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.     

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.     

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.     

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.     

Multiplexed Representation of Itch and Mechanical and Thermal Sensation in the Primary Somatosensory Cortex

The primary somatosensory cortex (S1) plays a critical role in processing multiple somatosensations, but the mechanism underlying the representation of different submodalities of somatosensation in S1 remains unclear. Using in vivo two-photon calcium imaging that simultaneously monitors hundreds of layer 2/3 pyramidal S1 neurons of awake male mice, we examined neuronal responses triggered by mechanical, thermal, or pruritic stimuli. We found that mechanical, thermal, and pruritic stimuli activated largely overlapping neuronal populations in the same somatotopic S1 subregion. Population decoding analysis revealed that the local neuronal population in S1 encoded sufficient information to distinguish different somatosensory submodalities. Although multimodal S1 neurons responding to multiple types of stimuli exhibited no spatial clustering, S1 neurons preferring mechanical and thermal stimuli tended to show local clustering. These findings demonstrated the coding scheme of different submodalities of somatosensation in S1, paving the way for a deeper understanding of the processing and integration of multimodal somatosensory information in the cortex.SIGNIFICANCE STATEMENT Cortical processing of somatosensory information is one of the most fundamental aspects in cognitive neuroscience. Previous studies mainly focused on mechanical sensory processing within the rodent whisking system, but mechanisms underlying the coding of multiple somatosensations remain largely unknown. In this study, we examined the representation of mechanical, thermal, and pruritic stimuli in S1 by in vivo two-photon calcium imaging of awake mice. We revealed a multiplexed representation for multiple somatosensory stimuli in S1 and demonstrated that the activity of a small population of S1 neurons is capable of decoding different somatosensory submodalities. Our results elucidate the coding mechanism for multiple somatosensations in S1 and provide new insights that improve the present understanding of how the brain processes multimodal sensory information.     

Population coding of song element sequence in the Bengalese finch HVC

Birdsong is a complex vocalization composed of various song elements organized according to sequential rules. Two alternative views exist that explain the neural representation of song element sequences in the songbird brain. The finding of sequential selective neurons supports the idea that the song element sequence is encoded in a chain of rigid selective neurons. Alternatively, song structure could be encoded in an ensemble of relatively broad selective neurons arranged in a distributed manner. Here we attempted to determine which neural representation actually occurs in the song system by recording neural responses to various stimuli and performing information-theoretic analysis on the data obtained. We recorded the neural responses to all possible element pairs of stimuli in the Bengalese finch brain nucleus high vocal centre (HVC). Our results showed that each neuron has broad but differential response properties to element sequences beyond the structure of self-generated song. To quantify the transmitted information by such a broadly tuned neural population, we calculated the time course of mutual information between auditory stimuli and neural activities. Confounded information, which represents the relationship between present and previous elements, increased significantly immediately after stimulus presentation. These results indicate that the song element sequence is encoded in a neural ensemble in the HVC via population coding. These findings give us a new encoding scheme for the song element sequence using a distributed neural representation rather than the chain model of rigid selective neurons.     

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.     

Spike sorting based on automatic template reconstruction with a partial solution to the overlapping problem

A new method for spike sorting is proposed which partly solves the overlapping problem. Principal component analysis and subtractive clustering techniques are used to estimate the number of neurons contributing to multi-unit recording. Spike templates (i.e. waveforms) are reconstructed according to the clustering results. A template-matching procedure is then performed. Firstly all temporally displaced templates are compared with the spike event to find the best-fitting template that yields the minimum residue variance. If the residue passes the chi(2)-test, the matching procedure stops and the spike event is classified as the best-fitting template. Otherwise the spike event may be an overlapping waveform. The procedure is then repeated with all possible combinations of two templates, three templates, etc. Once one combination is found, which yields the minimum residue variance among the combinations of the same number of component templates and makes the residue pass the chi(2)-test, the matching procedure stops. It is unnecessary to check the remaining combinations of more templates. Consequently, the computational effort is reduced and the over-fitting problem can be partly avoided. A simulated spike train was used to assess the performance of the proposed method, which was also applied to a real recording of chicken retina ganglion cells.     

The distribution of neuronal population activation (DPA) as a tool to study interaction and integration in cortical representations

In many cortical areas, simple stimuli or task conditions activate large populations of neurons. We hypothesize that such populations support processes of interaction within parametric representations and integration of multiple sources of input and we propose to study these processes using distributions of population activation (DPAs) as a tool. Such distributions can be viewed as neuronal representations of continuous stimulus or task parameters. They are built from basis functions contributed by each neuron. These functions may be explicitly chosen based on tuning curves or receptive field profiles. Or they may be determined by minimizing the distance between chosen target distributions and the constructed DPAs. In both cases, construction of the DPA is based on a set of reference conditions in which the stimulus or task parameters are sampled experimentally. In a second step, basis functions are kept fixed, and the DPAs are used to explore time dependent processing, interaction and integration of information. For instance, stimuli which simultaneously specify multiple parameter values can be used to study interactions within the parametric representation. We review an experiment, in which the representation of retinal position is probed in this way, revealing fast excitatory interactions among neurons representing similar retinal positions and slower inhibitory interactions among neurons representing dissimilar retinal positions. Similarly, DPAs can be used to analyze different sources of input that are fused within a parametric representation. We review an experiment in which the representation of the direction of goal-directed arm movements in motor and premotor cortex is studied when prior and current information about upcoming movement tasks are integrated.     

Face identification using one spike per neuron: resistance to image degradations.

The short response latencies of face selective neurons in the inferotemporal cortex impose major constraints on models of visual processing. It appears that visual information must essentially propagate in a feed-forward fashion with most neurons only having time to fire one spike. We hypothesize that flashed stimuli can be encoded by the order of firing of ganglion cells in the retina and propose a neuronal mechanism, that could be related to fast shunting inhibition, to decode such information. Based on these assumptions, we built a three-layered neural network of retino-topically organized neuronal maps. We showed, by using a learning rule involving spike timing dependant plasticity, that neuronal maps in the output layer can be trained to recognize natural photographs of faces. Not only was the model able to generalize to novel views of the same faces, it was also remarkably resistant to image noise and reductions in contrast.     

Neuronal correlates of aversive learning in command neurons for avoidance behavior ofHelix lucorum L.

Changes in responsiveness to food and noxious stimuli were studied in interneurons controlling feeding behaior and in putative command neurons for avoidance behavior after 10–15 paired presentaations of food and electrical shock in the pulmonate snail Helix lucorum L. It was shown earlier¹⁷ that such aversive learning procedure is associative, and the behavioral aversion to the reinforced type of food in intat snails is maintained for at least 21 days, while normal feeding behavior can be evoked by another type of food. Responses to food presentation in the feeding behavior interneurons changed only in pattern after learning procedure, while in the command neurons for avoidance behavior a new spike reaction appeared, which is assumed to be responsible for the behavioral changes. A possible mechanism of a conditioned reaction in the command neurons for avoidance behavior is discussed.     

Gravitational representation of simultaneously recorded brainstem respiratory neuron spike trains

Experiments designed to study concurrent processes in neural networks have been hampered by limitations of available analytical methods. A recently described gravitational representation of spike train data was used to evaluate groups of simultaneously monitored medullary respiratory related neurons in anesthetized, vagotomized cats. The results establish that the method can detect and define functional associations among elements of such groups after as few as 20 respiratory cycles.     

Category representation and generalization in the prefrontal cortex

Categorization is a function of the brain that serves to group together items and events in our environments. Here we review the following important issues related to category representation and generalization: namely, where categories are presented in the brain, and how the brain utilizes categorical membership to generate new information. Accumulated experimental evidence shows that the prefrontal cortex (PFC) plays a critical role in category formation and generalization. We propose that prefrontal neurons abstract the commonality beyond individual stimuli, and categorize these based on their common meaning by ignoring their physical properties and learning to represent the boundaries between behaviorally significant categories. We also claim that a subgroup of prefrontal neurons simultaneously receives the category-related information and specific property information (e.g. reward) associated with an exemplar, to form a category-based representation of that property, and propagates it among stimuli of the same category, possibly reflecting a neural basis for category generalization in the PFC. These results suggest that the PFC is involved in representing abstract rules, and generating new information on the basis of previously acquired knowledge.     

Modeling and analyzing higher-order correlations in non-Poissonian spike trains

Measuring pairwise and higher-order spike correlations is crucial for studying their potential impact on neuronal information processing. In order to avoid misinterpretation of results, the tools used for data analysis need to be carefully calibrated with respect to their sensitivity and robustness. This, in turn, requires surrogate data with statistical properties common to experimental spike trains. Here, we present a novel method to generate correlated non-Poissonian spike trains and study the impact of single-neuron spike statistics on the inference of higher-order correlations. Our method to mimic cooperative neuronal spike activity allows the realization of a large variety of renewal processes with controlled higher-order correlation structure. Based on surrogate data obtained by this procedure we investigate the robustness of the recently proposed method empirical de-Poissonization (Ehm et al., 2007). It assumes Poissonian spiking, which is common also for many other estimation techniques. We observe that some degree of deviation from this assumption can generally be tolerated, that the results are more reliable for small analysis bins, and that the degree of misestimation depends on the detailed spike statistics. As a consequence of these findings we finally propose a strategy to assess the reliability of results for experimental data.     

Stimulus duration in working memory is represented by neuronal activity in the monkey prefrontal cortex

Humans are capable of memorizing several attributes of a presented stimulus as well as its duration of presentation. However, the neuronal representation of stimulus duration in memory remains unknown. This study investigated activities of single neurons in the prefrontal cortex of monkeys while they were performing a behavioral task in which working memory for stimulus duration was needed. Here we describe specific neurons whose discharge rates reflect encoding or retention of the duration of the presentation of stimuli to be remembered. We also describe other specific neurons whose activities reflect encoding or retention of fixed duration, similar but unrelated to the stimulus duration presented in each trial. Some of these specific neurons showed the same duration-related discharges even while the monkeys were performing a different task, in which working memory for stimulus duration was no longer needed. From these results, we suggest that neurons in the prefrontal cortex play roles in encoding and retention of temporal information in working memory and that some of those neurons are dedicated to representation of temporal information attributed to stimuli even when the temporal information is unnecessary for correct performance.     

Generation of spike latency tuning by thalamocortical circuits in auditory cortex

In many sensory systems, the latency of spike responses of individual neurons is found to be tuned for stimulus features and proposed to be used as a coding strategy. Whether the spike latency tuning is simply relayed along sensory ascending pathways or generated by local circuits remains unclear. Here, in vivo whole-cell recordings from rat auditory cortical neurons in layer 4 revealed that the onset latency of their aggregate thalamic input exhibited nearly flat tuning for sound frequency, whereas their spike latency tuning was much sharper with a broadly expanded dynamic range. This suggests that the spike latency tuning is not simply inherited from the thalamus, but can be largely reconstructed by local circuits in the cortex. Dissecting of thalamocortical circuits and neural modeling further revealed that broadly tuned intracortical inhibition prolongs the integration time for spike generation preferentially at off-optimal frequencies, while sharply tuned intracortical excitation shortens it selectively at the optimal frequency. Such push and pull mechanisms mediated likely by feedforward excitatory and inhibitory inputs respectively greatly sharpen the spike latency tuning and expand its dynamic range. The modulation of integration time by thalamocortical-like circuits may represent an efficient strategy for converting information spatially coded in synaptic strength to temporal representation.     

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.     

A model for the interaction of oscillations and pattern generation with real-time computing in generic neural microcircuit models.

It is shown that real-time computations on spike patterns and temporal integration of information in neural microcircuit models are compatible with potentially descruptive additional inputs such as oscillations. A minor change in the connection statistics of such circuits (making synaptic connections to more distal target neurons more likely for excitatory than for inhibitory neurons) endows such generic neural microcircuit model with the ability to generate periodic patterns autonomously. We show that such pattern generation can also be multiplexed with pattern classification and temporal integration of information in the same neural circuit. These results can be interpreted as showing that periodic activity provides a second channel for communication in neural systems which can be used to synchronize or coordinate spatially separated processes, without encumbering local real-time computations on spike trains in diverse neural circuits.     

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.     

Developments in understanding neuronal spike trains and functional specializations in brain regions.

Understanding information processing at the neuronal level would provide valuable insights to computational intelligence research and computational neuroscience. In particular, understanding constraints on neuronal spike trains would provide indication about the type of syntactic rules used by neurons when processing information. A recent discovery, reported here, was made through analyzing microelectrode recordings (MER) made during surgical procedure in humans. Analysis of MERs of extracellular neuronal activity has gained increasing interest due to potential improvements to surgical techniques involving ablation or placement of deep brain stimulators, done in the treatment of advanced Parkinson's disease. Important to these procedures is the identification of different brain structures such as the globus pallidus internus from the spike train being recorded from the intracranial probe tip during surgery. Spike train data gathered during surgical procedure from multiple patients were processed using a novel feature extraction method reported here. Distinct structures within the spike trains were identified and used to build an effective brain region classifier. The extracted features upon analysis provide some insight into the 'syntactic' constraint on spike trains.