp recordings from the postsynaptic cell. These methods are based on sequential Monte Carlo techniques ("particle filtering"). We demonstrate, on model data, that these methods can recover the time course of excitatory and inhibitory synaptic inputs accurately on a single trial. Depending on the observation noise level, no averaging over multiple trials may be required. However, excitatory inputs are consistently inferred more accurately than inhibitory inputs at physiological resting potentials, due to the stronger driving force associated with excitatory conductances. Once these synaptic input time courses are recovered, it becomes possible to fit (via tractable convex optimization techniques) models describing the relationship between the sensory stimulus and the observed synaptic input. We develop both parametric and nonparametric expectation-maximization (EM) algorithms that consist of alternating iterations between these synaptic recovery and model estimation steps. We employ a fast, robust convex optimization-based method to effectively initialize the filter; these fast methods may be of independent interest. The proposed methods could be applied to better understand the balance between excitation and inhibition in sensory processing in vivo.

Stimulus-induced dissociation of neuronal firing rates and local field potential gamma power and its relationship to the resonance blood oxygen level-dependent signal in macaque primary visual cortex

The functional magnetic resonance imaging (fMRI) blood oxygenation level-dependent (BOLD) signal is regularly used to assign neuronal activity to cognitive function. Recent analyses have shown that the local field potential (LFP) gamma power is a better predictor of the fMRI BOLD signal than spiking activity. However, LFP gamma power and spiking activity are usually correlated, clouding the analysis of the neural basis of the BOLD signal. We show that changes in LFP gamma power and spiking activity in the primary visual cortex (V1) of the awake primate can be dissociated by using grating and plaid pattern stimuli, which differentially engage surround suppression and cross-orientation inhibition/facilitation within and between cortical columns. Grating presentation yielded substantial V1 LFP gamma frequency oscillations and significant multi-unit activity. Plaid pattern presentation significantly reduced the LFP gamma power while increasing population multi-unit activity. The fMRI BOLD activity followed the LFP gamma power changes, not the multi-unit activity. Inference of neuronal activity from the fMRI BOLD signal thus requires detailed a priori knowledge of how different stimuli or tasks activate the cortical network.

Orientation selectivity of synaptic input to neurons in mouse and cat primary visual cortex

Primary visual cortex (V1) is the site at which orientation selectivity emerges in mammals: visual thalamus afferents to V1 respond equally to all stimulus orientations, whereas their target V1 neurons respond selectively to stimulus orientation. The emergence of orientation selectivity in V1 has long served as a model for investigating cortical computation. Recent evidence for orientation selectivity in mouse V1 opens cortical computation to dissection by genetic and imaging tools, but also raises two essential questions: (1) How does orientation selectivity in mouse V1 neurons compare with that in previously described species? (2) What is the synaptic basis for orientation selectivity in mouse V1? A comparison of orientation selectivity in mouse and in cat, where such measures have traditionally been made, reveals that orientation selectivity in mouse V1 is weaker than in cat V1, but that spike threshold plays a similar role in narrowing selectivity between membrane potential and spike rate. To uncover the synaptic basis for orientation selectivity, we made whole-cell recordings in vivo from mouse V1 neurons, comparing neuronal input selectivity-based on membrane potential, synaptic excitation, and synaptic inhibition-to output selectivity based on spiking. We found that a neuron's excitatory and inhibitory inputs are selective for the same stimulus orientations as is its membrane potential response, and that inhibitory selectivity is not broader than excitatory selectivity. Inhibition has different dynamics than excitation, adapting more rapidly. In neurons with temporally modulated responses, the timing of excitation and inhibition was different in mice and cats.

Intracellular recording in behaving animals

Electrophysiological recordings from behaving animals provide an unparalleled view into the functional role of individual neurons. Intracellular approaches can be especially revealing as they provide information about a neuron's inputs and intrinsic cellular properties, which together determine its spiking output. Recent technical developments have made intracellular recording possible during an ever-increasing range of behaviors in both head-fixed and freely moving animals. These recordings have yielded fundamental insights into the cellular and circuit mechanisms underlying neural activity during natural behaviors in such areas as sensory perception, motor sequence generation, and spatial navigation, forging a direct link between cellular and systems neuroscience.

Broad inhibition sharpens orientation selectivity by expanding input dynamic range in mouse simple cells

Orientation selectivity (OS) is an emergent property in the primary visual cortex (V1). How OS arises from synaptic circuits remains unsolved. Here, in vivo whole-cell recordings in the mouse V1 revealed that simple cells received broadly tuned excitation and even more broadly tuned inhibition. Excitation and inhibition shared a similar orientation preference and temporally overlapped substantially. Neuron modeling and dynamic-clamp recording further revealed that excitatory inputs alone would result in membrane potential responses with significantly attenuated selectivity, due to a saturating input-output function of the membrane filtering. Inhibition ameliorated the attenuation of excitatory selectivity by expanding the input dynamic range and caused additional sharpening of output responses beyond unselectively suppressing responses at all orientations. This "blur-sharpening" effect allows selectivity conveyed by excitatory inputs to be better expressed, which may be a general mechanism underlying the generation of feature-selective responses in the face of strong excitatory inputs that are weakly biased.

Local model for contextual modulation in the cerebral cortex

A neural response to a sensory stimulus in cerebral cortex is modulated when other stimuli are presented simultaneously. The other stimuli can modulate responses even when they do not drive the neural output alone, indicating a non-linear summation of synaptic activity. The mechanisms of the nonlinearity have remained unclear. Here, I explore a model which considers both network and intracellular processes, and which can account for various types of contextual modulation. The processes include synaptic sensitivity function, determination of inhibition strength, dendritic decay of membrane voltage, and summation of excitatory and inhibitory membrane voltages. First, the model assumes that excitatory and inhibitory units have the same input sensitivity function, which is more broadly tuned than the output tuning function. Second, a central property of the model is that inhibition is a fraction of excitation, determined by covariance between the input and the sensitivity function. With proper fraction, a model neuron sums apparently decorrelated input, regardless of correlations in the original input. Third, the model assumes that synaptic input lands anisotropically on the dendrites, which together with passive dendritic decay cause exponential decay in summation along the input space. This explains the difference between input sensitivity function and output tuning function, and thus accounts for the division between driving classical and modulating extra-classical receptive fields. The model simulations replicate single-cell area summation function, far surround facilitation, and a shift in tuning function due to contextual stimulation. The model is very general, and should be applicable to various interactions between cortical representations.

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.

Voltage fluctuations in neurons: signal or noise?

Noise and variability are fundamental companions to ion channels and synapses and thus inescapable elements of brain function. The overriding unresolved issue is to what extent noise distorts and limits signaling on one hand and at the same time constitutes a crucial and fundamental enrichment that allows and facilitates complex adaptive behavior in an unpredictable world. Here we review the growing experimental evidence that functional network activity is associated with intense fluctuations in membrane potential and spike timing. We trace origins and consequences of noise and variability. Finally, we discuss noise-free neuronal signaling and detrimental and beneficial forms of noise in large-scale functional neural networks. Evidence that noise and variability in some cases go hand in hand with behavioral variability and increase behavioral choice, richness, and adaptability opens new avenues for future studies.

Distinct functions for direct and transthalamic corticocortical connections

Essentially all cortical areas receive thalamic inputs and send outputs to lower motor centers. Cortical areas communicate with each other by means of direct corticocortical and corticothalamocortical pathways, often organized in parallel. We distinguish these functionally, stressing that the transthalamic pathways are class 1 (formerly known as “driver”) pathways capable of transmitting information, whereas the direct pathways vary, being either class 2 (formerly known as “modulator”) or class 1. The transthalamic pathways provide a thalamic gate that can be open or closed (and otherwise more subtly modulated), and these inputs to the thalamus are generally branches of axons with motor functions. Thus the transthalamic corticocortical pathways that can be gated carry information about the cortical processing in one cortical area and also about the motor instructions currently being issued from that area and copied to other cortical areas.

GABAA inhibition controls response gain in visual cortex

GABA(A) inhibition is thought to play multiple roles in sensory cortex, such as controlling responsiveness and sensitivity, sharpening selectivity, and mediating competitive interactions. To test these proposals, we recorded in cat primary visual cortex (V1) after local iontophoresis of gabazine, the selective GABA(A) antagonist. Gabazine increased responsiveness by as much as 300%. It slightly decreased selectivity for stimulus orientation and direction, often by raising responses to all orientations. Strikingly, gabazine affected neither contrast sensitivity nor cross-orientation suppression, the competition seen when stimuli of different orientation are superimposed. These results were captured by a simple model in which GABA(A) inhibition has the same selectivity as excitation and keeps responses to unwanted stimuli below threshold. We conclude that GABA(A) inhibition in V1 helps enhance stimulus selectivity but is not responsible for competition among superimposed stimuli. It controls the sensitivity of V1 neurons by adjusting their response gain, without affecting their input gain.

Influence of a subtype of inhibitory interneuron on stimulus-specific responses in visual cortex

Inhibition modulates receptive field properties and integrative responses of neurons in cortical circuits. The contribution of specific interneuron classes to cortical circuits and emergent responses is unknown. Here, we examined neuronal responses in primary visual cortex (V1) of adult Dlx1(-/-) mice, which have a selective reduction in cortical dendrite-targeting interneurons (DTIs) that express calretinin, neuropeptide Y, and somatostatin. The V1 neurons examined in Dlx1(-/-) mice have reduced orientation selectivity and altered firing rates, with elevated late responses, suggesting that local inhibition at dendrites has a specific role in modulating neuronal computations. We did not detect overt changes in the physiological properties of thalamic relay neurons and features of thalamocortical projections, such as retinotopic maps and eye-specific inputs, in the mutant mice, suggesting that the defects are cortical in origin. These experimental results are well explained by a computational model that integrates broad tuning from dendrite-targeting and narrower tuning from soma-targeting interneuron subclasses. Our findings suggest a key role for DTIs in the fine-tuning of stimulus-specific cortical responses.

From elementary synaptic circuits to information processing in primary auditory cortex

A key for understanding how information is processed in the cortex is to unravel the dauntingly complex cortical neural circuitry. Recent technical innovations, in particular the in vivo whole-cell voltage-clamp recording techniques, make it possible to directly dissect the excitatory and inhibitory inputs underlying an individual cortical neuron's processing function. This method provides an essential complement to conventional approaches, with which the transfer functions of the neural system are derived by correlating neuronal spike outputs to sensory inputs. Here, we intend to introduce a potentially systematic strategy for resolving the structure of functional synaptic circuits. As complex circuits can be built upon elementary modules, the primary focus of this strategy is to identify elementary synaptic circuits and determine how these circuit units contribute to specific processing functions. This review will summarize recent studies on functional synaptic circuits in the primary auditory cortex, comment on existing experimental techniques for in vivo circuitry studies, and provide a perspective on immediate future directions.

Neocortical layer 4 as a pluripotent function linearizer

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

Gating of signal propagation in spiking neural networks by balanced and correlated excitation and inhibition

Both ongoing and natural stimulus driven neuronal activity are dominated by transients. Selective gating of these transients is mandatory for proper brain function and may, in fact, form the basis of millisecond-fast decision making and action selection. Here we propose that neuronal networks may exploit timing differences between correlated excitation and inhibition (temporal gating) to control the propagation of spiking activity transients. When combined with excitation-inhibition balance, temporal gating constitutes a powerful mechanism to control the propagation of mixtures of transient and tonic neural activity components.

Response features of parvalbumin-expressing interneurons suggest precise roles for subtypes of inhibition in visual cortex

Inhibitory interneurons in the cerebral cortex include a vast array of subtypes, varying in their molecular signatures, electrophysiological properties, and connectivity patterns. This diversity suggests that individual inhibitory classes have unique roles in cortical circuits; however, their characterization to date has been limited to broad classifications including many subtypes. We used the Cre/LoxP system, specifically labeling parvalbumin(PV)-expressing interneurons in visual cortex of PV-Cre mice with red fluorescent protein (RFP), followed by targeted loose-patch recordings and two-photon imaging of calcium responses in vivo to characterize the visual receptive field properties of these cells. Despite their relative molecular and morphological homogeneity, we find that PV+ neurons have a diversity of feature-specific visual responses that include sharp orientation and direction-selectivity, small receptive fields, and band-pass spatial frequency tuning. These results suggest that subsets of parvalbumin interneurons are components of specific cortical networks and that perisomatic inhibition contributes to the generation of precise response properties.

Neurophysiological and computational principles of cortical rhythms in cognition

Synchronous rhythms represent a core mechanism for sculpting temporal coordination of neural activity in the brain-wide network. This review focuses on oscillations in the cerebral cortex that occur during cognition, in alert behaving conditions. Over the last two decades, experimental and modeling work has made great strides in elucidating the detailed cellular and circuit basis of these rhythms, particularly gamma and theta rhythms. The underlying physiological mechanisms are diverse (ranging from resonance and pacemaker properties of single cells to multiple scenarios for population synchronization and wave propagation), but also exhibit unifying principles. A major conceptual advance was the realization that synaptic inhibition plays a fundamental role in rhythmogenesis, either in an interneuronal network or in a reciprocal excitatory-inhibitory loop. Computational functions of synchronous oscillations in cognition are still a matter of debate among systems neuroscientists, in part because the notion of regular oscillation seems to contradict the common observation that spiking discharges of individual neurons in the cortex are highly stochastic and far from being clocklike. However, recent findings have led to a framework that goes beyond the conventional theory of coupled oscillators and reconciles the apparent dichotomy between irregular single neuron activity and field potential oscillations. From this perspective, a plethora of studies will be reviewed on the involvement of long-distance neuronal coherence in cognitive functions such as multisensory integration, working memory, and selective attention. Finally, implications of abnormal neural synchronization are discussed as they relate to mental disorders like schizophrenia and autism.

From dendrite to soma: dynamic routing of inhibition by complementary interneuron microcircuits in olfactory cortex

Diverse inhibitory pathways shape cortical information processing; however, the relevant interneurons recruited by sensory stimuli and how they impact principal cells are unclear. Here we show that two major interneuron circuits govern dynamic inhibition in space and time within the olfactory cortex. Dendritic-targeting layer 1 interneurons receive strong input from the olfactory bulb and govern early-onset feedforward inhibition. However, this circuit is only transiently engaged during bursts of olfactory bulb input. In contrast, somatic-targeting layer 3 interneurons, recruited exclusively by recurrent excitation from pyramidal cells, produce late-onset feedback inhibition. Our results reveal two complementary interneuron circuits enforcing widespread inhibition, which shifts from the apical dendrites to somata of pyramidal cells during bursts of sensory input.

Fine-tuning of pre-balanced excitation and inhibition during auditory cortical development

Functional receptive fields of neurons in sensory cortices undergo progressive refinement during development. Such refinement may be attributed to the pruning of non-optimal excitatory inputs, reshaping of the excitatory tuning profile through modifying the strengths of individual inputs, or strengthening of cortical inhibition. These models have not been directly tested because of the technical difficulties in assaying the spatiotemporal patterns of functional synaptic inputs during development. Here we apply in vivo whole-cell voltage-clamp recordings to the recipient layer 4 neurons in the rat primary auditory cortex (A1) to determine the developmental changes in the frequency-intensity tonal receptive fields (TRFs) of their excitatory and inhibitory inputs. Surprisingly, we observe co-tuned excitation and inhibition immediately after the onset of hearing, suggesting that a tripartite thalamocortical circuit with relatively strong feedforward inhibition is formed independently of auditory experience. The frequency ranges of tone-driven excitatory and inhibitory inputs first expand within a few days of the onset of hearing and then persist into adulthood. The latter phase is accompanied by a sharpening of the excitatory but not inhibitory frequency tuning profile, which results in relatively broader inhibitory tuning in adult A1 neurons. Thus the development of cortical synaptic TRFs after the onset of hearing is marked by a slight breakdown of previously formed excitation-inhibition balance. Our results suggest that functional refinement of cortical TRFs does not require a selective pruning of inputs, but may depend more on a fine adjustment of excitatory input strengths.

Inhibitory modulation of cortical up states

The balance between excitation and inhibition is critical in the physiology of the cerebral cortex. To understand the influence of inhibitory control on the emergent activity of the cortical network, inhibition was progressively blocked in a slice preparation that generates spontaneous rhythmic up states at a similar frequency to those occurring in vivo during slow-wave sleep or anesthesia. Progressive removal of inhibition induced a parametric shortening of up state duration and elongation of the down states, the frequency of oscillations decaying. Concurrently, a gradual increase in the network firing rate during up states occurred. The slope of transitions between up and down states was quantified for different levels of inhibition. The slope of upward transitions reflects the recruitment of the local network and was progressively increased when inhibition was decreased, whereas the speed of activity propagation became faster. Removal of inhibition eventually resulted in epileptiform activity. Whereas gradual reduction of inhibition induced linear changes in up/down states and their propagation, epileptiform activity was the result of a nonlinear transformation. A computational network model showed that strong recurrence plus activity-dependent hyperpolarizing currents were sufficient to account for the observed up state modulations and predicted an increase in activity-dependent hyperpolarization following up states when inhibition was decreased, which was confirmed experimentally.

Lateral competition for cortical space by layer-specific horizontal circuits

The cerebral cortex constructs a coherent representation of the world by integrating distinct features of the sensory environment. Although these features are processed vertically across cortical layers, horizontal projections interconnecting neighbouring cortical domains allow these features to be processed in a context-dependent manner. Despite the wealth of physiological and psychophysical studies addressing the function of horizontal projections, how they coordinate activity among cortical domains remains poorly understood. We addressed this question by selectively activating horizontal projection neurons in mouse somatosensory cortex, and determined how the resulting spatial pattern of excitation and inhibition affects cortical activity. We found that horizontal projections suppress superficial layers while simultaneously activating deeper cortical output layers. This layer-specific modulation does not result from a spatial separation of excitation and inhibition, but from a layer-specific ratio between these two opposing conductances. Through this mechanism, cortical domains exploit horizontal projections to compete for cortical space.