Multineuronal vectorization is more efficient than time-segmental vectorization for information extraction from neuronal activities in the inferior temporal cortex

In order for patients with disabilities to control assistive devices with their own neural activity, multineuronal spike trains must be efficiently decoded because only limited computational resources can be used to generate prosthetic control signals in portable real-time applications. In this study, we compare the abilities of two vectorizing procedures (multineuronal and time-segmental) to extract information from spike trains during the same total neuron-seconds. In the multineuronal vectorizing procedure, we defined a response vector whose components represented the spike counts of one to five neurons. In the time-segmental vectorizing procedure, a response vector consisted of components representing a neuron’s spike counts for one to five time-segment(s) of a response period of 1 s. Spike trains were recorded from neurons in the inferior temporal cortex of monkeys presented with visual stimuli. We examined whether the amount of information of the visual stimuli carried by these neurons differed between the two vectorizing procedures. The amount of information calculated with the multineuronal vectorizing procedure, but not the time-segmental vectorizing procedure, significantly increased with the dimensions of the response vector. We conclude that the multineuronal vectorizing procedure is superior to the time-segmental vectorizing procedure in efficiently extracting information from neuronal signals.     

Natural movies evoke spike trains with low spike time variability in cat primary visual cortex

Neuronal responses in primary visual cortex have been found to be highly variable. This has led to the widespread notion that neuronal responses have to be averaged over large numbers of neurons to obtain suitably invariant responses that can be used to reliably encode or represent external stimuli. However, it is possible that the high variability of neuronal responses may result from the use of simple, artificial stimuli and that the visual cortex may respond differently to dynamic, naturalistic images. To investigate this question, we recorded the responses of primary visual cortical neurons in the anesthetized cat under stimulation with time-varying natural movies. We found that cortical neurons on the whole exhibited a high degree of spike count variability, but a surprisingly low degree of spike time variability. The spike count variability was further reduced when all but the first spike in a burst were removed. We also found that responses exhibiting low spike time variability exhibited low spike count variability, suggesting that rate coding and temporal coding might be more compatible than previously thought. In addition, we found the spike time variability to be significantly lower when stimulated by natural movies as compared with stimulation using drifting gratings. Our results indicate that response variability in primary visual cortex is stimulus dependent and significantly lower than previous measurements have indicated.     

The neurophysiology of backward visual masking: information analysis.

Backward masking can potentially provide evidence of the time needed for visual processing, a fundamental constraint that must be incorporated into computational models of vision. Although backward masking has been extensively used psychophysically, there is little direct evidence for the effects of visual masking on neuronal responses. To investigate the effects of a backward masking paradigm on the responses of neurons in the temporal visual cortex, we have shown that the response of the neurons is interrupted by the mask. Under conditions when humans can just identify the stimulus, with stimulus onset asynchronies (SOA) of 20 msec, neurons in macaques respond to their best stimulus for approximately 30 msec. We now quantify the information that is available from the responses of single neurons under backward masking conditions when two to six faces were shown. We show that the information available is greatly decreased as the mask is brought closer to the stimulus. The decrease is more marked than the decrease in firing rate because it is the selective part of the firing that is especially attenuated by the mask, not the spontaneous firing, and also because the neuronal response is more variable at short SOAs. However, even at the shortest SOA of 20 msec, the information available is on average 0.1 bits. This compares to 0.3 bits with only the 16-msec target stimulus shown and a typical value for such neurons of 0.4 to 0.5 bits with a 500-msec stimulus. The results thus show that considerable information is available from neuronal responses even under backward masking conditions that allow the neurons to have their main response in 30 msec. This provides evidence for how rapid the processing of visual information is in a cortical area and provides a fundamental constraint for understanding how cortical information processing operates.     

Synaptic learning rules, cortical circuits, and visual function.

Sensory experience is essential for the refinement of neuronal circuits during development and for learning and memory in the adult brain. Such experience-dependent plasticity is largely mediated by activity-dependent synaptic modification. In this review, we focus on a spike timing-dependent synaptic learning rule, in which the direction and magnitude of synaptic modification depend on the relative spike timing of presynaptic and postsynaptic neurons. We discuss a series of recent studies exploring the functional implications of this learning rule in the visual system. These studies show that temporally patterned visual stimuli can cause rapid changes in visual circuits, neuronal receptive fields, and visual perception, with a temporal specificity of tens of milliseconds. Particularly, motion stimuli that are common in natural scenes may interact strongly with the spike timing-dependent learning rule, leaving distinct marks in the perceptual function of the mature brain.     

Neurons in the amygdala of the monkey with responses selective for faces

To investigate the functions of the amygdala in visual information processing and in emotional and social responses, recordings were made from single neurons in the amygdala of the monkey. A population of neurons (40 of more than 1000 recorded in 4 monkeys) was investigated which responded primarily to faces. These neurons typically (1) responded to some human or monkey faces, which were presented to the monkey through a large aperture shutter so that response latencies could be measured, or were simply shown to the monkey, (2) responded to 2-dimensional representations of these faces, as well as to real 3-dimensional faces, (3) had no responses or only small (less than half maximum) responses to gratings, simple geometrical, other complex 3-D stimuli, or to arousing and aversive stimuli, (4) had response latencies of 110-200 ms, (5) were located in the basal accessory nucleus of the amygdala, (6) responded differently to different faces, as shown by measures of d', and could thus over a population of such neurons code information useful for making different responses to different individuals, (7) could in some cases (9/11 tested) respond to parts of faces, and (8) in a few cases (4/19 tested) responded more to a face which produced an emotional response. A comparison made in three monkeys of the responses of these neurons with the responses of 77 neurons with face-selective responses recorded in the cortex of the superior temporal sulcus (STS) showed that the amygdaloid neurons had longer response latencies (110-200 compared to 90-140 ms), and were in some respects more selective in their responses to different faces. It is suggested that the deficits in social and emotional behavior produced by amygdala lesions could be due in part to damage to a neuronal system specialized in utilizing information from faces so that appropriate social and emotional responses can be made to different individuals.     

Conditioned spikes: a simple and fast method to represent rates and temporal patterns in multielectrode recordings

Increasing evidence suggests that the brain utilizes distributed codes that can only be analyzed by simultaneously recording the activity of multiple neurons. This paper introduces a new methodology for studying neural ensemble recordings. The method uses a novel representation to provide complementary information about the stimuli which are contained in the temporal pattern of the spike sequence. By using this procedure, a high correlation of synchronized events with stimuli times is apparent. To quantify the results and to compare the performance of this method against the most traditional raster plot, we have used Fano factor and cross-correlation analysis. Our results suggest that several consecutive spikes from different neurons within an extended time window may encode behaviorally relevant information. We propose that this new representation, in addition to the other approaches currently used (standard raster plots, multivariate statistical methods, neuronal networks, information theory, etc.), can be a useful procedure to describe population spike dynamics.     

Neurons evaluate both the amplitude and the meaning of signals

The neuronal threshold, which can be determined by the level of depolarization immediately prior to spike generation, is different for responses to conditioned and discriminated stimuli after conditioning. However, it is impossible to determine excitability within a response to stimuli that failed to generate a spike. In the present study we examined the role of the AP threshold of two related Helix defensive neurons in the initiation of an AP during elaboration of the neuronal analog of a classical conditional reflex. These neurons fire in a synchronous manner though spikes sometimes are not generated simultaneously. We compared the change in spike threshold within the responses with other parameters of intracellular activity that also affect the neuronal response, but which can be measured without an analysis of the spike waveform (spike latency, slope of excitatory postsynaptic potentials, etc.). We found that the change in slope is not the reason for the change in the threshold during pairing. The thresholds within conditioned and discriminated responses affected spike generation and their values correlated with the spike presence or absence in related neurons. The excitability changed transiently within the responses, since these alterations were selective for the conditioned and discriminated stimuli and applied primarily to the first spike of the response. The firing threshold seems to be a dynamic property of excitable membrane. Neurons appear to evaluate the significance of the input signal, transiently change their own excitability and only after that compare the magnitude of this signal and threshold.     

A comparative analysis of integrating visual information in local neuronal ensembles

Spike directivity, a new measure that quantifies the transient charge density dynamics within action potentials provides better results in discriminating different categories of visual object recognition. Specifically, intracranial recordings from medial temporal lobe (MTL) of epileptic patients have been analyzed using firing rate, interspike intervals and spike directivity. A comparative statistical analysis of the same spikes from a local ensemble of four selected neurons shows that electrical patterns in these neurons display higher separability to input images compared to spike timing features. If the observation vector includes data from all four neurons then the comparative analysis shows a highly significant separation between categories for spike directivity (p=0.0023) and does not display separability for interspike interval (p=0.3768) and firing rate (p=0.5492). Since electrical patterns in neuronal spikes provide information regarding different presented objects this result shows that related information is intracellularly processed in neurons and carried out within a millisecond-level time domain of action potential occurrence. This significant statistical outcome obtained from a local ensemble of four neurons suggests that meaningful information can be electrically inferred at the network level to generate a better discrimination of presented images.     

Massively parallel neural circuits for stereoscopic color vision: Encoding,decoding and identification

Past work demonstrated how monochromatic visual stimuli could be faithfully encoded and decoded under Nyquist-type rate conditions. Color visual stimuli were then traditionally encoded and decoded in multiple separate monochromatic channels. The brain, however, appears to mix information about color channels at the earliest stages of the visual system, including the retina itself. If information about color is mixed and encoded by a common pool of neurons, how can colors be demixed and perceived?We present Color Video Time Encoding Machines (Color Video TEMs) for encoding color visual stimuli that take into account a variety of color representations within a single neural circuit. We then derive a Color Video Time Decoding Machine (Color Video TDM) algorithm for color demixing and reconstruction of color visual scenes from spikes produced by a population of visual neurons. In addition, we formulate Color Video Channel Identification Machines (Color Video CIMs) for functionally identifying color visual processing performed by a spiking neural circuit.Furthermore, we derive a duality between TDMs and CIMs that unifies the two and leads to a general theory of neural information representation for stereoscopic color vision. We provide examples demonstrating that a massively parallel color visual neural circuit can be first identified with arbitrary precision and its spike trains can be subsequently used to reconstruct the encoded stimuli. We argue that evaluation of the functional identification methodology can be effectively and intuitively performed in the stimulus space. In this space, a signal reconstructed from spike trains generated by the identified neural circuit can be compared to the original stimulus.     

State-Dependent Cortical Unit Activity Reflects Dynamic Brain State Transitions in Anesthesia

Understanding the effects of anesthesia on cortical neuronal spiking and information transfer could help illuminate the neuronal basis of the conscious state. Recent investigations suggest that the brain state identified by local field potential spectrum is not stationary but changes spontaneously at a fixed level of anesthetic concentration. How cortical unit activity changes with dynamically transitioning brain states under anesthesia is unclear. Extracellular unit activity was measured with 64-channel silicon microelectrode arrays in cortical layers 5/6 of the primary visual cortex of chronically instrumented, freely moving male rats (n = 7) during stepwise reduction of the anesthetic desflurane (6%, 4%, 2%, and 0%). Unsupervised machine learning applied to multiunit spike patterns revealed five distinct brain states. A novel desynchronized brain state with increased spike rate variability, sample entropy, and EMG activity occurred in 6% desflurane with 40.0% frequency. The other four brain states reflected graded levels of anesthesia. As anesthesia deepened the spike rate of neurons decreased regardless of their spike rate profile at baseline conscious state. Actively firing neurons with wide-spiking pattern showed increased bursting activity along with increased spike timing variability, unit-to-population correlation, and unit-to-unit transfer entropy, despite the overall decrease in transfer entropy. The narrow-spiking neurons showed similar changes but to a lesser degree. These results suggest that (1) anesthetic effect on spike rate is distinct from sleep, (2) synchronously fragmented spiking pattern is a signature of anesthetic-induced unconsciousness, and (3) the paradoxical, desynchronized brain state in deep anesthesia contends the generally presumed monotonic, dose-dependent anesthetic effect on the brain.SIGNIFICANCE STATEMENT Recent studies suggest that spontaneous changes in brain state occur under anesthesia. However, the spiking behavior of cortical neurons associated with such state changes has not been investigated. We found that local brain states defined by multiunit activity had a nonunitary relationship with the current anesthetic level. A paradoxical brain state displaying asynchronous firing pattern and high EMG activity was found unexpectedly in deep anesthesia. In contrast, the synchronous fragmentation of neuronal spiking appeared to be a robust signature of the state of anesthesia. The findings challenge the assumption of monotonic, anesthetic dose-dependent behavior of cortical neuron populations. They enhance the interpretation of neuroscientific data obtained under anesthesia and the understanding of the neuronal basis of anesthetic-induced state of unconsciousness.     

Diversity,heterogeneity and orientation‐dependent variation of spike count correlation in the cat visual cortex

Cortical neurons are known to be noisy encoders of information, showing large response variabilities with repeated presentations of identical stimuli. These spike count variabilities are correlated over the cell population and their neuronal mechanism and functional significance have not been well understood. Recently there has been much debate over the magnitude of the population mean of the correlation, ranging from 0.1 to 0.2 down to nearly zero. We performed multi‐neuron recordings on the cat visual cortex and found that the population mean did not necessarily represent the nature of correlated variabilities because the spike count correlation showed significant diversity and heterogeneity. Although the population mean was relatively small (0.06), the correlations of individual unit pairs were distributed over a broad range, extending to both positive and negative values. In most of the recording sessions of local cell populations (83%), significantly positive correlations coexisted with significantly negative ones in different unit pairs. Furthermore, nearly 20% of the unit pairs showed significant variation in the spike count correlation for different stimulus orientations. Correlation analysis between the spike count correlation and the firing activity of the unit pair suggested that the orientation tuning properties of the two quantities were unlikely to have originated from a common neuronal mechanism. Diversity, heterogeneity and context‐dependent variation suggests that the correlated spike count variabilities originate not from fixed anatomical connections but rather from the dynamic interaction of neuronal networks.     

Spectral representation--analyzing single-unit activity in extracellularly recorded neuronal data without spike sorting

One step in the conventional analysis of extracellularly recorded neuronal data is spike sorting, which separates electrical signal into action potentials from different neurons. Because spike sorting involves human judgment, it can be subjective and time intensive, particularly for large sets of neurons. Here we propose a simple, automated way to construct alternative representations of neuronal activity, called spectral representation (SR). In this approach, neuronal spikes are mapped to a discrete space of spike waveform features and time. Spectral representation enables us to find single-unit stimulus-related changes in neuronal activity without spike sorting. We tested the ability of this method to predict stimuli using both simulated data and experimental data from an auditory mapping study in anesthetized marmoset monkeys. We find that our approach produces more accurate classification of stimuli than spike-sorted data for both simulated and experimental conditions. Furthermore, this method lends itself to automated analysis of extracellularly recorded neuronal ensembles. Additionally, we suggest ways in which these representations can be readily extended to assist in spike sorting and the evaluation of single-neuron peri-stimulus time histograms.     

Neuronal integration mechanisms have little effect on spike auto-correlations of cortical neurons.

Cortical neurons of behaving animals generate irregular spike sequences, but the sequences generally differ from an entirely random sequence (Poisson process), and they have temporal correlations (spike auto-correlations). Temporally correlated spike sequences can be brought about because of incoming synaptic inputs to the neuron, or because of the neuronal integration mechanism. In this paper, we attempt to determine which is the origin of spike auto-correlations observed in the spiking data recorded from neurons in the prefrontal cortex of a monkey preserving a cue information in the delay response task experiment. Each incoming input is assumed to be independent from its own spike events, and the temporal integration in the neuron is assumed to be reset by every spike event. So, the process to spike is assumed to be divided into two processes: the process independent from its own spikes, which drives the process reset by its own spikes. Under these assumptions, it is found that the spike-independent process needs to have temporal correlations, through examinations of two kinds of correlation coefficient of consecutive inter-spike intervals. It is also found that the spike-reset process has little effect on the spike auto-correlations and the interval distributions. This suggests that the spike auto-correlation does originate in the temporal correlation of incoming synaptic inputs and the neuronal integration mechanism has little effect on the spike auto-correlation.     

Spike-based strategies for rapid processing.

Most experimental and theoretical studies of brain function assume that neurons transmit information as a rate code, but recent studies on the speed of visual processing impose temporal constraints that appear incompatible with such a coding scheme. Other coding schemes that use the pattern of spikes across a population a neurons may be much more efficient. For example, since strongly activated neurons tend to fire first, one can use the order of firing as a code. We argue that Rank Order Coding is not only very efficient, but also easy to implement in biological hardware: neurons can be made sensitive to the order of activation of their inputs by including a feed-forward shunting inhibition mechanism that progressively desensitizes the neuronal population during a wave of afferent activity. In such a case, maximum activation will only be produced when the afferent inputs are activated in the order of their synaptic weights.     

Rate maintenance and resonance in the entorhinal cortex

Throughout the brain, neurons encode information in fundamental units of spikes. Each spike represents the combined thresholding of synaptic inputs and intrinsic neuronal dynamics. Here, we address a basic question of spike train formation: how do perithreshold synaptic inputs perturb the output of a spiking neuron? We recorded from single entorhinal principal cells in vitro and drove them to spike steadily at ～5 Hz (theta range) with direct current injection, then used a dynamic‐clamp to superimpose strong excitatory conductance inputs at varying rates. Neurons spiked most reliably when the input rate matched the intrinsic neuronal firing rate. We also found a striking tendency of neurons to preserve their rates and coefficients of variation, independently of input rates. As mechanisms for this rate maintenance, we show that the efficacy of the conductance inputs varied with the relationship of input rate to neuronal firing rate, and with the arrival time of the input within the natural period. Using a novel method of spike classification, we developed a minimal Markov model that reproduced the measured statistics of the output spike trains and thus allowed us to identify and compare contributions to the rate maintenance and resonance. We suggest that the strength of rate maintenance may be used as a new categorization scheme for neuronal response and note that individual intrinsic spiking mechanisms may play a significant role in forming the rhythmic spike trains of activated neurons; in the entorhinal cortex, individual pacemakers may dominate production of the regional theta rhythm.     

Measuring spike pattern reliability with the Lempel-Ziv-distance

Spike train distance measures serve two purposes: to measure neuronal firing reliability, and to provide a metric with which spike trains can be classified. We introduce a novel spike train distance based on the Lempel-Ziv complexity that does not require the choice of arbitrary analysis parameters, is easy to implement, and computationally cheap. We determine firing reliability in vivo by calculating the deviation of the mean distance of spike trains obtained from multiple presentations of an identical stimulus from a Poisson reference. Using both the Lempel-Ziv-distance (LZ-distance) and a distance focussing on coincident firing, the pattern and timing reliability of neuronal firing is determined for spike data obtained along the visual information processing pathway of macaque monkey (LGN, simple and complex cells of V1, and area MT). In combination with the sequential superparamagnetic clustering algorithm, we show that the LZ-distance groups together spike trains with similar but not necessarily synchronized firing patterns. For both applications, we show how the LZ-distance gives additional insights, as it adds a new perspective on the problem of firing reliability determination and allows neuron classifications in cases, where other distance measures fail.     

Role of the locus coeruleus in transmission onto anterior colliculus neurons.

The present study was an attempt to elucidate the role of locus coeruleus (LC) on neuronal activities in the anterior colliculus of rats anesthetized with alpha-chloralose. The spike latency of neurons in the deep grey and white layers of anterior colliculus elicited by stimulation of the optic chiasm (OC), lateral geniculate body (LGB) and visual cortex (VC) was longer than that of neurons in the more superficial layers: optic and intermediate grey layers. When conditioning stimuli were applied to LC, a significant inhibition of spike generation upon OC, LGB and VC stimulation was observed on neurons of deep grey and white layers, but not on those of optic and intermediate grey layers. The conditioning stimulation did not alter spike generation, either in the neurons of deep grey and white layers or those of optic and intermediate grey layers, following stimulation of the superficial grey layer. These results strongly suggest that noradrenaline originating in the LC could produce an inhibition of neuronal activities in the deep grey and white layers, and such is probably the result of inhibition of neurons located in the superficial layer of the anterior colliculus.     

How the brain uses time to represent and process visual information

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.