Robust temporal coding of contrast by V1 neurons for transient but not for steady-state stimuli

Ballistocardiogram suppression in concurrent EEG-MRI by dynamic modeling of heartbeats

The ballistocardiogram (BCG), the induced electric potentials by the head motion originating from heartbeats, is a prominent source of noise in electroencephalography (EEG) data during magnetic resonance imaging (MRI). Although methods have been proposed to suppress the BCG artifact, more work considering the variability of cardiac cycles and head motion across time and subjects is needed to provide highly robust correction. Here, a method called "dynamic modeling of heartbeats" (DMH) is proposed to reduce BCG artifacts in EEG data recorded inside an MRI system. The DMH method models BCG artifacts by combining EEG points at time instants with similar dynamics. The modeled BCG artifact is then subtracted from the EEG recording to suppress the BCG artifact. Performance of DMH was tested and specifically compared with the Optimal Basis Set (OBS) method on EEG data recorded inside a 3T MRI system with either no MRI acquisition (Inside-MRI), echo-planar imaging (EPI-EEG), or fast MRI acquisition using simultaneous multi-slice and inverse imaging methods (SMS-InI-EEG). In a steady-state visual evoked response (SSVEP) paradigm, the 15-Hz oscillatory neuronal activity at the visual cortex after DMH processing was about 130% of that achieved by OBS processing for Inside-MRI, SMS-InI-EEG, and EPI-EEG conditions. The DMH method is computationally efficient for suppressing BCG artifacts and in the future may help to improve the quality of EEG data recorded in high-field MRI systems for neuroscientific and clinical applications.

Multivariate Identification of Functional Neural Networks Underpinning Humorous Movie Viewing

While univariate functional magnetic resonance imaging (fMRI) data analysis methods have been utilized successfully to map brain areas associated with cognitive and emotional functions during viewing of naturalistic stimuli such as movies, multivariate methods might provide the means to study how brain structures act in concert as networks during free viewing of movie clips. Here, to achieve this, we generalized the partial least squares (PLS) analysis, based on correlations between voxels, experimental conditions, and behavioral measures, to identify large-scale neuronal networks activated during the first time and repeated watching of three ∼5-min comedy clips. We identified networks that were similarly activated across subjects during free viewing of the movies, including the ones associated with self-rated experienced humorousness that were composed of the frontal, parietal, and temporal areas acting in concert. In conclusion, the PLS method seems to be well suited for the joint analysis of multi-subject neuroimaging and behavioral data to quantify a functionally relevant brain network activity without the need for explicit temporal models.

Inter-Subject Analysis: A Partial Gaussian Graphical Model Approach

Different from traditional intra-subject analysis, the goal of inter-subject analysis (ISA) is to explore the dependency structure between different subjects with the intra-subject dependency as nuisance. ISA has important applications in neuroscience to study the functional connectivity between brain regions under natural stimuli. We propose a modeling framework for ISA that is based on Gaussian graphical models, under which ISA can be converted to the problem of estimation and inference of a partial Gaussian graphical model. The main statistical challenge is that we do not impose sparsity constraints on the whole precision matrix and we only assume the inter-subject part is sparse. For estimation, we propose to estimate an alternative parameter to get around the nonsparse issue and it can achieve asymptotic consistency even if the intra-subject dependency is dense. For inference, we propose an "untangle and chord" procedure to de-bias our estimator. It is valid without the sparsity assumption on the inverse Hessian of the log-likelihood function. This inferential method is general and can be applied to many other statistical problems, thus it is of independent theoretical interest. Numerical experiments on both simulated and brain imaging data validate our methods and theory. Supplementary materials for this article are available online.

Attention amplifies neural representations of changes in sensory input at the expense of perceptual accuracy

Attention enhances the neural representations of behaviorally relevant stimuli, typically by a push-pull increase of the neuronal response gain to attended vs. unattended stimuli. This selectively improves perception and consequently behavioral performance. However, to enhance the detectability of stimulus changes, attention might also distort neural representations, compromising accurate stimulus representation. We test this hypothesis by recording neural responses in the visual cortex of rhesus monkeys during a motion direction change detection task. We find that attention indeed amplifies the neural representation of direction changes, beyond a similar effect of adaptation. We further show that humans overestimate such direction changes, providing a perceptual correlate of our neurophysiological observations. Our results demonstrate that attention distorts the neural representations of abrupt sensory changes and consequently perceptual accuracy. This likely represents an evolutionary adaptive mechanism that allows sensory systems to flexibly forgo accurate representation of stimulus features to improve the encoding of stimulus change.

A Spike Train Distance Robust to Firing Rate Changes Based on the Earth Mover's Distance

Neural spike train analysis methods are mainly used for understanding the temporal aspects of neural information processing. One approach is to measure the dissimilarity between the spike trains of a pair of neurons, often referred to as the spike train distance. The spike train distance has been often used to classify neuronal units with similar temporal patterns. Several methods to compute spike train distance have been developed so far. Intuitively, a desirable distance should be the shortest length between two objects. The Earth Mover’s Distance (EMD) can compute spike train distance by measuring the shortest length between two spike trains via shifting a fraction of spikes from one spike train to another. The EMD could accurately measure spike timing differences, temporal similarity, and spikes time synchrony. It is also robust to firing rate changes. Victor and Purpura (1996) distance measures the minimum cost between two spike trains. Although it also measures the shortest path between spike trains, its output can vary with the time-scale parameter. In contrast, the EMD measures distance in a unique way by calculating the genuine shortest length between spike trains. The EMD also outperforms other existing spike train distance methods in measuring various aspects of the temporal characteristics of spike trains and in robustness to firing rate changes. The EMD can effectively measure the shortest length between spike trains without being considerably affected by the overall firing rate difference between them. Hence, it is suitable for pure temporal coding exclusively, which is a predominant premise underlying the present study.

Elements of qualitative cognition: An information topology perspective

Elementary quantitative and qualitative aspects of consciousness are investigated conjointly from the biology, neuroscience, physic and mathematic point of view, by the mean of a theory written with Bennequin that derives and extends information theory within algebraic topology. Information structures, that accounts for statistical dependencies within n-body interacting systems are interpreted a la Leibniz as a monadic-panpsychic framework where consciousness is information and physical, and arise from collective interactions. The electrodynamic intrinsic nature of consciousness, sustained by an analogical code, is illustrated by standard neuroscience and psychophysic results. It accounts for the diversity of the learning mechanisms, including adaptive and homeostatic processes on multiple scales, and details their expression within information theory. The axiomatization and logic of cognition are rooted in measure theory expressed within a topos intrinsic probabilistic constructive logic. Information topology provides a synthesis of the main models of consciousness (Neural Assemblies, Integrated Information, Global Neuronal Workspace, Free Energy Principle) within a formal Gestalt theory, an expression of information structures and patterns in correspondence with Galois cohomology and discrete symmetries. The methods provide new formalization of deep neural network with homologicaly imposed architecture applied to challenges in AI-machine learning.

Invariant neural responses for sensory categories revealed by the time-varying information for communication calls

Although information theoretic approaches have been used extensively in the analysis of the neural code, they have yet to be used to describe how information is accumulated in time while sensory systems are categorizing dynamic sensory stimuli such as speech sounds or visual objects. Here, we present a novel method to estimate the cumulative information for stimuli or categories. We further define a time-varying categorical information index that, by comparing the information obtained for stimuli versus categories of these same stimuli, quantifies invariant neural representations. We use these methods to investigate the dynamic properties of avian cortical auditory neurons recorded in zebra finches that were listening to a large set of call stimuli sampled from the complete vocal repertoire of this species. We found that the time-varying rates carry 5 times more information than the mean firing rates even in the first 100 ms. We also found that cumulative information has slow time constants (100–600 ms) relative to the typical integration time of single neurons, reflecting the fact that the behaviorally informative features of auditory objects are time-varying sound patterns. When we correlated firing rates and information values, we found that average information correlates with average firing rate but that higher-rates found at the onset response yielded similar information values as the lower-rates found in the sustained response: the onset and sustained response of avian cortical auditory neurons provide similar levels of independent information about call identity and call-type. Finally, our information measures allowed us to rigorously define categorical neurons; these categorical neurons show a high degree of invariance for vocalizations within a call-type. Peak invariance is found around 150 ms after stimulus onset. Surprisingly, call-type invariant neurons were found in both primary and secondary avian auditory areas.

Just as the recognition of faces requires neural representations that are invariant to scale and rotation, the recognition of behaviorally relevant auditory objects, such as spoken words, requires neural representations that are invariant to the speaker uttering the word and to his or her location. Here, we used information theory to investigate the time course of the neural representation of bird communication calls and of behaviorally relevant categories of these same calls: the call-types of the bird’s repertoire. We found that neurons in both the primary and secondary avian auditory cortex exhibit invariant responses to call renditions within a call-type, suggestive of a potential role for extracting the meaning of these communication calls. We also found that time plays an important role: first, neural responses carry significantly more information when represented by temporal patterns calculated at the small time scale of 10 ms than when measured as average rates and, second, this information accumulates in a non-redundant fashion up to long integration times of 600 ms. This rich temporal neural representation is matched to the temporal richness found in the communication calls of this species.

Differential brain mechanisms during reading human vs. machine translated fiction and news texts

Few neuroimaigng studies on reading comprehension have been conducted under natural reading settings. In this study, we showed texts presented in a natural way during functional MRI (fMRI) measurements to reveal brain areas sensitive to reading comprehension. Specifically, this paradigm independently manipulated two holistic features of article style: text genre and translation style, a qualitative index of how typical word choices and arrangements are made in daily use of the language. Specifically, articles from The New York Times (news) and Reader’s Digest (fiction) translated from English to Mandarin Chinese either by human experts or machine (Google Translate) were used to investigate the correlation of brain activity across participants during article reading. We found that bi-hemispheric visual cortex, precuneus, and occipito-parietal junction show significantly correlated hemodynamics across participants regardless of translation style and article genre. Compared to machine translation, reading human expert translation elicited more reliable fMRI signals across participants at precuneus, potentially because narrative representations and contents can be coherently presented over tens of seconds. We also found significantly stronger inter-subject correlated fMRI signals at temporal poles and fusiform gyri in fiction reading than in news reading. This may be attributed to more stable empathy processing across participants in fiction reading. The degree of stability of brain responses across subjects at extra-linguistic areas was found correlated with subjective rating on the text fluency. The functional connectivity between these areas was modulated by text genre and translation style. Taken together, our imaging results suggested stable and selective neural substrates associated with comprehending holistic features of written narratives.

Influences of High-Level Features, Gaze, and Scene Transitions on the Reliability of BOLD Responses to Natural Movie Stimuli

Complex, sustained, dynamic, and naturalistic visual stimulation can evoke distributed brain activities that are highly reproducible within and across individuals. However, the precise origins of such reproducible responses remain incompletely understood. Here, we employed concurrent functional magnetic resonance imaging (fMRI) and eye tracking to investigate the experimental and behavioral factors that influence fMRI activity and its intra- and inter-subject reproducibility during repeated movie stimuli. We found that widely distributed and highly reproducible fMRI responses were attributed primarily to the high-level natural content in the movie. In the absence of such natural content, low-level visual features alone in a spatiotemporally scrambled control stimulus evoked significantly reduced degree and extent of reproducible responses, which were mostly confined to the primary visual cortex (V1). We also found that the varying gaze behavior affected the cortical response at the peripheral part of V1 and in the oculomotor network, with minor effects on the response reproducibility over the extrastriate visual areas. Lastly, scene transitions in the movie stimulus due to film editing partly caused the reproducible fMRI responses at widespread cortical areas, especially along the ventral visual pathway. Therefore, the naturalistic nature of a movie stimulus is necessary for driving highly reliable visual activations. In a movie-stimulation paradigm, scene transitions and individuals’ gaze behavior should be taken as potential confounding factors in order to properly interpret cortical activity that supports natural vision.

Feed-Forward Propagation of Temporal and Rate Information between Cortical Populations during Coherent Activation in Engineered In Vitro Networks

Transient propagation of information across neuronal assembles is thought to underlie many cognitive processes. However, the nature of the neural code that is embedded within these transmissions remains uncertain. Much of our understanding of how information is transmitted among these assemblies has been derived from computational models. While these models have been instrumental in understanding these processes they often make simplifying assumptions about the biophysical properties of neurons that may influence the nature and properties expressed. To address this issue we created an in vitro analog of a feed-forward network composed of two small populations (also referred to as assemblies or layers) of living dissociated rat cortical neurons. The populations were separated by, and communicated through, a microelectromechanical systems (MEMS) device containing a strip of microscale tunnels. Delayed culturing of one population in the first layer followed by the second a few days later induced the unidirectional growth of axons through the microtunnels resulting in a primarily feed-forward communication between these two small neural populations. In this study we systematically manipulated the number of tunnels that connected each layer and hence, the number of axons providing communication between those populations. We then assess the effect of reducing the number of tunnels has upon the properties of between-layer communication capacity and fidelity of neural transmission among spike trains transmitted across and within layers. We show evidence based on Victor-Purpura's and van Rossum's spike train similarity metrics supporting the presence of both rate and temporal information embedded within these transmissions whose fidelity increased during communication both between and within layers when the number of tunnels are increased. We also provide evidence reinforcing the role of synchronized activity upon transmission fidelity during the spontaneous synchronized network burst events that propagated between layers and highlight the potential applications of these MEMs devices as a tool for further investigation of structure and functional dynamics among neural populations.

Modification of Brain Oscillations via Rhythmic Light Stimulation Provides Evidence for Entrainment but Not for Superposition of Event-Related Responses

The functional relevance of brain oscillations in the alpha frequency range (8-13 Hz) has been repeatedly investigated through the use of rhythmic visual stimulation. The underlying mechanism of the steady-state visual evoked potential (SSVEP) measured in EEG during rhythmic stimulation, however, is not known. There are two hypotheses on the origin of SSVEPs: entrainment of brain oscillations and superposition of event-related responses (ERPs). The entrainment but not the superposition hypothesis justifies rhythmic visual stimulation as a means to manipulate brain oscillations, because superposition assumes a linear summation of single responses, independent from ongoing brain oscillations. Here, we stimulated participants with a rhythmic flickering light of different frequencies and intensities. We measured entrainment by comparing the phase coupling of brain oscillations stimulated by rhythmic visual flicker with the oscillations induced by arrhythmic jittered stimulation, varying the time, stimulation frequency, and intensity conditions. In line with a theoretical concept of entrainment (the so called Arnold tongue), we found the phase coupling to be more pronounced with increasing stimulation intensity as well as at stimulation frequencies closer to each participant's intrinsic frequency. Only inside the Arnold tongue did the conditions significantly differ from the jittered stimulation. Furthermore, even in a single sequence of an SSVEP, we found non-linear features (intermittency of phase locking) that contradict the linear summation of single responses, as assumed by the superposition hypothesis. Our findings provide unequivocal evidence that visual rhythmic stimulation entrains brain oscillations, thus validating the approach of rhythmic stimulation as a manipulation of brain oscillations.

Transient activity in monkey area MT represents speed changes and is correlated with human behavioral performance

Neurons in the middle temporal area (MT) respond to motion onsets and speed changes with a transient-sustained firing pattern. The latency of the transient response has recently been shown to correlate with reaction time in a speed change detection task, but it is not known how the sign, the amplitude, and the latency of this response depend on the sign and the magnitude of a speed change, and whether these transients can be decoded to explain speed change detection behavior. To investigate this issue, we measured the neuronal representation of a wide range of positive and negative speed changes in area MT of fixating macaques and obtained three major findings. First, speed change transients not only reflect a neuron's absolute speed tuning but are shaped by an additional gain that scales the tuned response according to the magnitude of a relative speed change. Second, by means of a threshold model positive and negative population transients of a moderate number of MT neurons explain detection of both positive and negative speed changes, respectively, at a level comparable to human detection rates under identical visual stimulation. Third, like reaction times in a psychophysical model of velocity detection, speed change response latencies follow a power-law function of the absolute difference of a speed change. Both this neuronal representation and its close correlation with behavioral measures of speed change detection suggest that neuronal transients in area MT facilitate the detection of rapid changes in visual input.

Survey of encoding and decoding of visual stimulus via FMRI: an image analysis perspective

A variety of exciting scientific achievements have been made in the last few decades in brain encoding and decoding via functional magnetic resonance imaging (fMRI). This trend continues to rise in recent years, as evidenced by the increasing number of published papers in this topic and several published survey papers addressing different aspects of research issues. Essentially, these survey articles were mainly from cognitive neuroscience and neuroimaging perspectives, although computational challenges were briefly discussed. To complement existing survey articles, this paper focuses on the survey of the variety of image analysis methodologies, such as neuroimage registration, fMRI signal analysis, ROI (regions of interest) selection, machine learning algorithms, reproducibility analysis, structural and functional connectivity, and natural image analysis, which were employed in previous brain encoding/decoding research works. This paper also provides discussions of potential limitations of those image analysis methodologies and possible future improvements. It is hoped that extensive discussions of image analysis issues could contribute to the advancements of the increasingly important brain encoding/decoding field.

Temporally-patterned deep brain stimulation in a mouse model of multiple traumatic brain injury

We report that mice with closed-head multiple traumatic brain injury (TBI) show a decrease in the motoric aspects of generalized arousal, as measured by automated, quantitative behavioral assays. Further, we found that temporally-patterned deep brain stimulation (DBS) can increase generalized arousal and spontaneous motor activity in this mouse model of TBI. This arousal increase is input-pattern-dependent, as changing the temporal pattern of DBS can modulate its effect on motor activity. Finally, an extensive examination of mouse behavioral capacities, looking for deficits in this model of TBI, suggest that the strongest effects of TBI in this model are found in the initiation of any kind of movement.

Data-driven analysis of functional brain interactions during free listening to music and speech

Natural stimulus functional magnetic resonance imaging (N-fMRI) such as fMRI acquired when participants were watching video streams or listening to audio streams has been increasingly used to investigate functional mechanisms of the human brain in recent years. One of the fundamental challenges in functional brain mapping based on N-fMRI is to model the brain's functional responses to continuous, naturalistic and dynamic natural stimuli. To address this challenge, in this paper we present a data-driven approach to exploring functional interactions in the human brain during free listening to music and speech streams. Specifically, we model the brain responses using N-fMRI by measuring the functional interactions on large-scale brain networks with intrinsically established structural correspondence, and perform music and speech classification tasks to guide the systematic identification of consistent and discriminative functional interactions when multiple subjects were listening music and speech in multiple categories. The underlying premise is that the functional interactions derived from N-fMRI data of multiple subjects should exhibit both consistency and discriminability. Our experimental results show that a variety of brain systems including attention, memory, auditory/language, emotion, and action networks are among the most relevant brain systems involved in classic music, pop music and speech differentiation. Our study provides an alternative approach to investigating the human brain's mechanism in comprehension of complex natural music and speech.

Neural mechanism for sensing fast motion in dim light

Luminance is a fundamental property of visual scenes. A population of neurons in primary visual cortex (V1) is sensitive to uniform luminance. In natural vision, however, the retinal image often changes rapidly. Consequently the luminance signals visual cells receive are transiently varying. How V1 neurons respond to such luminance changes is unknown. By applying large static uniform stimuli or grating stimuli altering at 25 Hz that resemble the rapid luminance changes in the environment, we show that approximately 40% V1 cells responded to rapid luminance changes of uniform stimuli. Most of them strongly preferred luminance decrements. Importantly, when tested with drifting gratings, the preferred speeds of these cells were significantly higher than cells responsive to static grating stimuli but not to uniform stimuli. This responsiveness can be accounted for by the preferences for low spatial frequencies and high temporal frequencies. These luminance-sensitive cells subserve the detection of fast motion under the conditions of dim illumination.

Shifted encoding strategy in retinal luminance adaptation: from firing rate to neural correlation

Neuronal responses to prolonged stimulation attenuate over time. Here, we ask a fundamental question: is adaptation a simple process for the neural system during which sustained input is ignored, or is it actually part of a strategy for the neural system to adjust its encoding properties dynamically? After simultaneously recording the activities of a group of bullfrog's retinal ganglion cells (dimming detectors) in response to sustained dimming stimulation, we applied a combination of information analysis approaches to explore the time-dependent nature of information encoding during the adaptation. We found that at the early stage of the adaptation, the stimulus information was mainly encoded in firing rates, whereas at the late stage of the adaptation, it was more encoded in neural correlations. Such a transition in encoding properties is not a simple consequence of the attenuation of neuronal firing rates, but rather involves an active change in the neural correlation strengths, suggesting that it is a strategy adopted by the neural system for functional purposes. Our results reveal that in encoding a prolonged stimulation, the neural system may utilize concerted, but less active, firings of neurons to encode information.

Sensory processing during viewing of cinematographic material: computational modeling and functional neuroimaging

The investigation of brain activity using naturalistic, ecologically-valid stimuli is becoming an important challenge for neuroscience research. Several approaches have been proposed, primarily relying on data-driven methods (e.g. independent component analysis, ICA). However, data-driven methods often require some post-hoc interpretation of the imaging results to draw inferences about the underlying sensory, motor or cognitive functions. Here, we propose using a biologically-plausible computational model to extract (multi-)sensory stimulus statistics that can be used for standard hypothesis-driven analyses (general linear model, GLM). We ran two separate fMRI experiments, which both involved subjects watching an episode of a TV-series. In Exp 1, we manipulated the presentation by switching on-and-off color, motion and/or sound at variable intervals, whereas in Exp 2, the video was played in the original version, with all the consequent continuous changes of the different sensory features intact. Both for vision and audition, we extracted stimulus statistics corresponding to spatial and temporal discontinuities of low-level features, as well as a combined measure related to the overall stimulus saliency. Results showed that activity in occipital visual cortex and the superior temporal auditory cortex co-varied with changes of low-level features. Visual saliency was found to further boost activity in extra-striate visual cortex plus posterior parietal cortex, while auditory saliency was found to enhance activity in the superior temporal cortex. Data-driven ICA analyses of the same datasets also identified "sensory" networks comprising visual and auditory areas, but without providing specific information about the possible underlying processes, e.g., these processes could relate to modality, stimulus features and/or saliency. We conclude that the combination of computational modeling and GLM enables the tracking of the impact of bottom-up signals on brain activity during viewing of complex and dynamic multisensory stimuli, beyond the capability of purely data-driven approaches.

A novel, jitter-based method for detecting and measuring spike synchrony and quantifying temporal firing precision

BACKGROUND

Precise spike synchrony, at the millisecond or even sub-millisecond time scale, has been reported in different brain areas, but its neurobiological meaning and its underlying mechanisms remain unknown or controversial. Studying these questions is complicated by the lack of a validated, well-normalized and robust index for quantifying synchrony. Previously used measures of synchrony are often improperly normalized and thereby are not comparable between different experimental conditions, are sensitive to variations in firing rate or to the firing rate differential between the two neurons, and/or rely on untenable assumptions of firing rate stationarity and Poisson statistics. I describe here a novel measure, the Jitter-Based Synchrony Index (JBSI), that overcomes these issues.

RESULTS AND DISCUSSION

The JBSI method is based on the introduction of virtual spike jitter. While previous implementations of the jitter method used it only to detect synchrony, the JBSI method also quantifies synchrony. Previous implementations of the jitter method used computationally intensive Monte Carlo simulations to generate surrogate spike trains, whereas the JBSI is computed analytically. The JBSI method does not assume any specific firing model, and does not require that the spike trains be locked to a repeating external stimulus. The JBSI can assume values from 1 (maximal possible synchrony) to -1 (minimal possible synchrony) and is therefore properly normalized. Using simulated Poisson spike trains with introduced controlled spike coincidences, I demonstrate that the JBSI is a linear measure of the spike coincidence rate, is independent of the mean firing frequency or the firing frequency differential between the two neurons, and is not sensitive to co-modulations in the firing rates of the two neurons. In contrast, several commonly used synchrony indices fail under one or more of these scenarios. I also demonstrate how the JBSI can be used to estimate the spike timing precision in the system.

CONCLUSIONS

The JBSI is a conceptually simple and computationally efficient method that can be used to compute the statistical significance of firing synchrony, to quantify synchrony as a well-normalized index, and to estimate the degree of temporal precision in the system.

Low error discrimination using a correlated population code

We explored the manner in which spatial information is encoded by retinal ganglion cell populations. We flashed a set of 36 shape stimuli onto the tiger salamander retina and used different decoding algorithms to read out information from a population of 162 ganglion cells. We compared the discrimination performance of linear decoders, which ignore correlation induced by common stimulation, with nonlinear decoders, which can accurately model these correlations. Similar to previous studies, decoders that ignored correlation suffered only a modest drop in discrimination performance for groups of up to ∼30 cells. However, for more realistic groups of 100+ cells, we found order-of-magnitude differences in the error rate. We also compared decoders that used only the presence of a single spike from each cell with more complex decoders that included information from multiple spike counts and multiple time bins. More complex decoders substantially outperformed simpler decoders, showing the importance of spike timing information. Particularly effective was the first spike latency representation, which allowed zero discrimination errors for the majority of shape stimuli. Furthermore, the performance of nonlinear decoders showed even greater enhancement compared with linear decoders for these complex representations. Finally, decoders that approximated the correlation structure in the population by matching all pairwise correlations with a maximum entropy model fit to all 162 neurons were quite successful, especially for the spike latency representation. Together, these results suggest a picture in which linear decoders allow a coarse categorization of shape stimuli, whereas nonlinear decoders, which take advantage of both correlation and spike timing, are needed to achieve high-fidelity discrimination.

What can spike train distances tell us about the neural code?

Time scale parametric spike train distances like the Victor and the van Rossum distances are often applied to study the neural code based on neural stimuli discrimination. Different neural coding hypotheses, such as rate or coincidence coding, can be assessed by combining a time scale parametric spike train distance with a classifier in order to obtain the optimal discrimination performance. The time scale for which the responses to different stimuli are distinguished best is assumed to be the discriminative precision of the neural code. The relevance of temporal coding is evaluated by comparing the optimal discrimination performance with the one achieved when assuming a rate code. We here characterize the measures quantifying the discrimination performance, the discriminative precision, and the relevance of temporal coding. Furthermore, we evaluate the information these quantities provide about the neural code. We show that the discriminative precision is too unspecific to be interpreted in terms of the time scales relevant for encoding. Accordingly, the time scale parametric nature of the distances is mainly an advantage because it allows maximizing the discrimination performance across a whole set of measures with different sensitivities determined by the time scale parameter, but not due to the possibility to examine the temporal properties of the neural code.

Temporal patterning of pulses during deep brain stimulation affects central nervous system arousal

Regulation of CNS arousal is important for a wide variety of functions, including the initiation of all motivated behaviors. Usually studied with pharmacological or hormonal tools, CNS arousal can also be elevated by deep brain stimulation (DBS), in the human brain and in animals. The effectiveness of DBS is conventionally held to depend on pulse width, frequency, amplitude and stimulation duration. We demonstrate a novel approach for testing the effectiveness of DBS to increase arousal in intact female mice: all of the foregoing parameters are held constant. Only the temporal patterning of the pulses within the stimulation is varied. To create differentially patterned pulse trains, a deterministic nonlinear dynamic equation was used to generate a series of pulses with a predetermined average frequency. Three temporal patterns of stimulation were defined: two nonlinear patterns, Nonlinear1 (NL1) and Nonlinear2 (NL2), and the conventional pattern, Fixed Frequency (FF). Female mice with bilateral monopolar electrodes were observed before, during and after hippocampal or medial thalamic stimulation. NL1 hippocampal stimulation was significantly more effective at increasing behavioral arousal than either FF or NL2; however, FF and NL2 stimulation of the medial thalamus were more effective than NL1. During the same experiments, we recorded an unpredicted increase in the spectral power of slow waves in the cortical EEG. Our data comprise the first demonstration that the temporal pattern of DBS can be used to elevate its effectiveness, and also point the way toward the use of nonlinear dynamics in the exploration of means to optimize DBS.

Leber congenital amaurosis due to RPE65 mutations and its treatment with gene therapy

Leber congenital amaurosis (LCA) is a rare hereditary retinal degeneration caused by mutations in more than a dozen genes. RPE65, one of these mutated genes, is highly expressed in the retinal pigment epithelium where it encodes the retinoid isomerase enzyme essential for the production of chromophore which forms the visual pigment in rod and cone photoreceptors of the retina. Congenital loss of chromophore production due to RPE65-deficiency together with progressive photoreceptor degeneration cause severe and progressive loss of vision. RPE65-associated LCA recently gained recognition outside of specialty ophthalmic circles due to early success achieved by three clinical trials of gene therapy using recombinant adeno-associated virus (AAV) vectors. The trials were built on multitude of basic, pre-clinical and clinical research defining the pathophysiology of the disease in human subjects and animal models, and demonstrating the proof-of-concept of gene (augmentation) therapy. Substantial gains in visual function of clinical trial participants provided evidence for physiologically relevant biological activity resulting from a newly introduced gene. This article reviews the current knowledge on retinal degeneration and visual dysfunction in animal models and human patients with RPE65 disease, and examines the consequences of gene therapy in terms of improvement of vision reported.

Reliability of cortical activity during natural stimulation

Response reliability is complementary to more conventional measurements of response amplitudes, and can reveal phenomena that response amplitudes do not. Here we review studies that measured reliability of cortical activity within or between human subjects in response to naturalistic stimulation (e.g. free viewing of movies). Despite the seemingly uncontrolled nature of the task, some of these complex stimuli evoke highly reliable, selective and time-locked activity in many brain areas, including some regions that show little response modulation in most conventional experimental protocols. This activity provides an opportunity to address novel questions concerning natural vision, temporal scale of processing, memory and the neural basis of inter-group differences.

Postnatal development of onset transient responses in macaque V1 AND V2 neurons

Vision of newborn infants is limited by immaturities in their visual brain. In adult primates, the transient onset discharges of visual cortical neurons are thought to be intimately involved with capturing the rapid succession of brief images in visual scenes. Here we sought to determine the responsiveness and quality of transient responses in individual neurons of the primary visual cortex (V1) and visual area 2 (V2) of infant monkeys. We show that the transient component of neuronal firing to 640-ms stationary gratings was as robust and as reliable as in adults only 2 wk after birth, whereas the sustained component was more sluggish in infants than in adults. Thus the cortical circuitry supporting onset transient responses is functionally mature near birth, and our findings predict that neonates, known for their "impoverished vision," are capable of initiating relatively mature fixating eye movements and of performing in detection of simple objects far better than traditionally thought.

In vitro and in vivo measures of evoked excitatory and inhibitory conductance dynamics in sensory cortices

In order to better understand the synaptic nature of the integration process operated by cortical neurons during sensory processing, it is necessary to devise quantitative methods which allow one to infer the level of conductance change evoked by the sensory stimulation and, consequently, the dynamics of the balance between excitation and inhibition. Such detailed measurements are required to characterize the static versus dynamic nature of the non-linear interactions triggered at the single cell level by sensory stimulus. This paper primarily reviews experimental data from our laboratory based on direct conductance measurements during whole-cell patch clamp recordings in two experimental preparations: (1) in vitro, during electrical stimulation in the visual cortex of the rat and (2) in vivo, during visual stimulation, in the primary visual cortex of the anaesthetized cat. Both studies demonstrate that shunting inhibition is expressed as well in vivo as in vitro. Our in vivo data reveals that a high level of diversity is observed in the degree of interaction (from linear to non-linear) and in the temporal interplay (from push-pull to synchronous) between stimulus-driven excitation (E) and inhibition (I). A detailed analysis of the E/I balance during evoked spike activity further shows that the firing strength results from a simultaneous decrease of evoked inhibition and increase of excitation. Secondary, the paper overviews the various computational methods used in the literature to assess conductance dynamics, measured in current clamp as well as in voltage clamp in different neocortical areas and species, and discuss the consistency of their estimations.

Contribution of spike timing to the information transmitted by HVC neurons

In many species, neurons with highly selective stimulus-response properties characterize higher order sensory areas and/or sensory motor areas of the CNS. In the songbird nuclei, the responses of HVC (used as a proper name) neurons during playback of the bird's own song (BOS) are probably one of the most striking examples of selectivity for natural stimuli. We examined here to what extent spike-timing carries information about natural and time-reversed versions of the BOS. From a heterogenous population of 107 HVC neurons recorded in long-day or short-day conditions, a standard indicator of stimulus preference based on spike-count (the d' index) indicates that a limited proportion of cells can be classified as selective for the BOS (20% with a |d'| > 1). In contrast, quantifying the information conveyed by spike trains with the metric-space of J.D. Victor & K.P Purpura [(1996) J. Neurophysiol., 76, 1310-1326] indicates that 62% of the cells display significant amounts of transmitted information, among which 77% are 'temporal cells'. 'Temporal cells' correspond to cells transmitting significant amounts of information when spike-timing is considered, whereas no information, or lower amounts of transmitted information, is obtained when only spike-count is considered. Computing a correlation index between spike trains [S. Schreiber et al. (2003) Neurocomputing, 52-54,925-931] revealed that spike-timing reliability is higher for the forward than for the reverse BOS, whatever the day length and the cell type are. Cells classified as selective in terms of spike-counts (d' index) had greater amounts of transmitted information, but cells classified as non-selective (d' < 0.5) can also transmit significant amounts of information. Thus, information theory methods demonstrate that a much larger proportion of neurons than expected based on spike-count only participate in the discrimination between stimuli.

Speed dependence of tuning to one-dimensional features in V1

Using drifting compound grating stimuli matched in energy and frequency spectrum, we previously showed that neurons in the primary visual cortex (V1) were tuned to line-like, edge-like, and intermediate one-dimensional features. Because these compound grating stimuli were drifting, allowing for potential interaction between shape and motion, we examine here the dependence of V1 feature tuning on drift speed. We find that the feature selectivity and specificity of individual V1 neurons strongly depend on speed. A simple model explains these observations in terms of an interaction between linear filtering by the receptive field and the static nonlinearity of spike threshold, embedded in a recurrent network. Although the speed-dependent behaviors in single V1 neurons preclude their acting as extractors of one-dimensional features, the population as a whole retains a representation of a full suite of features.

Response variability of marmoset parvocellular neurons

This study concerns the properties of neurons carrying signals for colour vision in primates. We investigated the variability of responses of individual parvocellular lateral geniculate neurons of dichromatic and trichromatic marmosets to drifting sinusoidal luminance and chromatic gratings. Response variability was quantified by the cycle-to-cycle variation in Fourier components of the response. Averaged across the population, the variability at low contrasts was greater than predicted by a Poisson process, and at high contrasts the responses were approximately 40% more variable than responses at low contrasts. The contrast-dependent increase in variability was nevertheless below that expected from the increase in firing rate. Variability falls below the Poisson prediction at high contrast, and intrinsic variability of the spike train decreases as contrast increases. Thus, while deeply modulated responses in parvocellular cells have a larger absolute variability than weakly modulated ones, they have a more favourable signal: noise ratio than predicted by a Poisson process. Similar results were obtained from a small sample of magnocellular and koniocellular ('blue-on') neurons. For parvocellular neurons with pronounced colour opponency, chromatic responses were, on average, less variable (10-15%, p<0.01) than luminance responses of equal magnitude. Conversely, non-opponent parvocellular neurons showed the opposite tendency. This is consistent with a supra-additive noise source prior to combination of cone signals. In summary, though variability of parvocellular neurons is largely independent of the way in which they combine cone signals, the noise characteristics of retinal circuitry may augment specialization of parvocellular neurons to signal luminance or chromatic contrast.

Spike train metrics

Quantifying similarity and dissimilarity of spike trains is an important requisite for understanding neural codes. Spike metrics constitute a class of approaches to this problem. In contrast to most signal-processing methods, spike metrics operate on time series of all-or-none events, and are, thus, particularly appropriate for extracellularly recorded neural signals. The spike metric approach can be extended to multineuronal recordings, mitigating the 'curse of dimensionality' typically associated with analyses of multivariate data. Spike metrics have been usefully applied to the analysis of neural coding in a variety of systems, including vision, audition, olfaction, taste and electric sense.

Statistical Issues in the Analysis of Neuronal Data

Analysis of data from neurophysiological investigations can be challenging. Particularly when experiments involve dynamics of neuronal response, scientific inference can become subtle and some statistical methods may make much more efficient use of the data than others. This article reviews well-established statistical principles, which provide useful guidance, and argues that good statistical practice can substantially enhance results. Recent work on estimation of firing rate, population coding, and time-varying correlation provides improvements in experimental sensitivity equivalent to large increases in the number of neurons examined. Modern nonparametric methods are applicable to data from repeated trials. Many within-trial analyses based on a Poisson assumption can be extended to non-Poisson data. New methods have made it possible to track changes in receptive fields, and to study trial-to-trial variation, with modest amounts of data.

The neural basis of temporal processing

A complete understanding of sensory and motor processing requires characterization of how the nervous system processes time in the range of tens to hundreds of milliseconds (ms). Temporal processing on this scale is required for simple sensory problems, such as interval, duration, and motion discrimination, as well as complex forms of sensory processing, such as speech recognition. Timing is also required for a wide range of motor tasks from eyelid conditioning to playing the piano. Here we review the behavioral, electrophysiological, and theoretical literature on the neural basis of temporal processing. These data suggest that temporal processing is likely to be distributed among different structures, rather than relying on a centralized timing area, as has been suggested in internal clock models. We also discuss whether temporal processing relies on specialized neural mechanisms, which perform temporal computations independent of spatial ones. We suggest that, given the intricate link between temporal and spatial information in most sensory and motor tasks, timing and spatial processing are intrinsic properties of neural function, and specialized timing mechanisms such as delay lines, oscillators, or a spectrum of different time constants are not required. Rather temporal processing may rely on state-dependent changes in network dynamics.

Temporal Codes and Computations for Sensory Representation and Scene Analysis

This paper considers a space of possible temporal codes, surveys neurophysiological and psychological evidence for their use in nervous systems, and presents examples of neural timing networks that operate in the time-domain. Sensory qualities can be encoded temporally by means of two broad strategies: stimulus-driven temporal correlations (phase-locking) and stimulus-triggering of endogenous temporal response patterns. Evidence for stimulus-related spike timing patterns exists in nearly every sensory modality, and such information can be potentially utilized for representation of stimulus qualities, localization of sources, and perceptual grouping. Multiple strategies for temporal (time, frequency, and code-division) multiplexing of information for transmission and grouping are outlined. Using delays and multiplications (coincidences), neural timing networks perform time-domain signal processing operations to compare, extract and separate temporal patterns. Separation of synthetic double vowels by a recurrent neural timing network is used to illustrate how coherences in temporal fine structure can be exploited to build up and separate periodic signals with different fundamentals. Timing nets constitute a time-domain scene analysis strategy based on temporal pattern invariance rather than feature-based labeling, segregation and binding of channels. Further potential implications of temporal codes and computations for new kinds of neural networks are explored.

Visual cortex neurons of monkeys and cats: temporal dynamics of the spatial frequency response function

We measured the responses of striate cortex neurons as a function of spatial frequency on a fine time scale, over the course of an interval that is comparable to the duration of a single fixation (200 ms). Stationary gratings were flashed on for 200 ms and then off for 300 ms; the responses were analyzed at sequential 1-ms intervals. We found that 1) the preferred spatial frequency shifts through time from low frequencies to high frequencies, 2) the latency of the response increases as a function of spatial frequency, and 3) the poststimulus time histograms (PSTHs) are relatively shape-invariant across spatial frequency. The dynamic shifts in preferred spatial frequency appear to be a simple consequence of the latency shifts and the transient nature of the PSTH. The effects of these dynamic shifts on the coding of spatial frequency information are examined within the context of several different temporal integration strategies, and pattern-detection performance is determined as a function of the interval of integration, following response onset. The findings are considered within the context of related investigations as well as a number of functional issues: motion selectivity in depth, "coarse-to-fine" processing, direction selectivity, latency as a code for stimulus attributes, and behavioral response latency. Finally, we demonstrate that the results are qualitatively consistent with a simple feedforward model, similar to the one originally proposed in 1962 by Hubel and Wiesel, that incorporates measured differences in the response latencies and the receptive field sizes of different lateral geniculate nucleus inputs.

Synaptic transmission of chaotic spike trains between primary afferent fiber and spinal dorsal horn neuron in the rat

Primary sensory neurons can generate irregular burst firings in which the existence of significant deterministic behaviors of chaotic dynamics has been proved with nonlinear time series analysis. But how well the deterministic characteristics and neural information of presynaptic chaotic spike trains were transmitted into postsynaptic spike trains is still an open question. Here we investigated the synaptic transmission of chaotic spike trains between primary Adelta afferent fiber and spinal dorsal horn neuron. Two kinds of basic stimulus unit, brief burst and single pulse, were employed by us to comprise chaotic stimulus trains. For time series analysis, we defined "events" as the longest sequences of spikes with all interspike intervals less than or equal to a certain threshold and extracted the interevent intervals (IEIs) from spike trains. Return map analysis of the IEI series showed that the main temporal structure of chaotic input trains could be detected in postsynaptic output trains, especially under brief-burst stimulation. Using correlation dimension and nonlinear prediction methods, we found that synaptic transmission could influence the nonlinear characteristics of chaotic trains, such as fractal dimension and short-term predictability, with greater influence made under single-pulse stimulation. By calculating the mutual information between input and output trains, we found the information carried by presynaptic spike trains could not be completely transmitted at primary afferent synapses, and that brief bursts could more reliably transmit the information carried by chaotic input trains across synapses. These results indicate that although unreliability exists during synaptic transmission, the main deterministic characteristics of chaotic burst trains can be transmitted across primary afferent synapses. Moreover, brief bursts that come from the periphery can more reliably transmit neural information between primary afferent fibers and spinal dorsal horn neurons.

From another angle: Differences in cortical coding between fine and coarse discrimination of orientation

We measured the information available for orientation discrimination from metric distances for 24 cells in area 17 of cats that were paralyzed and anesthetized with Propofol and N(2)O. The metric distance information confirms fundamental coding differences for discrimination between fine (<10 degrees ) and coarse (>10 degrees ) orientation differences. The information for discriminating larger orientation differences is contained mainly in the firing rate, with minor enhancements from the coarse (30-70 ms) temporal structure in the firing rate. Both precise spike timing (9.2 ms) and intervals (6.8 ms) sustained over the stimulus presentation provide information for fine discrimination of orientation, where almost no reliable information is provided by the spike count. We compare and confirm the results (using the same data set) to vector distances based on classification theory. The results support a dynamic spiking mechanism where coordinated activity could provide fast and reliable information about detailed angle and/or direction information in the region of the preferred orientation.

Submillisecond precision of the input-output transformation function mediated by fast sodium dendritic spikes in basal dendrites of CA1 pyramidal neurons

The ability of cortical neurons to perform temporally accurate computations has been shown to be important for encoding of information in the cortex; however, cortical neurons are expected to be imprecise temporal encoders because of the stochastic nature of synaptic transmission and ion channel gating, dendritic filtering, and background synaptic noise. Here we show for the first time that fast local spikes in basal dendrites can serve to improve the temporal precision of neuronal output. Integration of coactivated, spatially distributed synaptic inputs produces temporally imprecise output action potentials within a time window of several milliseconds. In contrast, integration of closely spaced basal inputs initiates local dendritic spikes that amplify and sharpen the summed somatic potential. In turn, these fast basal spikes allow precise timing of output action potentials with submillisecond temporal jitter over a wide range of activation intensities and background synaptic noise. Our findings indicate that fast spikes initiated in individual basal dendrites can serve as precise "timers" of output action potentials in various network activity states and thus may contribute to temporal coding in the cortex.

Neural coding of spatial phase in V1 of the macaque monkey

We examine the responses of single neurons and pairs of neurons, simultaneously recorded with a single tetrode in the primary visual cortex of the anesthetized macaque monkey, to transient presentations of stationary gratings of varying spatial phase. Such simultaneously recorded neurons tended to have similar tuning to the phase of the grating. To determine the response features that reliably discriminate these stimuli, we use the metric-space approach extended to pairs of neurons. We find that paying attention to the times of individual spikes, at a resolution of approximately 30 ms, and keeping track of which neuron fires which spike rather than just the summed local activity contribute substantially to phase coding. The contribution is both quantitative (increasing the fidelity of phase coding) and qualitative (enabling a 2-dimensional "response space" that corresponds to the spatial phase cycle). We use a novel approach, the extraction of "temporal profiles" from the metric space analysis, to interpret and compare temporal coding across neurons. Temporal profiles were remarkably consistent across a large subset of neurons. This consistency indicates that simple mechanisms (e.g., comparing the size of the transient and sustained components of the response) allow the temporal contribution to phase coding to be decoded.

Orientation and direction selectivity of synaptic inputs in visual cortical neurons: a diversity of combinations produces spike tuning

This intracellular study investigates synaptic mechanisms of orientation and direction selectivity in cat area 17. Visually evoked inhibition was analyzed in 88 cells by detecting spike suppression, hyperpolarization, and reduction of trial-to-trial variability of membrane potential. In 25 of these cells, inhibition visibility was enhanced by depolarization and spike inactivation and by direct measurement of synaptic conductances. We conclude that excitatory and inhibitory inputs share the tuning preference of spiking output in 60% of cases, whereas inhibition is tuned to a different orientation in 40% of cases. For this latter type of cells, conductance measurements showed that excitation shared either the preference of the spiking output or that of the inhibition. This diversity of input combinations may reflect inhomogeneities in functional intracortical connectivity regulated by correlation-based activity-dependent processes.

Spatial organization of receptive fields of V1 neurons of alert monkeys: comparison with responses to gratings

We studied the spatial organization of receptive fields and the responses to gratings of neurons in parafoveal V1 of alert monkeys. Activating regions (ARs) of 228 cells were mapped with increment and decrement bars while compensating for fixational eye movements. For cells with two or more ARs, the overlap between ARs responsive to increments (INC) and ARs responsive to decrements (DEC) was characterized by a quantitative overlap index (OI). The distribution of overlap indices was bimodal. The larger group (78% of cells) was composed of complex cells with strongly overlapping ARs (OI >/= 0.5). The smaller group (14%) was composed of simple cells with minimal spatial overlap of ARs (OI </= 0.3). Simple cells were preferentially located in layers dominated by the magnocellular pathway. A third group of neurons, the monocontrast cells (8%), responded only to one sign of contrast and had more sustained responses to flashed stimuli than other cells. One hundred fourteen neurons were also studied with drifting sinusoidal gratings of various spatial frequencies and window widths. For complex cells, the relative modulation (RM, the ratio of the 1st harmonic to the mean firing rate), ranged from 0.6 +/- 0.4 to 1.1 +/- 0.5 (mean +/- SD), depending on the stimulus conditions and the mode of correction for eye movements. RM was not correlated with the degree of overlap of ARs, indicating that the spatial organization of receptive fields cannot reliably be predicted from RM values. In fact, a subset of complex cells had RM > 1, the traditional criterion for identifying simple cells. However, unlike simple cells, even those complex cells with high RM could exhibit diverse nonlinear responses when the spatial frequency or window size was changed. Furthermore, the responses of complex cells to counterphase gratings were predominantly nonlinear even harmonics. These results show that RM is not a robust test of linearity. Our results indicate that complex cells are the most frequently encountered neurons in primate V1, and their behavior needs to receive more emphasis in models of visual function.

A unified approach to the study of temporal, correlational, and rate coding

We demonstrate that the information contained in the spike occurrence times of a population of neurons can be broken up into a series of terms, each reflecting something about potential coding mechanisms. This is possible in the coding regime in which few spikes are emitted in the relevant time window. This approach allows us to study the additional information contributed by spike timing beyond that present in the spike counts and to examine the contributions to the whole information of different statistical properties of spike trains, such as firing rates and correlation functions. It thus forms the basis for a new quantitative procedure for analyzing simultaneous multiple neuron recordings and provides theoretical constraints on neural coding strategies. We find a transition between two coding regimes, depending on the size of the relevant observation timescale. For time windows shorter than the timescale of the stimulus-induced response fluctuations, there exists a spike count coding phase, in which the purely temporal information is of third order in time. For time windows much longer than the characteristic timescale, there can be additional timing information of first order, leading to a temporal coding phase in which timing information may affect the instantaneous information rate. In this new framework, we study the relative contributions of the dynamic firing rate and correlation variables to the full temporal information, the interaction of signal and noise correlations in temporal coding, synergy between spikes and between cells, and the effect of refractoriness. We illustrate the utility of the technique by analyzing a few cells from the rat barrel cortex.

Temporal coding of contrast in primary visual cortex: when, what, and why

How do neurons in the primary visual cortex (V1) encode the contrast of a visual stimulus? In this paper, the information that V1 responses convey about the contrast of static visual stimuli is explicitly calculated. These responses often contain several easily distinguished temporal components, which will be called latency, transient, tonic, and off. Calculating the information about contrast conveyed in each component and in groups of components makes it possible to delineate aspects of the temporal structure that may be relevant for contrast encoding. The results indicate that as much or more contrast-related information is encoded into the temporal structure of spike train responses as into the firing rate and that the temporally coded information is manifested most strongly in the latency to response onset. Transient, tonic, and off responses contribute relatively little. The results also reveal that temporal coding is important for distinguishing subtle contrast differences, whereas firing rates are useful for gross discrimination. This suggests that the temporal structure of neurons' responses may extend the dynamic range for contrast encoding in the primate visual system.

Formal and attribute-specific information in primary visual cortex

We estimate the rates at which neurons in the primary visual cortex (V1) of anesthetized macaque monkeys transmit stimulus-related information in response to three types of visual stimulus. The stimuli-randomly modulated checkerboard patterns, stationary sinusoidal gratings, and drifting sinusoidal gratings-have very different spatiotemporal structures. We obtain the overall rate of information transmission, which we call formal information, by a direct method. We find the highest information rates in the responses of simple cells to drifting gratings (median: 10.3 bits/s, 0.92 bits/spike); responses to randomly modulated stimuli and stationary gratings transmit information at significantly lower rates. In general, simple cells transmit information at higher rates, and over a larger range, than do complex cells. Thus in the responses of V1 neurons, stimuli that are rapidly modulated do not necessarily evoke higher information rates, as might be the case with motion-sensitive neurons in area MT. By an extension of the direct method, we parse the formal information into attribute-specific components, which provide estimates of the information transmitted about contrast and spatiotemporal pattern. We find that contrast-specific information rates vary across neurons-about 0.3 to 2.1 bits/s or 0.05 to 0.22 bits/spike-but depend little on stimulus type. Spatiotemporal pattern-specific information rates, however, depend strongly on the type of stimulus and neuron (simple or complex). The remaining information rate, typically between 10 and 32% of the formal information rate for each neuron, cannot be unambiguously assigned to either contrast or spatiotemporal pattern. This indicates that some information concerning these two stimulus attributes is confounded in the responses of single neurons in V1. A model that considers a simple cell to consist of a linear spatiotemporal filter followed by a static rectifier predicts higher information rates than are found in real neurons and completely fails to replicate the performance of real cells in generating the confounded information.

How the brain uses time to represent and process visual information(1)

Information theory provides a theoretical framework for addressing fundamental questions concerning the nature of neural codes. Harnessing its power is not straightforward, because of the differences between mathematical abstractions and laboratory reality. We describe an approach to the analysis of neural codes that seeks to identify the informative features of neural responses, rather than to estimate the information content of neural responses per se. Our analysis, applied to neurons in primary visual cortex (V1), demonstrates that the informative precision of spike times varies with the stimulus modality being represented. Contrast is represented by spike times on the shortest time scale, and different kinds of pattern information are represented on longer time scales. The interspike interval distribution has a structure that is unanticipated from the firing rate. The significance of this structure is not that it contains additional information, but rather, that it may provide a means for simple synaptic mechanisms to decode the information that is multiplexed within a spike train. Extensions of this analysis to the simultaneous responses of pairs of neurons indicate that neighboring neurons convey largely independent information, if the decoding process is sensitive to the neuron of origin and not just the average firing rate. In summary, stimulus-related information is encoded into the precise times of spikes fired by V1 neurons. Much of this information would be obscured if individual spikes were merely taken to be estimators of the firing rate. Additional information would be lost by averaging across the responses of neurons in a local population. We propose that synaptic mechanisms sensitive to interspike intervals and dendritic processing beyond simple summation exist at least in part to enable the brain to take advantage of this extra information.

Neural representation and the cortical code

The principle function of the central nervous system is to represent and transform information and thereby mediate appropriate decisions and behaviors. The cerebral cortex is one of the primary seats of the internal representations maintained and used in perception, memory, decision making, motor control, and subjective experience, but the basic coding scheme by which this information is carried and transformed by neurons is not yet fully understood. This article defines and reviews how information is represented in the firing rates and temporal patterns of populations of cortical neurons, with a particular emphasis on how this information mediates behavior and experience.

Robustness and variability of neuronal coding by amplitude-sensitive afferents in the weakly electric fish eigenmannia

We investigated the variability of P-receptor afferent spike trains in the weakly electric fish, Eigenmannia, to repeated presentations of random electric field AMs (RAMs) and quantified its impact on the encoding of time-varying stimuli. A new measure of spike timing jitter was developed using the notion of spike train distances recently introduced by Victor and Purpura. This measure of variability is widely applicable to neuronal responses, irrespective of the type of stimuli used (deterministic vs. random) or the reliability of the recorded spike trains. In our data, the mean spike count and its variance measured in short time windows were poorly correlated with the reliability of P-receptor afferent spike trains, implying that such measures provide unreliable indices of trial-to-trial variability. P-receptor afferent spike trains were considerably less variable than those of Poisson model neurons. The average timing jitter of spikes lay within 1-2 cycles of the electric organ discharge (EOD). At low, but not at high firing rates, the timing jitter was dependent on the cutoff frequency of the stimulus and, to a lesser extent, on its contrast. When spikes were artificially manipulated to increase jitter, information conveyed by P-receptor afferents was degraded only for average jitters considerably larger than those observed experimentally. This suggests that the intrinsic variability of single spike trains lies outside of the range where it might degrade the information conveyed, yet still allows for improvement in coding by averaging across multiple afferent fibers. Our results were summarized in a phenomenological model of P-receptor afferents, incorporating both their linear transfer properties and the variability of their spike trains. This model complements an earlier one proposed by Nelson et al. for P-receptor afferents of Apteronotus. Because of their relatively high precision with respect to the EOD cycle frequency, P-receptor afferent spike trains possess the temporal resolution necessary to support coincidence detection operations at the next stage in the amplitude-coding pathway.