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.     

Input prediction and autonomous movement analysis in recurrent circuits of spiking neurons

Temporal integration of information and prediction of future sensory inputs are assumed to be important computational tasks of generic cortical microcircuits. It has remained open how cortical microcircuits could possibly achieve this, especially since they consist--in contrast to most neural network models--of neurons and synapses with heterogeneous dynamic responses. It turns out, however, that the diversity of computational units increases the capability of microcircuit models for temporal integration. Furthermore the prediction of future input may be rather easy for such circuits since it suffices to train the readouts from such microcircuits. In this article we show that very simple readouts from a generic recurrently connected circuit of integrate-and-fire neurons with diverse dynamic synapses can be trained in an unsupervised manner to predict movements of different objects, that move within an unlimited number of combinations of speed, angle, and offset over a simulated sensory field. The autonomously trained microcircuit model is also able to compute the direction of motion, which is a computationally difficult problem ('aperture problem') since it requires disambiguation of local sensory readings through the context of other sensory readings at the current and preceding moments. Furthermore the same circuit can be trained simultaneously in a supervised manner to also report the shape and velocity of the moving object. Finally it is shown that the trained neural circuit supports novelty detection and the generation of 'imagined movements'. Altogether the results of this article suggest that it is not necessary to construct specific and biologically unrealistic neural circuit models for specific sensory processing tasks, since 'found' generic cortical microcircuit models in combination with very simple perceptron-like readouts can easily be trained to solve such computational tasks.     

Edge of chaos and prediction of computational performance for neural circuit models.

We analyze in this article the significance of the edge of chaos for real-time computations in neural microcircuit models consisting of spiking neurons and dynamic synapses. We find that the edge of chaos predicts quite well those values of circuit parameters that yield maximal computational performance. But obviously it makes no prediction of their computational performance for other parameter values. Therefore, we propose a new method for predicting the computational performance of neural microcircuit models. The new measure estimates directly the kernel property and the generalization capability of a neural microcircuit. We validate the proposed measure by comparing its prediction with direct evaluations of the computational performance of various neural microcircuit models. The proposed method also allows us to quantify differences in the computational performance and generalization capability of neural circuits in different dynamic regimes (UP- and DOWN-states) that have been demonstrated through intracellular recordings in vivo.     

Ascertaining neuron importance by information theoretical analysis in motor Brain–Machine Interfaces

Point process modeling of neural spike recordings has the potential to capture with high specificity the information contained in spike time occurrence. In Brain–Machine Interfaces (BMIs) the neural tuning characteristic assessed from neural spike recordings can distinguish neuron importance in terms of its modulation with the movement task. Consequently, it improves generalization and reduces significantly computation in previous decoding algorithms, where models reconstruct the kinematics from recorded activities of hundreds of neurons. We propose to apply information theoretical analysis based on an instantaneous tuning model to extract the important neuron subsets for point process decoding on BMI. The cortical distribution of extracted neuron subsets is analyzed and the statistical decoding performance using subset selection is studied with respect to different number of neurons and compared to the one by the full neuron ensemble. With much less computation, the extracted importance neurons provide comparable kinematic reconstructions compared to the full neuron ensemble. The performance of the extracted subset is compared to the random selected subset with same number of neurons to further validate the effectiveness of the subset-extraction approach.     

Synergy from silence in a combinatorial neural code

The manner in which groups of neurons represent events in the external world is a central question in neuroscience. Estimation of the information encoded by small groups of neurons has shown that in many neural systems, cells carry mildly redundant information. These measures average over all the activity patterns of a neural population. Here, we analyze the population code of the salamander and guinea pig retinas by quantifying the information conveyed by specific multicell activity patterns. Synchronous spikes, even though they are relatively rare and highly informative, convey less information than the sum of either spike alone, making them redundant coding symbols. Instead, patterns of spiking in one cell and silence in others, which are relatively common and often overlooked as special coding symbols, were found to be mostly synergistic. Our results reflect that the mild average redundancy between ganglion cells that was previously reported is actually the result of redundant and synergistic multicell patterns, whose contributions partially cancel each other when taking the average over all patterns. We further show that similar coding properties emerge in a generic model of neural responses, suggesting that this form of combinatorial coding, in which specific compound patterns carry synergistic or redundant information, may exist in other neural circuits.     

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.     

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.     

Aberrant features of in vivo striatal dynamics in Parkinson's disease

The striatum plays an important role in learning, selecting, and executing actions. As a major input hub of the basal ganglia, it receives and processes a diverse array of signals related to sensory, motor, and cognitive information. Aberrant neural activity in this area is implicated in a wide variety of neurological and psychiatric disorders. It is therefore important to understand the hallmarks of disrupted striatal signal processing. This review surveys literature examining how in vivo striatal microcircuit dynamics are impacted in animal models of one of the most widely studied movement disorders, Parkinson's disease. The review identifies four major features of aberrant striatal dynamics: altered relative levels of direct and indirect pathway activity, impaired information processing by projection neurons, altered information processing by interneurons, and increased synchrony.     

Coarse-Grained Clustering Dynamics of Heterogeneously Coupled Neurons

We study a population of spiking neurons which are subject to independent noise processes and a strong common time-dependent input. We show that the response of output spikes to independent noise shapes information transmission of such populations even when information transmission properties of single neurons are left unchanged. In particular, we consider two Poisson models in which independent noise either (i) adds and deletes spikes (AD model) or (ii) shifts spike times (STS model). We show that in both models suprathreshold stochastic resonance (SSR) can be observed, where the information transmitted by a neural population is increased with addition of independent noise. In the AD model, the presence of the SSR effect is robust and independent of the population size or the noise spectral statistics. In the STS model, the information transmission properties of the population are determined by the spectral statistics of the noise, leading to a strongly increased effect of SSR in some regimes, or an absence of SSR in others. Furthermore, we observe a high-pass filtering of information in the STS model that is absent in the AD model. We quantify information transmission by means of the lower bound on the mutual information rate and the spectral coherence function. To this end, we derive the signal–output cross-spectrum, the output power spectrum, and the cross-spectrum of two spike trains for both models analytically.     

From Neuroelectrodynamics to Thinking Machines

Natural systems can provide excellent solutions to build artificial intelligent systems. The brain represents the best model of computation that leads to general intelligent action. However, current mainstream models reflect a weak understanding of computations performed in the brain that is translated in a failure of building powerful thinking machines. Specifically, temporal reductionist neural models elude the complexity of information processing since spike timing models reinforce the idea of neurons that compress temporal information and that computation can be reduced to a communication of information between neurons. The active brain dynamics and neuronal data analyses reveal multiple computational levels where information is intracellularly processed in neurons. New experimental findings and theoretical approach of neuroelectrodynamics challenge current models as they now stand and advocate for a change in paradigm for bio-inspired computing machines.     

A neural circuit basis for spatial working memory.

The maintenance of a mental image in memory over a time scale of seconds is mediated by the persistent discharges of neurons in a distributed brain network. The representation of the spatial location of a remembered visual stimulus has been studied most extensively and provides the best-understood model of how mnemonic information is encoded in the brain. Neural correlates of spatial working memory are manifested in multiple brain areas, including the prefrontal and parietal association cortices. Spatial working memory ability is severely compromised in schizophrenia, a condition that has been linked to prefrontal cortical malfunction. Recent computational modeling work, in interplay with physiological studies of behaving monkeys, has begun to identify microcircuit properties and neural dynamics that are sufficient to generate memory-related persistent activity in a recurrent network of excitatory and inhibitory neurons during spatial working memory. This review summarizes recent results and discusses issues of current debate. It is argued that understanding collective neural dynamics in a recurrent microcircuit provides a key step in bridging the gap between network memory function and its underlying cellular mechanisms. Progress in this direction will shed fundamental insights into the neural basis of spatial working memory impairment associated with mental disorders.     

Optimal firing rate estimation.

We define a measure for evaluating the quality of a predictive model of the behavior of a spiking neuron. This measure, information gain per spike (Is), indicates how much more information is provided by the model than if the prediction were made by specifying the neuron's average firing rate over the same time period. We apply a maximum Is criterion to optimize the performance of Gaussian smoothing filters for estimating neural firing rates. With data from bullfrog vestibular semicircular canal neurons and data from simulated integrate-and-fire neurons, the optimal bandwidth for firing rate estimation is typically similar to the average firing rate. Precise timing and average rate models are limiting cases that perform poorly. We estimate that bullfrog semicircular canal sensory neurons transmit in the order of 1 bit of stimulus-related information per spike.     

An analytical comparison of the information in sorted and non-sorted cosine-tuned spike activity

Spike sorting is a technologically expensive component of the signal processing chain required to interpret population spike activity acquired in a neuromotor prosthesis. No systematic analysis of the value of spike sorting has been carried out, and little is known about the effects of spike sorting error on the ability of a brain-machine interface (BMI) to decode intended motor commands. We developed a theoretical framework to examine the effects of spike processing on the information available to a BMI decoder. We computed the mutual information in neural activity in a simplified model of directional cosine tuning to compare the effects of pooling activity from up to four neurons to the effects of sorting with varying amounts of spike error. The results showed that information in a small population of cosine-tuned neurons is maximized when the responses are sorted and there is diverse tuning of units, but information was affected little when pooling units with similar preferred directions. Spike error had adverse effects on information, such that non-sorted population activity had 79-92% of the information in its sorted counterpart for reasonable amounts of detection and sorting error and for units with moderate differences in preferred direction. This quantification of information loss associated with pooling units and with spike detection and sorting error will help to guide the engineering decisions in designing a BMI spike processing system.     

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.     

Measuring the quality of neuronal identification in ensemble recordings

Technological advances in electrode construction and digital signal processing now allow recording simultaneous extracellular action potential discharges from many single neurons, with the potential to revolutionize understanding of the neural codes for sensory, motor, and cognitive variables. Such studies have revealed the importance of ensemble neural codes, encoding information in the dynamic relationships among the action potential spike trains of multiple single neurons. Although the success of this research depends on the accurate classification of extracellular action potentials to individual neurons, there are no widely used quantitative methods for assessing the quality of the classifications. Here we describe information theoretic measures of action potential waveform isolation applicable to any dataset that have an intuitive, universal interpretation, that are not dependent on the methods or choice of parameters for single-unit isolation, and that have been validated using a dataset of simultaneous intracellular and extracellular neuronal recordings from Sprague Dawley rats.     

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.     

Long-term cultivation of multipotential neural stem cells from adult rat subependyma

Cultivation of adult rat neural stem cells (RNSCs) from the ventricular subependyma has been reported to be more difficult than growth of mouse neural stem cells. This is unfortunate, because rats provide useful models of brain function and disease, and implantation of RNSCs in these models could provide critical information on allograft behavior. Growing the cells in an appropriate medium (NS-A+B27 supplement), plating at sufficient densities (>5 cells per mm²), and minimizing opportunities for detachment from the substratum made it possible to isolate and cultivate these cells for over 6 months for >50 passages with no apparent change in phenotype. Single clones could be expanded indefinitely and differentiated to form astrocytes, oligodendrocytes, and neurons, demonstrating that the cultures did indeed contain neural stem cells. The cells had a much shorter cell cycle time (13 h) than doubling time (35 h), suggesting that these cells produce post-mitotic cells in approximately two of three divisions, thus making expansion difficult. The optimization of methods to grow adult RNSCs and identification of characteristics that limit their growth should prove useful in increasing the use of RNSCs for studies of their potential role in brain health and disease.     

Repetition rates of specific interval patterns in single spike train reflect excitation level of specific receptor types, shown by high-speed favored-pattern detection method

Interval patterns in single spike train, e.g. "favored patterns (FPs, the FP is a sequence of successive intervals of action potentials that occur more often than what is reasonably expected at random.)", may represent neural codes containing information. The present study developed a "high-speed FP-detection method" which could qualitatively and quantitatively analyze FPs. By using this method, single spike trains of nucleus paraventricularis (NPV) and rostral ventrolateral medulla (RVL) having different firing patterns, being involved in regulation of arterial pressure, and controlled by different transmitters, were chosen for analysis. Results: (1) Corticotropin releasing factor, substance P and agonists of alpha-, beta- and M-receptor microinjected into these brain areas, respectively, induced dominant change of specific FP. Repetition rates of specific FPs reflect excitation level of specific receptor types. It shows that chemical codes (different transmitters with their receptor types or subtypes) are transformed into electrical codes (different FPs). (2) When alpha-, beta- and M-receptors of RVL neurons were activated simultaneously by intrinsic excitatory transmitters released due to activation of input pathway, only repetition rate of the specific FP that represented the predominant activity of the receptor type (alpha-adrenergic receptor) markedly increased. The activities of other receptor types (beta- and M-receptors) were masked. (3) Intrinsic inhibitory transmitters (GABA, beta-endorphin) in the RVL all decreased specific FP repetition rate of dominant receptor type. These results may provide a new way to further explore how information in the CNS is conveyed and processed.