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.     

Functional organization of local neuronal networks in the cat neocortex. Dependence on the food motivation

The multiple unit activity (MUA) from clusters of adjacent neurones in deep layers of the frontal and motor cortex was recorded in alert cats with different levels of alimentary motivation. Up to 7 spike trains were selected from the MUA. Neurones in the local circuits could be divided into 2 groups: large neurones with prevailing divergent characteristics, and small neurones with prevailing convergent characteristics. A 24-hour food deprivation altered the cross-correlation interneuronal connections with a time delay within the range of 2 to 100 ms.     

Spatiotemporal Spike Coding of Behavioral Adaptation in the Dorsal Anterior Cingulate Cortex

The frontal cortex controls behavioral adaptation in environments governed by complex rules. Many studies have established the relevance of firing rate modulation after informative events signaling whether and how to update the behavioral policy. However, whether the spatiotemporal features of these neuronal activities contribute to encoding imminent behavioral updates remains unclear. We investigated this issue in the dorsal anterior cingulate cortex (dACC) of monkeys while they adapted their behavior based on their memory of feedback from past choices. We analyzed spike trains of both single units and pairs of simultaneously recorded neurons using an algorithm that emulates different biologically plausible decoding circuits. This method permits the assessment of the performance of both spike-count and spike-timing sensitive decoders. In response to the feedback, single neurons emitted stereotypical spike trains whose temporal structure identified informative events with higher accuracy than mere spike count. The optimal decoding time scale was in the range of 70–200 ms, which is significantly shorter than the memory time scale required by the behavioral task. Importantly, the temporal spiking patterns of single units were predictive of the monkeys’ behavioral response time. Furthermore, some features of these spiking patterns often varied between jointly recorded neurons. All together, our results suggest that dACC drives behavioral adaptation through complex spatiotemporal spike coding. They also indicate that downstream networks, which decode dACC feedback signals, are unlikely to act as mere neural integrators.     

Gap Junctions Are Essential for Generating the Correlated Spike Activity of Neighboring Retinal Ganglion Cells

Neurons throughout the brain show spike activity that is temporally correlated to that expressed by their neighbors, yet the generating mechanism(s) remains unclear. In the retina, ganglion cells (GCs) show robust, concerted spiking that shapes the information transmitted to central targets. Here we report the synaptic circuits responsible for generating the different types of concerted spiking of GC neighbors in the mouse retina. The most precise concerted spiking was generated by reciprocal electrical coupling of GC neighbors via gap junctions, whereas indirect electrical coupling to a common cohort of amacrine cells generated the correlated activity with medium precision. In contrast, the correlated spiking with the lowest temporal precision was produced by shared synaptic inputs carrying photoreceptor noise. Overall, our results demonstrate that different synaptic circuits generate the discrete types of GC correlated activity. Moreover, our findings expand our understanding of the roles of gap junctions in the retina, showing that they are essential for generating all forms of concerted GC activity transmitted to central brain targets.     

Millisecond-timescale local network coding in the rat primary somatosensory cortex

Correlation among neocortical neurons is thought to play an indispensable role in mediating sensory processing of external stimuli. The role of temporal precision in this correlation has been hypothesized to enhance information flow along sensory pathways. Its role in mediating the integration of information at the output of these pathways, however, remains poorly understood. Here, we examined spike timing correlation between simultaneously recorded layer V neurons within and across columns of the primary somatosensory cortex of anesthetized rats during unilateral whisker stimulation. We used bayesian statistics and information theory to quantify the causal influence between the recorded cells with millisecond precision. For each stimulated whisker, we inferred stable, whisker-specific, dynamic bayesian networks over many repeated trials, with network similarity of 83.3±6% within whisker, compared to only 50.3±18% across whiskers. These networks further provided information about whisker identity that was approximately 6 times higher than what was provided by the latency to first spike and 13 times higher than what was provided by the spike count of individual neurons examined separately. Furthermore, prediction of individual neurons' precise firing conditioned on knowledge of putative pre-synaptic cell firing was 3 times higher than predictions conditioned on stimulus onset alone. Taken together, these results suggest the presence of a temporally precise network coding mechanism that integrates information across neighboring columns within layer V about vibrissa position and whisking kinetics to mediate whisker movement by motor areas innervated by layer V.     

Patterns of spatio-temporal correlations in the neural activity of the cat motor cortex during trained forelimb movements

In order to study how neurons in the primary motor cortex (MI) are dynamically linked together during skilled movement, we recorded simultaneously from many cortical neurons in cats trained to perform a reaching and retrieval task using their forelimbs. Analysis of task-related spike activity in the MI of the hemisphere contralateral to the reaching forelimb (in identified forelimb or hindlimb representations) recorded through chronically implanted microwires, was followed by pairwise evaluation of temporally correlated activity in these neurons during task performance using shuffle corrected cross-correlograms. Over many months of recording, a variety of task-related modulations of neural activities were observed in individual efferent zones. Positively correlated activity (mainly narrow peaks at zero or short latencies) was seen during task performance frequently between neurons recorded within the forelimb representation of MI, rarely within the hindlimb area of MI, and never between forelimb and hindlimb areas. Correlated activity was frequently observed between neurons with different patterns of task-related activity or preferential activity during different task elements (reaching, feeding, etc.), and located in efferent zones with dissimilar representation as defined by intracortical microstimulation. The observed synchronization of action potentials among selected but functionally varied groups of MI neurons possibly reflects dynamic recruitment of network connections between efferent zones during skilled movement.     

State-space analysis of time-varying higher-order spike correlation for multiple neural spike train data

Precise spike coordination between the spiking activities of multiple neurons is suggested as an indication of coordinated network activity in active cell assemblies. Spike correlation analysis aims to identify such cooperative network activity by detecting excess spike synchrony in simultaneously recorded multiple neural spike sequences. Cooperative activity is expected to organize dynamically during behavior and cognition; therefore currently available analysis techniques must be extended to enable the estimation of multiple time-varying spike interactions between neurons simultaneously. In particular, new methods must take advantage of the simultaneous observations of multiple neurons by addressing their higher-order dependencies, which cannot be revealed by pairwise analyses alone. In this paper, we develop a method for estimating time-varying spike interactions by means of a state-space analysis. Discretized parallel spike sequences are modeled as multi-variate binary processes using a log-linear model that provides a well-defined measure of higher-order spike correlation in an information geometry framework. We construct a recursive Bayesian filter/smoother for the extraction of spike interaction parameters. This method can simultaneously estimate the dynamic pairwise spike interactions of multiple single neurons, thereby extending the Ising/spin-glass model analysis of multiple neural spike train data to a nonstationary analysis. Furthermore, the method can estimate dynamic higher-order spike interactions. To validate the inclusion of the higher-order terms in the model, we construct an approximation method to assess the goodness-of-fit to spike data. In addition, we formulate a test method for the presence of higher-order spike correlation even in nonstationary spike data, e.g., data from awake behaving animals. The utility of the proposed methods is tested using simulated spike data with known underlying correlation dynamics. Finally, we apply the methods to neural spike data simultaneously recorded from the motor cortex of an awake monkey and demonstrate that the higher-order spike correlation organizes dynamically in relation to a behavioral demand.     

Task-Dependent Changes in Cross-Level Coupling between Single Neurons and Oscillatory Activity in Multiscale Networks

Understanding the principles governing the dynamic coordination of functional brain networks remains an important unmet goal within neuroscience. How do distributed ensembles of neurons transiently coordinate their activity across a variety of spatial and temporal scales? While a complete mechanistic account of this process remains elusive, evidence suggests that neuronal oscillations may play a key role in this process, with different rhythms influencing both local computation and long-range communication. To investigate this question, we recorded multiple single unit and local field potential (LFP) activity from microelectrode arrays implanted bilaterally in macaque motor areas. Monkeys performed a delayed center-out reach task either manually using their natural arm (Manual Control, MC) or under direct neural control through a brain-machine interface (Brain Control, BC). In accord with prior work, we found that the spiking activity of individual neurons is coupled to multiple aspects of the ongoing motor beta rhythm (10–45 Hz) during both MC and BC, with neurons exhibiting a diversity of coupling preferences. However, here we show that for identified single neurons, this beta-to-rate mapping can change in a reversible and task-dependent way. For example, as beta power increases, a given neuron may increase spiking during MC but decrease spiking during BC, or exhibit a reversible shift in the preferred phase of firing. The within-task stability of coupling, combined with the reversible cross-task changes in coupling, suggest that task-dependent changes in the beta-to-rate mapping play a role in the transient functional reorganization of neural ensembles. We characterize the range of task-dependent changes in the mapping from beta amplitude, phase, and inter-hemispheric phase differences to the spike rates of an ensemble of simultaneously-recorded neurons, and discuss the potential implications that dynamic remapping from oscillatory activity to spike rate and timing may hold for models of computation and communication in distributed functional brain networks.     

Auditory information coding by modeled cochlear nucleus neurons

In this paper we use information theory to quantify the information in the output spike trains of modeled cochlear nucleus globular bushy cells (GBCs). GBCs are part of the sound localization pathway. They are known for their precise temporal processing, and they code amplitude modulations with high fidelity. Here we investigated the information transmission for a natural sound, a recorded vowel. We conclude that the maximum information transmission rate for a single neuron was close to 1,050 bits/s, which corresponds to a value of approximately 5.8 bits per spike. For quasi-periodic signals like voiced speech, the transmitted information saturated as word duration increased. In general, approximately 80% of the available information from the spike trains was transmitted within about 20 ms. Transmitted information for speech signals concentrated around formant frequency regions. The efficiency of neural coding was above 60% up to the highest temporal resolution we investigated (20 μs). The increase in transmitted information to that precision indicates that these neurons are able to code information with extremely high fidelity, which is required for sound localization. On the other hand, only 20% of the information was captured when the temporal resolution was reduced to 4 ms. As the temporal resolution of most speech recognition systems is limited to less than 10 ms, this massive information loss might be one of the reasons which are responsible for the lack of noise robustness of these systems.     

Recurring discharge patterns in multiple spike trains

Simultaneously recorded spike trains were obtained using microwire bundles from unrestrained, drug-free cats during different sleep-waking states in forebrain areas associated with cardiac and respiratory activity. Cardiac and respiratory activity was simultaneously recorded with the spike trains. We applied the recurring discharge patterns detection procedure described in a companion paper (Frostig et al. 1990) to the spike and cardiorespiratory trains. The pattern detection procedure was applied to detect only precise (in time and structure) recurring patterns. Recurring discharge patterns were detected in all simultaneously recorded groups. Recurring discharge patterns were composed of up to ten spikes per pattern and involved up to four simultaneously recorded spike trains. Fourty-two percent of the recurring patterns contained cardiac and/or respiratory events in addition to neuronal spikes. When patterns were compared over different sleep-waking states it was found the the same units produced different patterns in different states, that patterns were significantly more compact in time during quiet sleep, and that changes in the discharge rates accompanying changes in sleep-waking states were not correlated with changes in pattern rate.     

Temporal organization of interneuronal relations in the cerebral cortex of the cat in relation to the level of alimentary motivation

By means of records of multicellular activity, interneuronal relations and their modifications in two cortical zones (Visual and motor) were studied in cats at different levels of alimentary motivation. For quantitative evaluation of interneuronal relations the statistic method of cross-correlation analysis of impulse trains was used in determining the probability of the appearance of the discharge of one neurone after the impulse of the other one. For groups of neurones in both investigated cortical areas, three-neurones microsystems were singled out and their activity was analyzed by temporal parameters of interaction between neurones at the interval of 120 ms, both within one microarea (intraanalyzer connections) and between microareas of two cortical zones. The correlation of temporal parameters of interneuronal connections (temporal delays in the activity of neuronal pairs) changed depending on spatial localization of neurones and functional condition of the animals. The existence is suggested of "informational" (1-30 ms) and "motivational" (90-120 ms) values of interneuronal relations for interanalyser connections.     

Temporal precision and temporal drift in brain and behavior of zebra finch song.

In the zebra finch forebrain nucleus robustus archistriatalis (RA), neurons burst during singing. We showed that the internal structure of spike bursts was regulated with a precision of circa 0.2 ms, and yielded alignment of acoustic features of song with a precision of circa 1 ms. In addition, interburst intervals and corresponding syllable durations displayed systematic variation within song (average elongation 0.3 ms/s song), and slower "drift" across songs. Systematic variation on even a coarser time scale might be difficult to detect in other systems, but could affect the analysis of temporal patterning. The close relationship between precise timing of individual spikes and stereotypic behavior suggests that song is represented in RA by a temporal code.     

Measuring spike timing distance in the Hindmarsh–Rose neurons

In the present paper, a simple spike timing distance is defined which can be used to measure the degree of synchronization with the information only encoded in the precise timing of the spike trains. Via calculating the spike timing distance defined in this paper, the spike train similarity of uncoupled Hindmarsh–Rose neurons in bursting or spiking states with different initial conditions is investigated and the results are compared with other spike train distance measures. Later, the spike timing distance measure is applied to study the synchronization of coupled or common noise-stimulated neurons. Counterintuitively, the addition of weak coupling or common noise doesn’t enhance the degree of synchronization although after critical values, both of them can induce complete synchronizations. More interestingly, the common noise plays opposite roles for weak and strong enough couplings. Finally, it should be noted that the measure defined in this paper can be extended to measure large neuronal ensembles and the lag synchronization.     

Selectivity of stimulus induced responses in cultured hippocampal networks on microelectrode arrays

Sensory information can be encoded using the average firing rate and spike occurrence times in neuronal network responses to external stimuli. Decoding or retrieving stimulus characteristics from the response pattern generally implies that the corresponding neural network has a selective response to various input signals. The role of various spiking activity characteristics (e.g., spike rate and precise spike timing) for basic information processing was widely investigated on the level of neural populations but gave inconsistent evidence for particular mechanisms. Multisite electrophysiology of cultured neural networks grown on microelectrode arrays is a recently developed tool and currently an active research area. In this study, we analyzed the stimulus responses represented by network-wide bursts evoked from various spatial locations (electrodes). We found that the response characteristics, such as the burst initiation time and the spike rate, can be used to retrieve information about the stimulus location. The best selectivity in the response spiking pattern could be found for a small subpopulation of neurones (electrodes) at relatively short post-stimulus intervals. Such intervals were unique for each culture due to the non-uniform organization of the functional connectivity in the network during spontaneous development.     

No parallel fiber volleys in the cerebellar cortex: evidence from cross-correlation analysis between Purkinje cells in a computer model and in recordings from anesthetized rats

Purkinje cells aligned on the medio-lateral axis share a large proportion of their 175,000 parallel fiber inputs. This arrangement has led to the hypothesis that movement timing is coded in the cerebellum by beams of synchronously active parallel fibers. In computer simulations I show that such synchronous activation leads to a narrow spike cross-correlation between pairs of Purkinje cells. This peak was completely absent when shared parallel fiber input was active in an asynchronous mode. To determine the presence of synchronous parallel fiber beams {in vivo} I recorded from pairs of Purkinje cells in crus IIa of anesthetized rats. I found a complete absence of precise spike synchronization, even when both cells were strongly modulated in their spike rate by trains of air-puff stimuli to the face. These results indicate that Purkinje cell spiking is not controlled by volleys of synchronous parallel fiber inputs in the conditions examined. Instead, the data support a model by which granule cells primarily control Purkinje cell spiking via dynamic population rate changes.     

Spike timing and reliability in cortical pyramidal neurons: effects of EPSC kinetics, input synchronization and background noise on spike timing

In vivo studies have shown that neurons in the neocortex can generate action potentials at high temporal precision. The mechanisms controlling timing and reliability of action potential generation in neocortical neurons, however, are still poorly understood. Here we investigated the temporal precision and reliability of spike firing in cortical layer V pyramidal cells at near-threshold membrane potentials. Timing and reliability of spike responses were a function of EPSC kinetics, temporal jitter of population excitatory inputs, and of background synaptic noise. We used somatic current injection to mimic population synaptic input events and measured spike probability and spike time precision (STP), the latter defined as the time window (Deltat) holding 80% of response spikes. EPSC rise and decay times were varied over the known physiological spectrum. At spike threshold level, EPSC decay time had a stronger influence on STP than rise time. Generally, STP was highest (6 ms) triggered spikes at lower temporal precision (>or=6.58 ms). We found an overall linear relationship between STP and spike delay. The difference in STP between fast and slow compound EPSCs could be reduced by incrementing the amplitude of slow compound EPSCs. The introduction of a temporal jitter to compound EPSCs had a comparatively small effect on STP, with a tenfold increase in jitter resulting in only a five fold decrease in STP. In the presence of simulated synaptic background activity, precisely timed spikes could still be induced by fast EPSCs, but not by slow EPSCs.     

Optimal signal filtering parameters for tetrode recording of neuronal activity

Extracellular recording of neuronal spiking is the main method of investigation of involvement of neurons in behavioral tasks. Development of multichannel electrodes made it possible to simultaneously record activity of the same group of neurons from different locations in the brain tissue. That method allows the researches to distinguish spiking of simultaneously recorded neurons by individual set of projection coefficients of amplitude parameters on axes corresponding to different channels of the multichannel electrode. We tested the possibility of effective separation of single unit spiking streams from multiunit activity recorded by tetrode and subjected to different filtering. We described the main limitations for effective spike identification and determined the optimal band of signal filtering for tetrode recording.     

A comparison of the spike train activity of the cortical neurons and of the spatial synchronization of the EEG

In chronic experiments EEG coherence and conjugation of impulse activity were compared of neurones of the visual and sensorimotor areas of rabbits neocortex simultaneously recorded with the same electrodes. Connection was revealed between the presence and properties of conjugated neurones activity and EEG coherence at various frequencies. At correlated neurones activity a greater EEG coherence was observed on frequencies of 3-4,5 Hz than at the independent activity. At the highest level of the EEG coherence the neurones discharged with less delay of one after the other in pairs, and in their synchronization a common source participated more often than at the lowest level of the EEG coherence.     

Measurements of simultaneously recorded spiking activity and local field potentials suggest that spatial selection emerges in the frontal eye field

The frontal eye field (FEF) participates in selecting the location of behaviorally relevant stimuli for guiding attention and eye movements. We simultaneously recorded local field potentials (LFPs) and spiking activity in the FEF of monkeys performing memory-guided saccade and covert visual search tasks. We compared visual latencies and the time course of spatially selective responses in LFPs and spiking activity. Consistent with the view that LFPs represent synaptic input, visual responses appeared first in the LFPs followed by visual responses in the spiking activity. However, spatially selective activity identifying the location of the target in the visual search array appeared in the spikes about 30 ms before it appeared in the LFPs. Because LFPs reflect dendritic input and spikes measure neuronal output in a local brain region, this temporal relationship suggests that spatial selection necessary for attention and eye movements is computed locally in FEF from spatially nonselective inputs.