Regulation of spike timing in visual cortical circuits

Onset coding is degraded in auditory nerve fibers from mutant mice lacking synaptic ribbons

Synaptic ribbons, found at the presynaptic membrane of sensory cells in both ear and eye, have been implicated in the vesicle-pool dynamics of synaptic transmission. To elucidate ribbon function, we characterized the response properties of single auditory nerve fibers in mice lacking Bassoon, a scaffolding protein involved in anchoring ribbons to the membrane. In bassoon mutants, immunohistochemistry showed that fewer than 3% of the hair cells' afferent synapses retained anchored ribbons. Auditory nerve fibers from mutants had normal threshold, dynamic range, and postonset adaptation in response to tone bursts, and they were able to phase lock with normal precision to amplitude-modulated tones. However, spontaneous and sound-evoked discharge rates were reduced, and the reliability of spikes, particularly at stimulus onset, was significantly degraded as shown by an increased variance of first-spike latencies. Modeling based on in vitro studies of normal and mutant hair cells links these findings to reduced release rates at the synapse. The degradation of response reliability in these mutants suggests that the ribbon and/or Bassoon normally facilitate high rates of exocytosis and that its absence significantly compromises the temporal resolving power of the auditory system.

Location of cells giving phase-locked responses to pure tones in the primary auditory cortex

Phase-locked responses to pure tones have previously been described in the primary auditory cortex (AI) of the guinea pig. They are interesting because they show that some cells may use a temporal code for representing sounds of 60-300 Hz rather than the rate or place mechanisms used over most of AI. Our previous study had shown that the phase-locked responses were grouped together, but it was not clear whether they were in separate minicolumns or a larger macrocolumn. We now show that the phase-locked cells are arranged in a macrocolumn within AI that forms a subdivision of the isofrequency bands. Phase-locked responses were recorded from 158 multiunits using silicon based multiprobes with four shanks. The phase-locked units gave the strongest response in layers III/IV but phase-locked units were also recorded in layers II, V and VI. The column included cells with characteristic frequencies of 80 Hz-1.3 kHz (0.5-0.8 mm long) and was about 0.5 mm wide. It was located at a constant position at the intersection of the coronal plane 1 mm caudal to bregma and the suture that forms the lateral edge of the parietal bone.

Functional consequences of correlated excitatory and inhibitory conductances in cortical networks

Neurons in the neocortex receive a large number of excitatory and inhibitory synaptic inputs. Excitation and inhibition dynamically balance each other, with inhibition lagging excitation by only few milliseconds. To characterize the functional consequences of such correlated excitation and inhibition, we studied models in which this correlation structure is induced by feedforward inhibition (FFI). Simple circuits show that an effective FFI changes the integrative behavior of neurons such that only synchronous inputs can elicit spikes, causing the responses to be sparse and precise. Further, effective FFI increases the selectivity for propagation of synchrony through a feedforward network, thereby increasing the stability to background activity. Last, we show that recurrent random networks with effective inhibition are more likely to exhibit dynamical network activity states as have been observed in vivo. Thus, when a feedforward signal path is embedded in such recurrent network, the stabilizing effect of effective inhibition creates an suitable substrate for signal propagation. In conclusion, correlated excitation and inhibition support the notion that synchronous spiking may be important for cortical processing.

Functional feedback from mushroom bodies to antennal lobes in the Drosophila olfactory pathway

Feedback plays important roles in sensory processing. Mushroom bodies are believed to be involved in olfactory learning/memory and multisensory integration in insects. Previous cobalt-labeling studies have suggested the existence of feedback from the mushroom bodies to the antennal lobes in the honey bee. In this study, the existence of functional feedback from Drosophila mushroom bodies to the antennal lobes was investigated through ectopic expression of the ATP receptor P2X(2) in the Kenyon cells of mushroom bodies. Activation of Kenyon cells induced depolarization in projection neurons and local interneurons in the antennal lobes in a nicotinic receptor-dependent manner. Activation of Kenyon cell axons in the betagamma-lobes in the mushroom body induced more potent responses in the antennal lobe neurons than activation of Kenyon cell somata. Our results indicate that functional feedback from Kenyon cells to projection neurons and local interneurons is present in Drosophila and is likely mediated by the betagamma-lobes. The presence of this functional feedback from the mushroom bodies to the antennal lobes suggests top-down modulation of olfactory information processing in Drosophila.

Variability of visual responses of superior colliculus neurons depends on stimulus velocity

Visually responding neurons in the superficial, retinorecipient layers of the cat superior colliculus receive input from two primarily parallel information processing channels, Y and W, which is reflected in their velocity response profiles. We quantified the time-dependent variability of responses of these neurons to stimuli moving with different velocities by Fano factor (FF) calculated in discrete time windows. The FF for cells responding to low-velocity stimuli, thus receiving W inputs, increased with the increase in the firing rate. In contrast, the dynamics of activity of the cells responding to fast moving stimuli, processed by Y pathway, correlated negatively with FF whether the response was excitatory or suppressive. These observations were tested against several types of surrogate data. Whereas Poisson description failed to reproduce the variability of all collicular responses, the inclusion of secondary structure to the generating point process recovered most of the observed features of responses to fast moving stimuli. Neither model could reproduce the variability of low-velocity responses, which suggests that, in this case, more complex time dependencies need to be taken into account. Our results indicate that Y and W channels may differ in reliability of responses to visual stimulation. Apart from previously reported morphological and physiological differences of the cells belonging to Y and W channels, this is a new feature distinguishing these two pathways.

Reconciling coherent oscillation with modulation of irregular spiking activity in selective attention: gamma-range synchronization between sensory and executive cortical areas

In this computational work, we investigated gamma-band synchronization across cortical circuits associated with selective attention. The model explicitly instantiates a reciprocally connected loop of spiking neurons between a sensory-type (area MT) and an executive-type (prefrontal/parietal) cortical circuit (the source area for top-down attentional signaling). Moreover, unlike models in which neurons behave as clock-like oscillators, in our model single-cell firing is highly irregular (close to Poisson), while local field potential exhibits a population rhythm. In this "sparsely synchronized oscillation" regime, the model reproduces and clarifies multiple observations from behaving animals. Top-down attentional inputs have a profound effect on network oscillatory dynamics while only modestly affecting single-neuron spiking statistics. In addition, attentional synchrony modulations are highly selective: interareal neuronal coherence occurs only when there is a close match between the preferred feature of neurons, the attended feature, and the presented stimulus, a prediction that is experimentally testable. When interareal coherence was abolished, attention-induced gain modulations of sensory neurons were slightly reduced. Therefore, our model reconciles the rate and synchronization effects, and suggests that interareal coherence contributes to large-scale neuronal computation in the brain through modest enhancement of rate modulations as well as a pronounced attention-specific enhancement of neural synchrony.

Decoding Poisson Spike Trains by Gaussian Filtering

The temporal waveform of neural activity is commonly estimated by low-pass filtering spike train data through convolution with a gaussian kernel. However, the criteria for selecting the gaussian width σ are not well understood. Given an ensemble of Poisson spike trains generated by an instantaneous firing rate function λ(t), the problem was to recover an optimal estimate of λ(t) by gaussian filtering. We provide equations describing the optimal value of σ using an error minimization criterion and examine how the optimal σ varies within a parameter space defining the statistics of inhomogeneous Poisson spike trains. The process was studied both analytically and through simulations. The rate functions λ(t) were randomly generated, with the three parameters defining spike statistics being the mean of λ(t), the variance of λ(t), and the exponent α of the Fourier amplitude spectrum 1/fα of λ(t). The value of σopt followed a power law as a function of the pooled mean interspike interval I, σopt = aIb, where a was inversely related to the coefficient of variation CV of λ(t), and b was inversely related to the Fourier spectrum exponent α. Besides applications for data analysis, optimal recovery of an analog signal waveform λ(t) from spike trains may also be useful in understanding neural signal processing in vivo.

Multiple timescale encoding of slowly varying whisker stimulus envelope in cortical and thalamic neurons in vivo

Adaptive processes over many timescales endow neurons with sensitivity to stimulus changes over a similarly wide range of scales. Although spike timing of single neurons can precisely signal rapid fluctuations in their inputs, the mean firing rate can convey information about slower-varying properties of the stimulus. Here, we investigate the firing rate response to a slowly varying envelope of whisker motion in two processing stages of the rat vibrissa pathway. The whiskers of anesthetized rats were moved through a noise trajectory with an amplitude that was sinusoidally modulated at one of several frequencies. In thalamic neurons, we found that the rate response to the stimulus envelope was also sinusoidal, with an approximately frequency-independent phase advance with respect to the input. Responses in cortex were similar but with a phase shift that was about three times larger, consistent with a larger amount of rate adaptation. These response properties can be described as a linear transformation of the input for which a single parameter quantifies the phase shift as well as the degree of adaptation. These results are reproduced by a model of adapting neurons connected by synapses with short-term plasticity, showing that the observed linear response and phase lead can be built up from a network that includes a sequence of nonlinear adapting elements. Our study elucidates how slowly varying envelope information under passive stimulation is preserved and transformed through the vibrissa processing pathway.

The subthreshold relation between cortical local field potential and neuronal firing unveiled by intracellular recordings in awake rats

In most of the in vivo electrophysiological studies of cortical processing, which are extracellular, the spike-triggered local field potential average (LFP STA) is the measure used to estimate the correlation between the synaptic inputs of individual neuron and the local population. To understand how the magnitude and shape of LFP STA reflect the underlying correlation of synaptic activities, the membrane potential of the firing neuron has to be recorded together with the LFP. Using intracellular recordings from the cortex of awake rats, we found that for a large range of firing rates and for different behavioral states, the LFP STA represents both in its waveform and its magnitude the cross-correlation between the membrane potential of the neuron and the LFP. This data, supported by further analysis, suggests that LFP STA does not represent large network events specific to the spike times, but rather the synchrony between the mean synaptic activity of the population and the membrane potential of the single neuron, present both around spike times and in the intervals between spikes. Furthermore, it introduces a novel interpretation of the available data from unit and LFP extracellular recording experiments.

Synchrony of thalamocortical inputs maximizes cortical reliability

Thalamic inputs strongly drive neurons in the primary visual cortex, even though these neurons constitute only approximately 5% of the synapses on layer 4 spiny stellate simple cells. We modeled the feedforward excitatory and inhibitory inputs to these cells based on in vivo recordings in cats, and we found that the reliability of spike transmission increased steeply between 20 and 40 synchronous thalamic inputs in a time window of 5 milliseconds, when the reliability per spike was most energetically efficient. The optimal range of synchronous inputs was influenced by the balance of background excitation and inhibition in the cortex, which could gate the flow of information into the cortex. Ensuring reliable transmission by spike synchrony in small populations of neurons may be a general principle of cortical function.

Top-down control of visual attention

Top-down visual attention improves perception of selected stimuli and that improvement is reflected in the neural activity at many stages throughout the visual system. Recent studies of top-down attention have elaborated on the signatures of its effects within visual cortex and have begun identifying its causal basis. Evidence from these studies suggests that the correlates of spatial attention exhibited by neurons within the visual system originate from a distributed network of structures involved in the programming of saccadic eye movements. We summarize this evidence and discuss its relationship to the neural mechanisms of spatial working memory.

Synaptic and network mechanisms of sparse and reliable visual cortical activity during nonclassical receptive field stimulation

During natural vision, the entire visual field is stimulated by images rich in spatiotemporal structure. Although many visual system studies restrict stimuli to the classical receptive field (CRF), it is known that costimulation of the CRF and the surrounding nonclassical receptive field (nCRF) increases neuronal response sparseness. The cellular and network mechanisms underlying increased response sparseness remain largely unexplored. Here we show that combined CRF + nCRF stimulation increases the sparseness, reliability, and precision of spiking and membrane potential responses in classical regular spiking (RS(C)) pyramidal neurons of cat primary visual cortex. Conversely, fast-spiking interneurons exhibit increased activity and decreased selectivity during CRF + nCRF stimulation. The increased sparseness and reliability of RS(C) neuron spiking is associated with increased inhibitory barrages and narrower visually evoked synaptic potentials. Our experimental observations were replicated with a simple computational model, suggesting that network interactions among neuronal subtypes ultimately sharpen recurrent excitation, producing specific and reliable visual responses.

Perceptual priming leads to reduction of gamma frequency oscillations

Oscillations of neural activity are ubiquitous in the brain and are critical for normal cognitive function. In the visual system, repetitive presentation of a stimulus results in the reduction of power elicited in the gamma frequency band. However, this reduction does not result in degradation of perception; on the contrary, perception is improved by prior experience with the stimulus. To explain how reduction of gamma frequency oscillations, observed in priming experiments, can lead to improvement in behavior, we assume that visual processing takes place in two distinct stages: representation sharpening in the early visual areas and competitive interaction among representations in the higher visual areas and the prefrontal cortex. Here, we present a network model of spiking neurons that demonstrates how stimulus repetition leads to a decrease in power of the local field potential oscillations in the gamma frequency range in the early layer and also improves network response by reducing the latency to reach a decision in the higher area.

Supervised Learning in Spiking Neural Networks with ReSuMe: Sequence Learning, Classification, and Spike Shifting

Learning from instructions or demonstrations is a fundamental property of our brain necessary to acquire new knowledge and develop novel skills or behavioral patterns. This type of learning is thought to be involved in most of our daily routines. Although the concept of instruction-based learning has been studied for several decades, the exact neural mechanisms implementing this process remain unrevealed. One of the central questions in this regard is, How do neurons learn to reproduce template signals (instructions) encoded in precisely timed sequences of spikes?

Here we present a model of supervised learning for biologically plausible neurons that addresses this question. In a set of experiments, we demonstrate that our approach enables us to train spiking neurons to reproduce arbitrary template spike patterns in response to given synaptic stimuli even in the presence of various sources of noise.

We show that the learning rule can also be used for decision-making tasks. Neurons can be trained to classify categories of input signals based on only a temporal configuration of spikes. The decision is communicated by emitting precisely timed spike trains associated with given input categories. Trained neurons can perform the classification task correctly even if stimuli and corresponding decision times are temporally separated and the relevant information is consequently highly overlapped by the ongoing neural activity.

Finally, we demonstrate that neurons can be trained to reproduce sequences of spikes with a controllable time shift with respect to target templates. A reproduced signal can follow or even precede the targets. This surprising result points out that spiking neurons can potentially be applied to forecast the behavior (firing times) of other reference neurons or networks.

How synchronization protects from noise

The functional role of synchronization has attracted much interest and debate: in particular, synchronization may allow distant sites in the brain to communicate and cooperate with each other, and therefore may play a role in temporal binding, in attention or in sensory-motor integration mechanisms. In this article, we study another role for synchronization: the so-called “collective enhancement of precision”. We argue, in a full nonlinear dynamical context, that synchronization may help protect interconnected neurons from the influence of random perturbations—intrinsic neuronal noise—which affect all neurons in the nervous system. More precisely, our main contribution is a mathematical proof that, under specific, quantified conditions, the impact of noise on individual interconnected systems and on their spatial mean can essentially be cancelled through synchronization. This property then allows reliable computations to be carried out even in the presence of significant noise (as experimentally found e.g., in retinal ganglion cells in primates). This in turn is key to obtaining meaningful downstream signals, whether in terms of precisely-timed interaction (temporal coding), population coding, or frequency coding. Similar concepts may be applicable to questions of noise and variability in systems biology.

Synchronization phenomena are pervasive in biology, creating collective behavior out of local interactions between neurons, cells, or animals. On the other hand, many of these systems function in the presence of large amounts of noise or disturbances, making one wonder how meaningful behavior can arise in these highly perturbed conditions. In this paper we show mathematically, in a general context, that synchronization is actually a means to protect interconnected systems from effects of noise and disturbances. One possible mechanism for synchronization is that the systems jointly create and then share a common signal, such as a mean electrical field or a global chemical concentration, which in turn makes each system directly connected to all others. Conversely, extracting meaningful information from average measurements over populations of cells (as commonly used for instance in electro-encephalography, or more recently in brain-machine interfaces) may require the presence of synchronization mechanisms similar to those we describe.

Coherence potentials: loss-less, all-or-none network events in the cortex

Transient associations among neurons are thought to underlie memory and behavior. However, little is known about how such associations occur or how they can be identified. Here we recorded ongoing local field potential (LFP) activity at multiple sites within the cortex of awake monkeys and organotypic cultures of cortex. We show that when the composite activity of a local neuronal group exceeds a threshold, its activity pattern, as reflected in the LFP, occurs without distortion at other cortex sites via fast synaptic transmission. These large-amplitude LFPs, which we call coherence potentials, extend up to hundreds of milliseconds and mark periods of loss-less spread of temporal and amplitude information much like action potentials at the single-cell level. However, coherence potentials have an additional degree of freedom in the diversity of their waveforms, which provides a high-dimensional parameter for encoding information and allows identification of particular associations. Such nonlinear behavior is analogous to the spread of ideas and behaviors in social networks.

Coupling regularizes individual units in noisy populations

The regularity of a noisy system can modulate in various ways. It is well known that coupling in a population can lower the variability of the entire network; the collective activity is more regular. Here, we show that diffusive (reciprocal) coupling of two simple Ornstein-Uhlenbeck (O-U) processes can regularize the individual, even when it is coupled to a noisier process. In cellular networks, the regularity of individual cells is important when a select few play a significant role. The regularizing effect of coupling surprisingly applies also to general nonlinear noisy oscillators. However, unlike with the O-U process, coupling-induced regularity is robust to different kinds of coupling. With two coupled noisy oscillators, we derive an asymptotic formula assuming weak noise and coupling for the variance of the period (i.e., spike times) that accurately captures this effect. Moreover, we find that reciprocal coupling can regularize the individual period of higher dimensional oscillators such as the Morris-Lecar and Brusselator models, even when coupled to noisier oscillators. Coupling can have a counterintuitive and beneficial effect on noisy systems. These results have implications for the role of connectivity with noisy oscillators and the modulation of variability of individual oscillators.

Dynamic control for synchronization of separated cortical areas through thalamic relay

Binding of features and information which are processed at different cortical areas is generally supposed to be achieved by synchrony despite the non-negligible delays between these areas. In this work we study the dynamics and synchronization properties of a simplified model of the thalamocortical circuit where different cortical areas are interconnected with a certain delay, that is longer than the internal time scale of the neurons. Using this simple model we find that the thalamus could serve as a central subcortical area that is able to generate zero-lag synchrony between distant cortical areas by means of dynamical relaying (Vicente et al., 2008). Our results show that the model circuit is able to generate fast oscillations in frequency ranges of the beta and gamma bands triggered by an external input to the thalamus formed by independent Poisson trains. We propose a control mechanism to turn "On" and "Off" the synchronization between cortical areas as a function of the relative rate of the external input fed into dorsal and ventral thalamic neuronal populations. The current results emphasize the hypothesis that the thalamus could control the dynamics of the thalamocortical functional networks enabling two separated cortical areas to be either synchronized (at zero-lag) or unsynchronized. This control may happen at a fast time scale, in agreement with experimental data, and without any need of plasticity or adaptation mechanisms which typically require longer time scales.

Reliable and precise neuronal firing during sensory plasticity in superficial layers of primary somatosensory cortex

Neocortical neurons show astonishing variation in the presence and timing of action potentials across stimulus trials, a phenomenon whose function and significance has been the subject of great interest. Here we present data showing that this response variability can be significantly reduced by altered sensory experience. Removal of all but one whisker from the side of the mouse face results in the rapid (within 24 h) potentiation of mean firing rates within the cortical representation of the spared whisker in young postnatal animals (postnatal days 13-16). Analysis of single-unit responses from whisker-spared animals shows that this potentiation can be attributed to an enhancement of trial-to-trial reliability (i.e., reduced response failures), as well as an increase in the mean number of spikes evoked within a successful trial. Changes were confined to superficial layers 2/3 and were not observed in the input layer of the cortex, layer 4. In addition to these changes in firing rates, we also observed profound changes in the precise timing of sensory-evoked responses. Trial-to-trial temporal precision was enhanced and the absolute latency of responses was reduced after single-whisker experience. Enhanced spike-timing precision and trial-to-trial reliability could also be triggered in adolescent animals with longer periods (7 d) of single-whisker experience. These experiments provide a quantitative analysis of how sensory experience can enhance both reliability and temporal precision in neocortical neurons and provide a framework for testing specific hypotheses about the role of response variability in cortical function and the molecular mechanisms underlying this phenomenon.

Cortical enlightenment: are attentional gamma oscillations driven by ING or PING?

The response of a neuron to sensory stimuli can only give correlational support for functional hypotheses. To experimentally test causal function, the neural activity needs to be manipulated in a cell-type-specific as well as spatially and temporally precise way. We review recent optogenetic experiments on parvalbumin-positive cortical interneurons that link modeling studies of synchronization to experimental studies on attentional modulation of gamma oscillations in primates.

Quantifying statistical interdependence by message passing on graphs-part I: one-dimensional point processes

We present a novel approach to quantify the statistical interdependence of two time series, referred to as stochastic event synchrony (SES). The first step is to extract the two given time series. The next step is to try to align events from one time series with events from the other. The better the alignment the more similar the two series are considered to be. More precisely, the similarity is quantified by the following parameters: time delay, variance of the time jitter, fraction of noncoincident events, and average similarity of the aligned events. The pairwise alignment and SES parameters are determined by statistical inference. In particular, the SES parameters are computed by maximum a posteriori (MAP) estimation, and the pairwise alignment is obtained by applying the max product algorithm. This letter deals with one-dimensional point processes; the extension to multidimensional point processes is considered in a companion letter in this issue. By analyzing surrogate data, we demonstrate that SES is able to quantify both timing precision and event reliability more robustly than classical measures can. As an illustration, neuronal spike data generated by Morris-Lecar neuron model are considered.

Visual processing in the central bee brain

Visual scenes comprise enormous amounts of information from which nervous systems extract behaviorally relevant cues. In most model systems, little is known about the transformation of visual information as it occurs along visual pathways. We examined how visual information is transformed physiologically as it is communicated from the eye to higher-order brain centers using bumblebees, which are known for their visual capabilities. We recorded intracellularly in vivo from 30 neurons in the central bumblebee brain (the lateral protocerebrum) and compared these neurons to 132 neurons from more distal areas along the visual pathway, namely the medulla and the lobula. In these three brain regions (medulla, lobula, and central brain), we examined correlations between the neurons' branching patterns and their responses primarily to color, but also to motion stimuli. Visual neurons projecting to the anterior central brain were generally color sensitive, while neurons projecting to the posterior central brain were predominantly motion sensitive. The temporal response properties differed significantly between these areas, with an increase in spike time precision across trials and a decrease in average reliable spiking as visual information processing progressed from the periphery to the central brain. These data suggest that neurons along the visual pathway to the central brain not only are segregated with regard to the physical features of the stimuli (e.g., color and motion), but also differ in the way they encode stimuli, possibly to allow for efficient parallel processing to occur.

A spike-timing pattern based neural network model for the study of memory dynamics

It is well accepted that the brain's computation relies on spatiotemporal activity of neural networks. In particular, there is growing evidence of the importance of continuously and precisely timed spiking activity. Therefore, it is important to characterize memory states in terms of spike-timing patterns that give both reliable memory of firing activities and precise memory of firing timings. The relationship between memory states and spike-timing patterns has been studied empirically with large-scale recording of neuron population in recent years. Here, by using a recurrent neural network model with dynamics at two time scales, we construct a dynamical memory network model which embeds both fast neural and synaptic variation and slow learning dynamics. A state vector is proposed to describe memory states in terms of spike-timing patterns of neural population, and a distance measure of state vector is defined to study several important phenomena of memory dynamics: partial memory recall, learning efficiency, learning with correlated stimuli. We show that the distance measure can capture the timing difference of memory states. In addition, we examine the influence of network topology on learning ability, and show that local connections can increase the network's ability to embed more memory states. Together theses results suggest that the proposed system based on spike-timing patterns gives a productive model for the study of detailed learning and memory dynamics.

Period-doubling bifurcation in an array of coupled stochastically excitable elements subjected to global periodic forcing

The collective behaviors of coupled, stochastically excitable elements subjected to global periodic forcing are investigated numerically and analytically. We show that the whole system undergoes a period-doubling bifurcation as the driving period decreases, while the individual elements still exhibit random excitations. Using a mean-field representation, we show that this macroscopic bifurcation behavior is caused by interactions between the random excitation, the refractory period, and recruitment (spatial cooperativity) of the excitable elements.

Spatial attention in area V4 is mediated by circuits in primary visual cortex

The ability to covertly select visual stimuli in our environment based on their behavioral relevance is an important skill. Stimulus selection has been studied experimentally, at the single neuron as well as at the population level, by recording from the visual cortex of subjects performing attention-demanding tasks, but studies at the local circuit level are lacking. We conducted simulations of a primary visual cortex (V1) model to provide insight into the local circuit computation underlying stimulus selection in V4. Two small oriented rectangular bars were placed at different locations in the 4 by 4 degree visual field represented by the V1 model, such that they activated different V1 neurons but such that they were both inside the classical receptive field (CRF) of the same V4 neuron. The biased competition framework [Desimone, R., & Duncan, J. (1995). Neural mechanisms of selective visual attention. Annual Review of Neuroscience, 18, 193-222] makes predictions for the response of V4 neurons and the modulation thereof by spatial and feature attention. In our simulation of the V1 network, we obtained results consistent with these predictions for V4 when the model had long-range excitatory projections targeting inhibitory neurons and when spatial attention was mediated by a spatially restricted projection that either inhibited the inhibitory neurons or excited the excitatory neurons. Although it is not clear whether attention effects measured in V4 neurons are generated mostly by local circuits within V4, our simulations suggest that spatial attention at a resolution less than the size of the CRF of a V4 neuron is inherited from upstream areas like V1 and relies on circuits mediating surround suppression at the single neuron level. Furthermore, the model displayed global oscillations in the alpha frequency range (around 10 Hz), whose coherence was highest in the absence of visual stimulation, which is consistent with electroencephalograms recorded in humans. By contrast, when a stimulus was presented the alpha oscillation sped up and became less coherent, whereas at the single column level (40-480 cells) transient beta/gamma oscillations were observed with a frequency between 25 and 50 Hz.

Measuring multiple spike train synchrony

Measures of multiple spike train synchrony are essential in order to study issues such as spike timing reliability, network synchronization, and neuronal coding. These measures can broadly be divided in multivariate measures and averages over bivariate measures. One of the most recent bivariate approaches, the ISI-distance, employs the ratio of instantaneous interspike intervals (ISIs). In this study we propose two extensions of the ISI-distance, the straightforward averaged bivariate ISI-distance and the multivariate ISI-diversity based on the coefficient of variation. Like the original measure these extensions combine many properties desirable in applications to real data. In particular, they are parameter-free, time scale independent, and easy to visualize in a time-resolved manner, as we illustrate with in vitro recordings from a cortical neuron. Using a simulated network of Hindemarsh-Rose neurons as a controlled configuration we compare the performance of our methods in distinguishing different levels of multi-neuron spike train synchrony to the performance of six other previously published measures. We show and explain why the averaged bivariate measures perform better than the multivariate ones and why the multivariate ISI-diversity is the best performer among the multivariate methods. Finally, in a comparison against standard methods that rely on moving window estimates, we use single-unit monkey data to demonstrate the advantages of the instantaneous nature of our methods.

High-frequency, long-range coupling between prefrontal and visual cortex during attention

Electrical recordings in humans and monkeys show attentional enhancement of evoked responses and gamma synchrony in ventral stream cortical areas. Does this synchrony result from intrinsic activity in visual cortex or from inputs from other structures? Using paired recordings in the frontal eye field (FEF) and area V4, we found that attention to a stimulus in their joint receptive field leads to enhanced oscillatory coupling between the two areas, particularly at gamma frequencies. This coupling appeared to be initiated by FEF and was time-shifted by about 8 to 13 milliseconds across a range of frequencies. Considering the expected conduction and synaptic delays between the areas, this time-shifted coupling at gamma frequencies may optimize the postsynaptic impact of spikes from one area upon the other, improving cross-area communication with attention.

Visual perception of ambiguous figures: synchronization based neural models

We develop and study two neural network models of perceptual alternations. Both models have a star-like architecture of connections with a central element connected to a set of peripheral elements. A particular perception is simulated in terms of partial synchronization between the central element and some sub-group of peripheral elements. The first model is constructed from phase oscillators and the mechanism of perceptual alternations is based on chaotic intermittency under fixed parameter values. Similar to experimental evidence, the distribution of times between perceptual alternations is represented by the gamma distribution. The second model is built of spiking neurons of the Hodgkin-Huxley type. The mechanism of perceptual alternations is based on plasticity of inhibitory synapses which increases the inhibition from the central unit to the neural assembly representing the current percept. As a result another perception is formed. Simulations show that the second model is in good agreement with behavioural data on switching times between percepts of ambiguous figures and with experimental results on binocular rivalry of two and four percepts.

Spike timing, spike count, and temporal information for the discrimination of tactile stimuli in the rat ventrobasal complex

The aim of this work was to investigate the role of spike timing for the discrimination of tactile stimuli in the thalamic ventrobasal complex of the rat. We applied information-theoretic measures and computational experiments on neurophysiological data to test the ability of single-neuron responses to discriminate stimulus location and stimulus dynamics using either spike count (40 ms bin size) or spike timing (1 ms bin size). Our main finding is not only that spike timing provides additional information over spike count alone, but specifically that the temporal aspects of the code can be more informative than spike count in the rat ventrobasal complex. Virtually all temporal information--i.e., information exclusively related to when the spikes occur--is conveyed by first spikes, arising mostly from latency differences between the responses to different stimuli. Although the imprecision of first spikes (i.e., the jitter) is highly detrimental for the information conveyed by latency differences, jitter differences can contribute to temporal information, but only if latency differences are close to zero. We conclude that temporal information conveyed by spike timing can be higher than spike count information for the discrimination of somatosensory stimuli in the rat ventrobasal complex.

Competitive STDP-Based Spike Pattern Learning

Recently it has been shown that a repeating arbitrary spatiotemporal spike pattern hidden in equally dense distracter spike trains can be robustly detected and learned by a single neuron equipped with spike-timing-dependent plasticity (STDP) (Masquelier, Guyonneau, & Thorpe, 2008). To be precise, the neuron becomes selective to successive coincidences of the pattern.

Here we extend this scheme to a more realistic scenario with multiple repeating patterns and multiple STDP neurons “listening” to the incoming spike trains. These “listening” neurons are in competition: as soon as one fires, it strongly inhibits the others through lateral connections (one-winner-take-all mechanism). This tends to prevent the neurons from learning the same (parts of the) repeating patterns, as shown in simulations. Instead, the population self-organizes, trying to cover the different patterns or coding one pattern by the successive firings of several neurons, and a powerful distributed coding scheme emerges.

Taken together, these results illustrate how the brain could easily encode and decode information in the spike times, a theory referred to as temporal coding, and how STDP could play a key role by detecting repeating patterns and generating selective response to them.

Rapid neocortical dynamics: cellular and network mechanisms

The highly interconnected local and large-scale networks of the neocortical sheet rapidly and dynamically modulate their functional connectivity according to behavioral demands. This basic operating principle of the neocortex is mediated by the continuously changing flow of excitatory and inhibitory synaptic barrages that not only control participation of neurons in networks but also define the networks themselves. The rapid control of neuronal responsiveness via synaptic bombardment is a fundamental property of cortical dynamics that may provide the basis of diverse behaviors, including sensory perception, motor integration, working memory, and attention.

A spike-timing code for discriminating conspecific vocalizations in the thalamocortical system of anesthetized and awake guinea pigs

Understanding how communication sounds are processed and encoded in the central auditory system is critical to understanding the neural bases of acoustic communication. Here, we examined neuronal representations of species-specific vocalizations, which are communication sounds that many species rely on for survival and social interaction. In some species, the evoked responses of auditory cortex neurons are stronger in response to natural conspecific vocalizations than to their time-reversed, spectrally identical, counterparts. We applied information theory-based analyses to single-unit spike trains collected in the auditory cortex (n = 139) and auditory thalamus (n = 135) of anesthetized animals as well as in the auditory cortex (n = 119) of awake guinea pigs during presentation of four conspecific vocalizations. Few thalamic and cortical cells (<10%) displayed a firing rate preference for the natural version of these vocalizations. In contrast, when the information transmitted by the spike trains was quantified with a temporal precision of 10-50 ms, many cells (>75%) displayed a significant amount of information (i.e., >2SD above chance levels), especially in the awake condition. The computed correlation index between spike trains (R(corr), defined by Schreiber et al., 2003) indicated similar spike-timing reliability for both the natural and time-reversed versions of each vocalization, but higher reliability for awake animals compared with anesthetized animals. Based on temporal discharge patterns, even cells that were only weakly responsive to vocalizations displayed a significant level of information. These findings emphasize the importance of temporal discharge patterns as a coding mechanism for natural communication sounds, particularly in awake animals.

Compensating time delays with neural predictions: are predictions sensory or motor?

Neural delays are a general property of computations carried out by neural circuits. Delays are a natural consequence of temporal summation and coding used by the nervous system to integrate information from multiple resources. For adaptive behaviour, however, these delays must be compensated. In order to sense and interact with moving objects, for example, the visual system must predict the future position of the object to compensate for delays. In this paper, we address two critical questions concerning the implementation of the compensation mechanisms in the brain, namely, where does compensation occur and how is it realized. We present evidence showing that compensation can happen in both the motor and sensory systems, and that compensation using 'diagonal neural pathways' is a suitable strategy for implementing compensation in the visual system. In this strategy, neural signals in the early stage of information processing are sent to the future cortical positions that correspond to the distance the object will travel in the period of transmission delay. We propose a computational model to elucidate this using the retinal visual information pathway.

Timing precision in population coding of natural scenes in the early visual system

The timing of spiking activity across neurons is a fundamental aspect of the neural population code. Individual neurons in the retina, thalamus, and cortex can have very precise and repeatable responses but exhibit degraded temporal precision in response to suboptimal stimuli. To investigate the functional implications for neural populations in natural conditions, we recorded in vivo the simultaneous responses, to movies of natural scenes, of multiple thalamic neurons likely converging to a common neuronal target in primary visual cortex. We show that the response of individual neurons is less precise at lower contrast, but that spike timing precision across neurons is relatively insensitive to global changes in visual contrast. Overall, spike timing precision within and across cells is on the order of 10 ms. Since closely timed spikes are more efficient in inducing a spike in downstream cortical neurons, and since fine temporal precision is necessary to represent the more slowly varying natural environment, we argue that preserving relative spike timing at a ∼10-ms resolution is a crucial property of the neural code entering cortex.

Neurons convey information about the world in the form of trains of action potentials (spikes). These trains are highly repeatable when the same stimulus is presented multiple times, and this temporal precision across repetitions can be as fine as a few milliseconds. It is usually assumed that this time scale also corresponds to the timing precision of several neighboring neurons firing in concert. However, the relative timing of spikes emitted by different neurons in a local population is not necessarily as fine as the temporal precision across repetitions within a single neuron. In the visual system of the brain, the level of contrast in the image entering the retina can affect single-neuron temporal precision, but the effects of contrast on the neural population code are unknown. Here we show that the temporal scale of the population code entering visual cortex is on the order of 10 ms and is largely insensitive to changes in visual contrast. Since closely timed spikes are more efficient in inducing a spike in downstream cortical neurons, and since fine temporal precision is necessary in representing the more slowly varying natural environment, preserving relative spike timing at a ∼10-ms resolution may be a crucial property of the neural code entering cortex.

Early neural representation of visual scenes occurs with a temporal precision on the order of 10 ms, which is precise enough to strongly drive downstream neurons in the visual pathway. Unlike individual neurons, the neural population code is largely insensitive to pronounced changes in visual contrast.

Data-driven significance estimation for precise spike correlation

The mechanisms underlying neuronal coding and, in particular, the role of temporal spike coordination are hotly debated. However, this debate is often confounded by an implicit discussion about the use of appropriate analysis methods. To avoid incorrect interpretation of data, the analysis of simultaneous spike trains for precise spike correlation needs to be properly adjusted to the features of the experimental spike trains. In particular, nonstationarity of the firing of individual neurons in time or across trials, a spike train structure deviating from Poisson, or a co-occurrence of such features in parallel spike trains are potent generators of false positives. Problems can be avoided by including these features in the null hypothesis of the significance test. In this context, the use of surrogate data becomes increasingly important, because the complexity of the data typically prevents analytical solutions. This review provides an overview of the potential obstacles in the correlation analysis of parallel spike data and possible routes to overcome them. The discussion is illustrated at every stage of the argument by referring to a specific analysis tool (the Unitary Events method). The conclusions, however, are of a general nature and hold for other analysis techniques. Thorough testing and calibration of analysis tools and the impact of potentially erroneous preprocessing stages are emphasized.

Long-range neural coupling through synchronization with attention

In a crowded visual scene, we typically employ attention to select stimuli that are behaviorally relevant. Two likely cortical sources of top-down attentional feedback to cortical visual areas are the prefrontal (PFC) and posterior parietal (PPC) cortices. Recent neurophysiological studies show that areas in PFC and PPC process signals about the locus of attention earlier than in extrastriate visual areas and are therefore likely to mediate attentional selection. Moreover, attentional selection appears to be mediated in part by neural synchrony between neurons in PFC/PPC and early visual areas, with phase relationships that seem optimal for increasing the impact of the top-down inputs to the visual cortex.

Temporal precision of spike response to fluctuating input in pulse-coupled networks of oscillating neurons

A single neuron is known to generate almost identical spike trains when the same fluctuating input is repeatedly applied. Here, we study the reliability of spike firing in a pulse-coupled network of oscillator neurons receiving fluctuating inputs. We can study the precise responses of the network as synchronization between uncoupled copies of the network by a common noisy input. To study the noise-induced synchronization between networks, we derive a self-consistent equation for the distribution of spike-time differences between the networks. Solving this equation, we elucidate how the spike precision changes as a function of the coupling strength.

Information transmission in oscillatory neural activity

Periodic neural activity not locked to the stimulus or to motor responses is usually ignored. Here, we present new tools for modeling and quantifying the information transmission based on periodic neural activity that occurs with quasi-random phase relative to the stimulus. We propose a model to reproduce characteristic features of oscillatory spike trains, such as histograms of inter-spike intervals and phase locking of spikes to an oscillatory influence. The proposed model is based on an inhomogeneous Gamma process governed by a density function that is a product of the usual stimulus-dependent rate and a quasi-periodic function. Further, we present an analysis method generalizing the direct method (Rieke et al. in Spikes: exploring the neural code. MIT Press, Cambridge, 1999; Brenner et al. in Neural Comput 12(7):1531-1552, 2000) to assess the information content in such data. We demonstrate these tools on recordings from relay cells in the lateral geniculate nucleus of the cat.

Dynamical relaying can yield zero time lag neuronal synchrony despite long conduction delays

Multielectrode recordings have revealed zero time lag synchronization among remote cerebral cortical areas. However, the axonal conduction delays among such distant regions can amount to several tens of milliseconds. It is still unclear which mechanism is giving rise to isochronous discharge of widely distributed neurons, despite such latencies. Here, we investigate the synchronization properties of a simple network motif and found that, even in the presence of large axonal conduction delays, distant neuronal populations self-organize into lag-free oscillations. According to our results, cortico-cortical association fibers and certain cortico-thalamo-cortical loops represent ideal circuits to circumvent the phase shifts and time lags associated with conduction delays.

The processing of color, motion, and stimulus timing are anatomically segregated in the bumblebee brain

Animals use vision to perform such diverse behaviors as finding food, interacting socially with other animals, choosing a mate, and avoiding predators. These behaviors are complex and the visual system must process color, motion, and pattern cues efficiently so that animals can respond to relevant stimuli. The visual system achieves this by dividing visual information into separate pathways, but to what extent are these parallel streams separated in the brain? To answer this question, we recorded intracellularly in vivo from 105 morphologically identified neurons in the lobula, a major visual processing structure of bumblebees (Bombus impatiens). We found that these cells have anatomically segregated dendritic inputs confined to one or two of six lobula layers. Lobula neurons exhibit physiological characteristics common to their respective input layer. Cells with arborizations in layers 1-4 are generally indifferent to color but sensitive to motion, whereas layer 5-6 neurons often respond to both color and motion cues. Furthermore, the temporal characteristics of these responses differ systematically with dendritic branching pattern. Some layers are more temporally precise, whereas others are less precise but more reliable across trials. Because different layers send projections to different regions of the central brain, we hypothesize that the anatomical layers of the lobula are the structural basis for the segregation of visual information into color, motion, and stimulus timing.

Reliability, synchrony and noise

The brain is noisy. Neurons receive tens of thousands of highly fluctuating inputs and generate spike trains that appear highly irregular. Much of this activity is spontaneous - uncoupled to overt stimuli or motor outputs - leading to questions about the functional impact of this noise. Although noise is most often thought of as disrupting patterned activity and interfering with the encoding of stimuli, recent theoretical and experimental work has shown that noise can play a constructive role - leading to increased reliability or regularity of neuronal firing in single neurons and across populations. These results raise fundamental questions about how noise can influence neural function and computation.

Synchrony between orientation-selective neurons is modulated during adaptation-induced plasticity in cat visual cortex

BACKGROUND

Visual neurons respond essentially to luminance variations occurring within their receptive fields. In primary visual cortex, each neuron is a filter for stimulus features such as orientation, motion direction and velocity, with the appropriate combination of features eliciting maximal firing rate. Temporal correlation of spike trains was proposed as a potential code for linking the neuronal responses evoked by various features of a same object. In the present study, synchrony strength was measured between cells following an adaptation protocol (prolonged exposure to a non-preferred stimulus) which induce plasticity of neurons' orientation preference.

RESULTS

Multi-unit activity from area 17 of anesthetized adult cats was recorded. Single cells were sorted out and (1) orientation tuning curves were measured before and following 12 min adaptation and 60 min after adaptation (2) pairwise synchrony was measured by an index that was normalized in relation to the cells' firing rate. We first observed that the prolonged presentation of a non-preferred stimulus produces attractive (58%) and repulsive (42%) shifts of cell's tuning curves. It follows that the adaptation-induced plasticity leads to changes in preferred orientation difference, i.e. increase or decrease in tuning properties between neurons. We report here that, after adaptation, the neuron pairs that shared closer tuning properties display a significant increase of synchronization. Recovery from adaptation was accompanied by a return to the initial synchrony level.

CONCLUSION

We conclude that synchrony reflects the similarity in neurons' response properties, and varies accordingly when these properties change.

Intelligence and EEG phase reset: a two compartmental model of phase shift and lock

OBJECTIVES

The purpose of this study was to explore the relationship between EEG phase reset and performance on the Wechsler Intelligence test.

METHODS

The electroencephalogram (EEG) was recorded from 19 scalp locations from 378 subjects ranging in age from 5 years to 17.6 years. The Wechsler Intelligence test (WISC-R) was administered to the same subjects on the same day but not while the EEG was recorded. Complex demodulation was used to compute instantaneous EEG phase differences between pairs of electrodes and the 1st and 2nd derivatives were used to measure phase reset by phase shift duration and phase lock duration. The dependent variable was full scale I.Q. and the independent variables were phase shift duration (SD) and phase lock duration (LD) with age as a covariate.

RESULTS

Phase shift duration (40-90 ms) was positively related to intelligence (P<.00001) and the phase lock duration (100-800 ms) was negatively related to intelligence (P<.00001). Phase reset in short interelectrode distances (6 cm) was more highly correlated to I.Q. (P<.0001) than in long distances (>12 cm).

CONCLUSIONS

The duration of unstable phase dynamics and phase locking represent a bounded optimization process, for example, too long a duration of phase locking then less flexibility and too short of a phase shift then reduced neural resources. A two compartmental model of local field coupling and neuron synchrony to a preferred phase was developed to explain the findings.