A novel mechanism of response selectivity of neurons in cat visual cortex

Rhythm and Synchrony in a Cortical Network Model

We studied mechanisms for cortical gamma-band activity in the cerebral cortex and identified neurobiological factors that affect such activity. This was done by analyzing the behavior of a previously developed, data-driven, large-scale network model that simulated many visual functions of monkey V1 cortex (Chariker et al., 2016). Gamma activity was an emergent property of the model. The model's gamma activity, like that of the real cortex, was (1) episodic, (2) variable in frequency and phase, and (3) graded in power with stimulus variables like orientation. The spike firing of the model's neuronal population was only partially synchronous during multiple firing events (MFEs) that occurred at gamma rates. Detailed analysis of the model's MFEs showed that gamma-band activity was multidimensional in its sources. Most spikes were evoked by excitatory inputs. A large fraction of these inputs came from recurrent excitation within the local circuit, but feedforward and feedback excitation also contributed, either through direct pulsing or by raising the overall baseline. Inhibition was responsible for ending MFEs, but disinhibition led directly to only a small minority of the synchronized spikes. As a potential explanation for the wide range of gamma characteristics observed in different parts of cortex, we found that the relative rise times of AMPA and GABA synaptic conductances have a strong effect on the degree of synchrony in gamma.SIGNIFICANCE STATEMENT Canonical computations used throughout the cerebral cortex are performed in primary visual cortex (V1). Providing theoretical mechanisms for these computations will advance understanding of computation throughout cortex. We studied one dynamical feature, gamma-band rhythms, in a large-scale, data-driven, computational model of monkey V1. Our most significant conclusion is that the sources of gamma band activity are multidimensional. A second major finding is that the relative rise times of excitatory and inhibitory synaptic potentials have strong effects on spike synchrony and peak gamma band power. Insight gained from studying our V1 model can shed light on the functions of other cortical regions.

Orientation Selectivity from Very Sparse LGN Inputs in a Comprehensive Model of Macaque V1 Cortex

A new computational model of the primary visual cortex (V1) of the macaque monkey was constructed to reconcile the visual functions of V1 with anatomical data on its LGN input, the extreme sparseness of which presented serious challenges to theoretically sound explanations of cortical function. We demonstrate that, even with such sparse input, it is possible to produce robust orientation selectivity, as well as continuity in the orientation map. We went beyond that to find plausible dynamic regimes of our new model that emulate simultaneously experimental data for a wide range of V1 phenomena, beginning with orientation selectivity but also including diversity in neuronal responses, bimodal distributions of the modulation ratio (the simple/complex classification), and dynamic signatures, such as gamma-band oscillations. Intracortical interactions play a major role in all aspects of the visual functions of the model.

SIGNIFICANCE STATEMENT

We present the first realistic model that has captured the sparseness of magnocellular LGN inputs to the macaque primary visual cortex and successfully derived orientation selectivity from them. Three implications are (1) even in input layers to the visual cortex, the system is less feedforward and more dominated by intracortical signals than previously thought, (2) interactions among cortical neurons in local populations produce dynamics not explained by single neurons, and (3) such dynamics are important for function. Our model also shows that a comprehensive picture is necessary to explain function, because different visual properties are related. This study points to the need for paradigm shifts in neuroscience modeling: greater emphasis on population dynamics and, where possible, a move toward data-driven, comprehensive models.

The cognitive significance of resonating neurons in the cerebral cortex

Most neural fibers of the cerebral cortex engage in electric signaling, but one particular fiber, the apical dendrite of the pyramidal neuron, specializes in electric resonating. This dendrite extends upward from somas of pyramidal neurons, the most numerous neurons of the cortex. The apical dendrite is embedded in a recurrent corticothalamic circuit that induces surges of electric current to move repeatedly down the dendrite. Narrow bandwidths of surge frequency (resonating) enable cortical circuits to use specific carrier frequencies, which isolate the processing of those circuits from other circuits. Resonating greatly enhances the intensity and duration of electrical activity of a neuron over a narrow frequency range, which underlies attention in its various modes. Within the minicolumn, separation of the central resonating circuit from the surrounding signal processing network separates "having" subjective impressions from "thinking about" them. Resonating neurons in the insular cortex apparently underlie cognitive impressions of feelings.

The mechanism of orientation selectivity in primary visual cortex without a functional map

Neurons in primary visual cortex (V1) display substantial orientation selectivity even in species where V1 lacks an orientation map, such as in mice and rats. The mechanism underlying orientation selectivity in V1 with such a salt-and-pepper organization is unknown; it is unclear whether a connectivity that depends on feature similarity is required, or a random connectivity suffices. Here we argue for the latter. We study the response to a drifting grating of a network model of layer 2/3 with random recurrent connectivity and feedforward input from layer 4 neurons with random preferred orientations. We show that even though the total feedforward and total recurrent excitatory and inhibitory inputs all have a very weak orientation selectivity, strong selectivity emerges in the neuronal spike responses if the network operates in the balanced excitation/inhibition regime. This is because in this regime the (large) untuned components in the excitatory and inhibitory contributions approximately cancel. As a result the untuned part of the input into a neuron as well as its modulation with orientation and time all have a size comparable to the neuronal threshold. However, the tuning of the F0 and F1 components of the input are uncorrelated and the high-frequency fluctuations are not tuned. This is reflected in the subthreshold voltage response. Remarkably, due to the nonlinear voltage-firing rate transfer function, the preferred orientation of the F0 and F1 components of the spike response are highly correlated.

Ultrafast population encoding by cortical neurons

The processing speed of the brain depends on the ability of neurons to rapidly relay input changes. Previous theoretical and experimental studies of the timescale of population firing rate responses arrived at controversial conclusions, some advocating an ultrafast response scale but others arguing for an inherent disadvantage of mean encoded signals for rapid detection of the stimulus onset. Here we assessed the timescale of population firing rate responses of neocortical neurons in experiments performed in the time domain and the frequency domain in vitro and in vivo. We show that populations of neocortical neurons can alter their firing rate within 1 ms in response to somatically delivered weak current signals presented on a fluctuating background. Signals with amplitudes of miniature postsynaptic currents can be robustly and rapidly detected in the population firing. We further show that population firing rate of neurons of rat visual cortex in vitro and cat visual cortex in vivo can reliably encode weak signals varying at frequencies up to ∼200-300 Hz, or ∼50 times faster than the firing rate of individual neurons. These results provide coherent evidence for the ultrafast, millisecond timescale of cortical population responses. Notably, fast responses to weak stimuli are limited to the mean encoding. Rapid detection of current variance changes requires extraordinarily large signal amplitudes. Our study presents conclusive evidence showing that cortical neurons are capable of rapidly relaying subtle mean current signals. This provides a vital mechanism for the propagation of rate-coded information within and across brain areas.

Modulation of the amplitude of γ-band activity by stimulus phase enhances signal encoding

Visual stimulation often leads to elevated fluctuations of the membrane potential in the γ-frequency range (25-70 Hz) in visual cortex neurons. Recently, we have found that the strength of γ-band fluctuations is coupled to the oscillation of the membrane potential at the temporal frequency of the stimulus, so that the γ-band fluctuations are stronger at depolarization peaks, but weaker at troughs of the stimulus frequency oscillation of the membrane potential. We hypothesized that this coupling may improve stimulus encoding. Here, we tested this hypothesis by using a single-compartment conductance-based neuron model, with parameters of the input adjusted to reproduce typical features of membrane potential and spike responses, recorded in cat visual cortical neurons in vivo during the presentation of moving gratings. We show that modulation of the γ-range membrane potential fluctuations by the amplitude of the slow membrane depolarization greatly improves stimulus encoding. Moreover, changing the degree of modulation of the γ-activity by the low-frequency signal within the range typically observed in visual cortex cells had a stronger effect on both the firing rates and information rates than changing the amplitude of the low-frequency stimulus itself. Thus, modulation of the γ-activity represents an efficient mechanism for regulation of neuronal firing and encoding of the temporal characteristics of visual stimuli.

Spatial and temporal features of synaptic to discharge receptive field transformation in cat area 17

The aim of the present study was to characterize the spatial and temporal features of synaptic and discharge receptive fields (RFs), and to quantify their relationships, in cat area 17. For this purpose, neurons were recorded intracellularly while high-frequency flashing bars were used to generate RFs maps for synaptic and spiking responses. Comparison of the maps shows that some features of the discharge RFs depended strongly on those of the synaptic RFs, whereas others were less dependent. Spiking RF duration depended poorly and spiking RF amplitude depended moderately on those of the underlying synaptic RFs. At the other extreme, the optimal spatial frequency and phase of the discharge RFs in simple cells were almost entirely inherited from those of the synaptic RFs. Subfield width, in both simple and complex cells, was less for spiking responses compared with synaptic responses, but synaptic to discharge width ratio was relatively variable from cell to cell. When considering the whole RF of simple cells, additional variability in width ratio resulted from the presence of additional synaptic subfields that remained subthreshold. Due to these additional, subthreshold subfields, spatial frequency tuning predicted from synaptic RFs appears sharper than that predicted from spiking RFs. Excitatory subfield overlap in spiking RFs was well predicted by subfield overlap at the synaptic level. When examined in different regions of the RF, latencies appeared to be quite variable, but this variability showed negligible dependence on distance from the RF center. Nevertheless, spiking response latency faithfully reflected synaptic response latency.

Stimulus-selective spiking is driven by the relative timing of synchronous excitation and disinhibition in cat striate neurons in vivo

What patterns of synaptic input cause cortical neurons to fire action potentials? Are they stochastic in nature, or do action potentials arise from the specific timing of synaptic input? We addressed these questions by measuring the membrane potential fluctuations associated with the generation of visually evoked action potentials in cat striate cortical neurons in vivo. In response to visual stimulation, action potentials occurred at the crest of large-amplitude, transient depolarizations (TDs) riding on sustained depolarization of the membrane potential. The magnitude, duration and rate of depolarization of these transient events were tuned for stimulus orientation. Using numerical simulations, we find that these transient events can arise from the temporal interplay between synchronous excitation and inhibition. To validate these findings, we made conductance measurements, at the preferred stimulus orientation, and showed that the TDs arise either from an increase in excitatory conductance, or from a combination of increased excitatory and decreased inhibitory conductance, both riding on sustained changes in synaptic conductances. The properties of the TDs and their underlying conductance suggest that they arise from a specific temporal interplay between synchronous excitatory and inhibitory synaptic inputs. Our results illustrate a mechanism by which the timing of synaptic inputs determines much of the spiking activity in striate cortical neurons.

Kinetic theory for neuronal networks with fast and slow excitatory conductances driven by the same spike train

We present a kinetic theory for all-to-all coupled networks of identical, linear, integrate-and-fire, excitatory point neurons in which a fast and a slow excitatory conductance are driven by the same spike train in the presence of synaptic failure. The maximal-entropy principle guides us in deriving a set of three (1+1) -dimensional kinetic moment equations from a Boltzmann-like equation describing the evolution of the one-neuron probability density function. We explain the emergence of correlation terms in the kinetic moment and Boltzmann-like equations as a consequence of simultaneous activation of both the fast and slow excitatory conductances and furnish numerical evidence for their importance in correctly describing the coarse-grained dynamics of the underlying neuronal network.

Lack of orientation and direction selectivity in a subgroup of fast-spiking inhibitory interneurons: cellular and synaptic mechanisms and comparison with other electrophysiological cell types

Neurons in cat area 17 can be grouped in 4 different electrophysiological cell classes (regular spiking, intrinsically bursting, chattering, and fast spiking [FS]). However, little is known of the functional properties of these different cell classes. Here we compared orientation and direction selectivity between these cell classes in cat area 17 and found that a subset of FS inhibitory neurons, usually with complex receptive fields, exhibited little selectivity in comparison with other cell types. Differences in occurrence and amplitude of gamma-range membrane fluctuations, as well as in numbers of action potentials in response to optimal visual stimuli, did not parallel differences observed for orientation and direction selectivity. Instead, differences in selectivity resulted mostly from differences in tuning of the membrane potential responses, although variations in spike threshold also contributed: weakly selective FS neurons exhibited both a lower spike threshold and more broadly tuned membrane potential responses in comparison with the other cell classes. Our results are consistent with the hypothesis that a subgroup of FS neurons receives connections and possesses intrinsic properties allowing the generation of weakly selective responses. The existence of weakly selective inhibitory neurons is consistent with orientation selectivity models that rely on broadly tuned inhibition.

Coding with spike shapes and graded potentials in cortical networks

In cortical neurones, analogue dendritic potentials are thought to be encoded into patterns of digital spikes. According to this view, neuronal codes and computations are based on the temporal patterns of spikes: spike times, bursts or spike rates. Recently, we proposed an 'action potential waveform code' for cortical pyramidal neurones in which the spike shape carries information. Broader somatic action potentials are reliably produced in response to higher conductance input, allowing for four times more information transfer than spike times alone. This information is preserved during synaptic integration in a single neurone, as back-propagating action potentials of diverse shapes differentially shunt incoming postsynaptic potentials and so participate in the next round of spike generation. An open question has been whether the information in action potential waveforms can also survive axonal conduction and directly influence synaptic transmission to neighbouring neurones. Several new findings have now brought new light to this subject, showing cortical information processing that transcends the classical models.

Apical dendrite activity in cognition and consciousness

The ongoing steady nature of consciousness in everyday life implies that the underlying neural activity possesses a high level of stability. The prolonged cognitive events of sustained attention, imagery, and working memory also imply high stability of underlying neural activity. This paper proposes that stabilization of neural activity is produced by apical dendrite activity in pyramidal neurons within recurrent corticothalamic circuits, and proposes that the wave activities of apical dendrites that stabilize ongoing activity constitute the subjective impressions of an attended object and the entire sensory background. The cortical minicolumn, as the functional unit of the cortex, is separated into an axis consisting of layer 5 pyramidal neurons and a surrounding shell consisting of layer 2/3 pyramidal neurons. It is proposed that apical dendrites of the axis generate sensory impressions, and basal dendrites of the shell process the brief-lasting input-output identifications of objects that give rise to ideas.

Unique features of action potential initiation in cortical neurons

Neurons process and encode information by generating sequences of action potentials. For all spiking neurons, the encoding of single-neuron computations into sequences of spikes is biophysically determined by the cell's action-potential-generating mechanism. It has recently been discovered that apparently minor modifications of this mechanism can qualitatively change the nature of neuronal encoding. Here we quantitatively analyse the dynamics of action potential initiation in cortical neurons in vivo, in vitro and in computational models. Unexpectedly, key features of the initiation dynamics of cortical neuron action potentials--their rapid initiation and variable onset potential--are outside the range of behaviours described by the classical Hodgkin-Huxley theory. We propose a new model based on the cooperative activation of sodium channels that reproduces the observed dynamics of action potential initiation. This new model predicts that Hodgkin-Huxley-type dynamics of action potential initiation can be induced by artificially decreasing the effective density of sodium channels. In vitro experiments confirm this prediction, supporting the hypothesis that cooperative sodium channel activation underlies the dynamics of action potential initiation in cortical neurons.

Neural network model of the primary visual cortex: from functional architecture to lateral connectivity and back

The role of intrinsic cortical dynamics is a debatable issue. A recent optical imaging study (Kenet et al., 2003) found that activity patterns similar to orientation maps (OMs), emerge in the primary visual cortex (V1) even in the absence of sensory input, suggesting an intrinsic mechanism of OM activation. To better understand these results and shed light on the intrinsic V1 processing, we suggest a neural network model in which OMs are encoded by the intrinsic lateral connections. The proposed connectivity pattern depends on the preferred orientation and, unlike previous models, on the degree of orientation selectivity of the interconnected neurons. We prove that the network has a ring attractor composed of an approximated version of the OMs. Consequently, OMs emerge spontaneously when the network is presented with an unstructured noisy input. Simulations show that the model can be applied to experimental data and generate realistic OMs. We study a variation of the model with spatially restricted connections, and show that it gives rise to states composed of several OMs. We hypothesize that these states can represent local properties of the visual scene.

Sustained attention and apical dendrite activity in recurrent circuits

Recurrent neural activity is a pervasive mode of cortical operations and is believed to underlie cognitive functions of working memory, attention, and the generation of spontaneous activity during sleep . It is proposed here that activity in corticothalamic recurrent circuits underlies the sustaining of attention, and that extended durations of attention are made possible by the stabilizing effects of electrical activity in long apical dendrites of pyramidal neurons. Using the cue-target delay task as a framework, the present paper describes sustained attention during the cue-target delay as activity in recurrent circuits involving layer 5/6 pyramidal neurons. At target onset, persistent activity in apical dendrites of layer 2/3 pyramidal neurons (projected from the recurrent circuits) can enhance the processing of incoming pulse trains at basal dendrites. Apical dendrite activity is assumed to modulate the soma processing of layer 2/3 and layer 5/6 pyramidal neurons at subthreshold voltage levels. The variability of successive soma depolarizations from the apical dendrite strongly influences the stability of activity in the corticothalamic recurrent circuit. Lower variability promotes higher stability. According to the present model of apical dendrite function, soma depolarizations can be reduced in variability and maintained within subthreshold levels by increasing the distance that EPSPs propagate along the apical dendrite. The close relationship between sustained attention and the electrical field potentials produced by repeated EPSP propagations in apical dendrites is supported in a brief review of sustained attention experiments that have employed measures of EEG, ERS/ERD, ERP, and LFP.

Stimulus-dependent changes in spike threshold enhance feature selectivity in rat barrel cortex neurons

Feature selectivity is a fundamental property of sensory cortex neurons, yet the mechanisms underlying its genesis are not fully understood. Using intracellular recordings in vivo from layers 2-6 of rat barrel cortex, we studied the selectivity of neurons to the angular direction of whisker deflection. The spike output and the underlying synaptic response decreased exponentially in magnitude as the direction of deflection diverged from the preferred. However, the spike output was more sharply tuned for direction than the underlying synaptic response amplitude. This difference in selectivity was attributable to the rectification imposed by the spike threshold on the input-output function of cells. As in the visual system, spike threshold was not constant and showed trial-to-trial variability. However, here we show that the mean spike threshold was direction dependent and increased as the direction diverged from the preferred. Spike threshold was also related to the rate of rise of the synaptic response, which was direction dependent and steepest for the preferred direction. To assess the impact of the direction-dependent changes in spike threshold on direction selectivity, we applied a fixed threshold to the synaptic responses and calculated a predicted spike output. The predicted output was more broadly tuned than the obtained spike response, demonstrating for the first time that the regulation of the spike threshold by the properties of the synaptic response effectively enhances the selectivity of the spike output.

Short-term depression in thalamocortical synapses of cat primary visual cortex

Neurons in primary visual cortex exhibit several nonlinearities in their responses to visual stimuli, including response decrements to repeated stimuli, contrast-dependent phase advance, contrast saturation, and cross-orientation suppression. Thalamocortical synaptic depression has been implicated in these phenomena but has not been examined directly in visual cortex in vivo. We assessed depression of visual thalamocortical synapses in vivo using 20-100 Hz trains of electrical stimuli delivered to the LGN. Cortical cells receiving direct input from the LGN, identified by short latency and low jitter of LGN-evoked PSPs, showed moderate reductions in PSP amplitude during the fastest trains. Cells receiving indirect input from the thalamus via other cortical excitatory neurons show a marked reduction in PSP amplitude during a train, which could be explained either by synaptic depression in corticocortical synapses or by an inhibition-mediated suppression of the firing of their afferents. Reducing spontaneous activity in the LGN (by retinal blockade) unmasked additional depression at the thalamocortical synapse but only for the first stimulus in the train. That is, the first PSP was increased in amplitude relative to the unblocked condition, but subsequent responses were essentially unchanged. Thus, the synapses are maintained at significant levels of depression by spontaneous activity. These findings constrain the role that thalamocortical depression can play in shaping cortical responses to visual stimuli.

Local networks in visual cortex and their influence on neuronal responses and dynamics

Networks of neurons in the cerebral cortex generate complex outputs that are not simply predicted by their inputs. These emergent responses underlie the function of the cortex. Understanding how cortical networks carry out such transformations requires a description of the responses of individual neurons and of their networks at multiple levels of analysis. We focus on orientation selectivity in primary visual cortex as a model system to understand cortical network computations. Recent experiments in our laboratory and others provide significant insight into how cortical networks generate and maintain orientation selectivity. We first review evidence for the diversity of orientation tuning characteristics in visual cortex. We then describe experiments that combine optical imaging of orientation maps with intracellular and extracellular recordings from individual neurons at known locations in the orientation map. The data indicate that excitatory and inhibitory synaptic inputs are summed across the cortex in a manner that is consistent with simple rules of integration of local inputs. These rules arise from known anatomical projection patterns in visual cortex. We propose that the generation and plasticity of orientation tuning is strongly influenced by local cortical networks-the diversity of these properties arises in part from the diversity of neighbourhood features that derive from the orientation map.

Different types of signal coupling in the visual cortex related to neural mechanisms of associative processing and perception

The hypothesis of object representation by synchronization in the visual cortex has been supported by our recent experiments in monkeys. They demonstrated local synchrony among gamma activities (30-90 Hz) and their perceptual modulation, according to the rules of figure-ground segregation. However, gamma-synchrony in primary visual cortex is restricted to few mm, challenging the synchronization hypothesis for larger cortical object representations. The restriction is due to randomly changing phase relations among locally synchronized patches which, however, form continuous waves of gamma-activity, traveling across object representations. The phase continuity of these waves may support coding of object continuity. Interactions across still larger distances, measured among cortical areas in human data, involve amplitude envelopes of gamma signals. Based on models with spiking neurons we discuss potentially underlying mechanisms. Most important for gamma synchronization are local facilitatory connections with distance-dependent delays. They also explain the occurrence of gamma waves and the restriction of gamma-synchrony. Fast local feedback inhibition generates gamma oscillations and supports local synchrony, while slow shunting inhibitory feedback supports figure-ground segregation. Finally, dispersion in inter-areal far projections destroys coherence of gamma signals, but preserves their amplitude modulations. In conclusion, we propose that the hypothesis of associative processing by gamma synchronization be extended to more general forms of signal coupling.

Amplification of trial-to-trial response variability by neurons in visual cortex

The visual cortex responds to repeated presentations of the same stimulus with high variability. Because the firing mechanism is remarkably noiseless, the source of this variability is thought to lie in the membrane potential fluctuations that result from summated synaptic input. Here this hypothesis is tested through measurements of membrane potential during visual stimulation. Surprisingly, trial-to-trial variability of membrane potential is found to be low. The ratio of variance to mean is much lower for membrane potential than for firing rate. The high variability of firing rate is explained by the threshold present in the function that converts inputs into firing rates. Given an input with small, constant noise, this function produces a firing rate with a large variance that grows with the mean. This model is validated on responses recorded both intracellularly and extracellularly. In neurons of visual cortex, thus, a simple deterministic mechanism amplifies the low variability of summated synaptic inputs into the large variability of firing rate. The computational advantages provided by this amplification are not known.

Neuronal integration of synaptic input in the fluctuation-driven regime

During sensory stimulation, visual cortical neurons undergo massive synaptic bombardment. This increases their input conductance, and action potentials mainly result from membrane potential fluctuations. To understand the response properties of neurons operating in this regime, we studied a model neuron with synaptic inputs represented by transient membrane conductance changes. We show that with a simultaneous increase of excitation and inhibition, the firing rate first increases, reaches a maximum, and then decreases at higher input rates. Comodulation of excitation and inhibition, therefore, does not provide a straightforward way of controlling the neuronal firing rate, in contrast to coding mechanisms postulated previously. The synaptically induced conductance increase plays a key role in this effect: it decreases firing rate by shunting membrane potential fluctuations, and increases it by reducing the membrane time constant, allowing for faster membrane potential transients. These findings do not depend on details of the model and, hence, are relevant to cells of other cortical areas as well.

Task-related coupling from high- to low-frequency signals among visual cortical areas in human subdural recordings

Cortical cooperativity during cognitive demands includes high- and low-frequency activities, which raises the question whether there are interdependencies between fast and slow processes and how they are reflected in electrical brain signals. We had the opportunity to record signals intracranially from occipital visual areas in an epileptic patient and quantified inter-areal signal coupling while the patient performed a visual delayed-match-to-sample task. We computed coherence, phase consistency and amplitude envelope correlation and we also determined inter-frequency coupling through correlation between low-frequency signal components and amplitude envelopes of high-frequency components. There was a pronounced task-related increase of correlation between gamma-band (28-70 Hz) signal envelopes from a superior (occipital) and low-frequency (0-3.5 Hz) signals from an inferior (occipital) visual area, lasting for approximately 1 s and possibly reflecting a short-term memory encoding process. The correlational delay between envelopes and low-frequency components was 40 ms. In contrast, coherence, phase consistency and envelope correlation showed event-, but no task-related changes of intra-areal and no changes of inter-areal coupling. Our data suggest a specific effect of gamma-activity in the superior onto low-frequency activity in the inferior area. We argue that temporal dispersion of conduction delays might prevent coherent transmission of high-frequency signals and thus account for the absence of gamma-coherence. As such dispersion is a general property of long-range projections, envelope-to-signal correlation possibly reflects a general neuronal mechanism. Hence, our method provides a powerful tool for detecting such inter-areal interactions not visible with conventional linear coupling measures.

Gamma-frequency fluctuations of the membrane potential and response selectivity in visual cortical neurons

Fluctuations at frequencies of 25-70 Hz is an inherent property of cortical activity. These rapid, gamma-range fluctuations are apparent in the local field potentials, in spiking of cells and cell groups, and in the membrane potential of neurons. To investigate stimulus dependence of the gamma-frequency fluctuations of the membrane potential, we have recorded intracellularly responses of cells in cat visual cortex to presentation of moving gratings. We found gamma-range fluctuations of the membrane potential in both simple and complex cells. The strength of the gamma-frequency fluctuations correlated with the stimulus optimality. Furthermore, the amplitude of the gamma-frequency fluctuations correlated with the phase of stimulus-imposed slow changes of the membrane potential. The combination of these features makes cortical neurons capable of encoding the slow changes in the visual world in a kind of amplitude modulation of the high frequency fluctuations. This assures reliable transformation of the membrane potential changes into spike responses without compromising the temporal resolution of visual information encoding in the low frequency range.

Synaptic integration by V1 neurons depends on location within the orientation map

Neurons in the primary visual cortex (V1) are organized into an orientation map consisting of orientation domains arranged radially around "pinwheel centers" at which the representations of all orientations converge. We have combined optical imaging of intrinsic signals with intracellular recordings to estimate the subthreshold inputs and spike outputs of neurons located near pinwheel centers or in orientation domains. We find that neurons near pinwheel centers have subthreshold responses to all stimulus orientations but spike responses to only a narrow range of orientations. Across the map, the selectivity of inputs covaries with the selectivity of orientations in the local cortical network, while the selectivity of spike outputs does not. Thus, the input-output transformation performed by V1 neurons is powerfully influenced by the local structure of the orientation map.