多通道神经信号分析方法及其应用

在神经科学研究中,多通道记录方法被普遍应用在对神经元群体活动特性的研究中.通过分析多个神经元的活动,可以了解神经系统协同编码外界信息的规则以及大脑实现各项功能的机制.为了挖掘出多通道神经信号中携带的信息及其潜在的相关性,需要合适的计算方法辅助对神经元放电活动进行解码.本文回顾了多通道神经信号分析中的一些常见方法,以及它...     

Spatial learning and action planning in a prefrontal cortical network model

The interplay between hippocampus and prefrontal cortex (PFC) is fundamental to spatial cognition. Complementing hippocampal place coding, prefrontal representations provide more abstract and hierarchically organized memories suitable for decision making. We model a prefrontal network mediating distributed information processing for spatial learning and action planning. Specific connectivity and synaptic adaptation principles shape the recurrent dynamics of the network arranged in cortical minicolumns. We show how the PFC columnar organization is suitable for learning sparse topological-metrical representations from redundant hippocampal inputs. The recurrent nature of the network supports multilevel spatial processing, allowing structural features of the environment to be encoded. An activation diffusion mechanism spreads the neural activity through the column population leading to trajectory planning. The model provides a functional framework for interpreting the activity of PFC neurons recorded during navigation tasks. We illustrate the link from single unit activity to behavioral responses. The results suggest plausible neural mechanisms subserving the cognitive "insight" capability originally attributed to rodents by Tolman & Honzik. Our time course analysis of neural responses shows how the interaction between hippocampus and PFC can yield the encoding of manifold information pertinent to spatial planning, including prospective coding and distance-to-goal correlates.     

Spatiotemporal burst coding for extracting features of spatiotemporally varying stimuli

Encoding features of spatiotemporally varying stimuli is quite important for understanding the neural mechanisms of various sensory coding. Temporal coding can encode features of time-varying stimulus, and population coding with temporal coding is adequate for encoding spatiotemporal correlation of stimulus features into spatiotemporal activity of neurons. However, little is known about how spatiotemporal features of stimulus are encoded by spatiotemporal property of neural activity. To address this issue, we propose here a population coding with burst spikes, called here spatiotemporal burst (STB) coding. In STB coding, the temporal variation of stimuli is encoded by the precise onset timing of burst spike, and the spatiotemporal correlation of stimuli is emphasized by one specific aspect of burst firing, or spike packet followed by silent interval. To show concretely the role of STB coding, we study the electrosensory system of a weakly electric fish. Weakly electric fish must perceive the information about an object nearby by analyzing spatiotemporal modulations of electric field around it. On the basis of well-characterized circuitry, we constructed a neural network model of the electrosensory system. Here we show that STB coding encodes well the information of object distance and size by extracting the spatiotemporal correlation of the distorted electric field. The burst activity of electrosensory neurons is also affected by feedback signals through synaptic plasticity. We show that the control of burst activity caused by the synaptic plasticity leads to extracting the stimulus features depending on the stimulus context. Our results suggest that sensory systems use burst spikes as a unit of sensory coding in order to extract spatiotemporal features of stimuli from spatially distributed stimuli.     

Information coding by ensembles of resonant neurons

In the present paper, we propose a novel neural procedure for signal processing and coding based on the subthreshold oscillations and resonance of the neural membrane potential that could be used by real neurons to perform frequency spectra analysis and information coding of incoming signals. Taking into account the biophysical properties of the neural membranes, we note that the subthreshold resonant behaviour they exhibit can be used to analyse incoming signals and represent them in the frequency domain. We study the reliability of the representation of signals depending on the biophysical parameters of the neurons, the fault-tolerance of this coding scheme and its robustness against noise and in the presence of spikes. The principal characteristics of our system are the use of the physical phenomenon of neural resonance (rarely considered in the literature for signal coding); it fits well with the biophysical parameters of most neurons that exhibit subthreshold oscillations; it is compatible with experimental data; and it can be easily integrated in a more general model of information processing and coding that includes communication between neurons based on spikes.     

Neural mass differences in gustation

Correlational techniques have been developed in the chemicalsenses for the measurement of neural and psychophysical information;in addition to correlations per se, these have included multidimensionalscaling and cluster analysis, and also may include factor analysis.Although very powerful, these methods are insensitive to numbersof neurons involved and amounts of evoked activity: particularlyat low levels of activity, low reliability may be interpretedas information by these methods. A variant of correlations isdiscussed as a measure of neural information, which includesnumbers of neurons and amounts of evoked activity in its computation,and is not unduly influenced by poor reliability at low responselevels. As a neural metric, this ‘neural mass differences’method is equally applicable to labeled-line or across-fiber-pattemmodels of neural coding. The data analyzed were the responsesof 40 single taste neurons in the parabrachial nuclei of thehamster to 32 taste stimuli.     

Energy coding in neural network with inhibitory neurons

This paper aimed at assessing and comparing the effects of the inhibitory neurons in the neural network on the neural energy distribution, and the network activities in the absence of the inhibitory neurons to understand the nature of neural energy distribution and neural energy coding. Stimulus, synchronous oscillation has significant difference between neural networks with and without inhibitory neurons, and this difference can be quantitatively evaluated by the characteristic energy distribution. In addition, the synchronous oscillation difference of the neural activity can be quantitatively described by change of the energy distribution if the network parameters are gradually adjusted. Compared with traditional method of correlation coefficient analysis, the quantitative indicators based on nervous energy distribution characteristics are more effective in reflecting the dynamic features of the neural network activities. Meanwhile, this neural coding method from a global perspective of neural activity effectively avoids the current defects of neural encoding and decoding theory and enormous difficulties encountered. Our studies have shown that neural energy coding is a new coding theory with high efficiency and great potential.     

Auto-association by multilayer perceptrons and singular value decomposition

The multilayer perceptron, when working in auto-association mode, is sometimes considered as an interesting candidate to perform data compression or dimensionality reduction of the feature space in information processing applications. The present paper shows that, for auto-association, the nonlinearities of the hidden units are useless and that the optimal parameter values can be derived directly by purely linear techniques relying on singular value decomposition and low rank matrix approximation, similar in spirit to the well-known Karhunen-Loève transform. This approach appears thus as an efficient alternative to the general error back-propagation algorithm commonly used for training multilayer perceptrons. Moreover, it also gives a clear interpretation of the rôle of the different parameters.     

Sparse Coding Models Can Exhibit Decreasing Sparseness while Learning Sparse Codes for Natural Images

The sparse coding hypothesis has enjoyed much success in predicting response properties of simple cells in primary visual cortex (V1) based solely on the statistics of natural scenes. In typical sparse coding models, model neuron activities and receptive fields are optimized to accurately represent input stimuli using the least amount of neural activity. As these networks develop to represent a given class of stimulus, the receptive fields are refined so that they capture the most important stimulus features. Intuitively, this is expected to result in sparser network activity over time. Recent experiments, however, show that stimulus-evoked activity in ferret V1 becomes less sparse during development, presenting an apparent challenge to the sparse coding hypothesis. Here we demonstrate that some sparse coding models, such as those employing homeostatic mechanisms on neural firing rates, can exhibit decreasing sparseness during learning, while still achieving good agreement with mature V1 receptive field shapes and a reasonably sparse mature network state. We conclude that observed developmental trends do not rule out sparseness as a principle of neural coding per se: a mature network can perform sparse coding even if sparseness decreases somewhat during development. To make comparisons between model and physiological receptive fields, we introduce a new nonparametric method for comparing receptive field shapes using image registration techniques.     

Coding processes involved in the cortical representation of complex tactile stimuli

To understand how information is coded in the primary somatosensory cortex (S1) we need to decipher the relationship between neural activity and tactile stimuli. Such a relationship can be formally measured by mutual information. The present study was designed to determine how S1 neuronal populations code for the multidimensional kinetic features (i.e. random, time-varying patterns of force) of complex tactile stimuli, applied at different locations of the rat forepaw. More precisely, the stimulus localization and feature extraction were analyzed as two independent processes, using both rate coding and temporal coding strategies. To model the process of stimulus kinetic feature extraction, multidimensional stimuli were projected onto lower dimensional subspace and then clustered according to their similarity. Different combinations of stimuli clustering were applied to differentiate each stimulus identification process. Information analyses show that both processes are synergistic, this synergy is enhanced within the temporal coding framework. The stimulus localization process is faster than the stimulus feature extraction process. The latter provides more information quantity with rate coding strategy, whereas the localization process maximizes the mutual information within the temporal coding framework. Therefore, combining mutual information analysis with robust clustering of complex stimuli provides a framework to study neural coding mechanisms related to complex stimuli discrimination.     

A model for integrating elementary neural functions into delayed-response behavior

It is well established that various cortical regions can implement a wide array of neural processes, yet the mechanisms which integrate these processes into behavior-producing, brain-scale activity remain elusive. We propose that an important role in this respect might be played by executive structures controlling the traffic of information between the cortical regions involved. To illustrate this hypothesis, we present a neural network model comprising a set of interconnected structures harboring stimulus-related activity (visual representation, working memory, and planning), and a group of executive units with task-related activity patterns that manage the information flowing between them. The resulting dynamics allows the network to perform the dual task of either retaining an image during a delay (delayed-matching to sample task), or recalling from this image another one that has been associated with it during training (delayed-pair association task). The model reproduces behavioral and electrophysiological data gathered on the inferior temporal and prefrontal cortices of primates performing these same tasks. It also makes predictions on how neural activity coding for the recall of the image associated with the sample emerges and becomes prospective during the training phase. The network dynamics proves to be very stable against perturbations, and it exhibits signs of scale-invariant organization and cooperativity. The present network represents a possible neural implementation for active, top-down, prospective memory retrieval in primates. The model suggests that brain activity leading to performance of cognitive tasks might be organized in modular fashion, simple neural functions becoming integrated into more complex behavior by executive structures harbored in prefrontal cortex and/or basal ganglia.     

Constrained brain volume in an efficient coding model explains the fraction of excitatory and inhibitory neurons in sensory cortices

The number of neurons in mammalian cortex varies by multiple orders of magnitude across different species. In contrast, the ratio of excitatory to inhibitory neurons (E:I ratio) varies in a much smaller range, from 3:1 to 9:1 and remains roughly constant for different sensory areas within a species. Despite this structure being important for understanding the function of neural circuits, the reason for this consistency is not yet understood. While recent models of vision based on the efficient coding hypothesis show that increasing the number of both excitatory and inhibitory cells improves stimulus representation, the two cannot increase simultaneously due to constraints on brain volume. In this work, we implement an efficient coding model of vision under a constraint on the volume (using number of neurons as a surrogate) while varying the E:I ratio. We show that the performance of the model is optimal at biologically observed E:I ratios under several metrics. We argue that this happens due to trade-offs between the computational accuracy and the representation capacity for natural stimuli. Further, we make experimentally testable predictions that 1) the optimal E:I ratio should be higher for species with a higher sparsity in the neural activity and 2) the character of inhibitory synaptic distributions and firing rates should change depending on E:I ratio. Our findings, which are supported by our new preliminary analyses of publicly available data, provide the first quantitative and testable hypothesis based on optimal coding models for the distribution of excitatory and inhibitory neural types in the mammalian sensory cortices.     

Detailed and abstract phase-locked attractor network models of early olfactory systems

Across species, primary olfactory centers show similarities both in their cellular organization and their types of olfactory information coding. In this article, we consider an excitatory-inhibitory spiking neural network as a model of early olfactory systems (antennal lobe for insects, olfactory bulb for vertebrates). In line with experimental results, we show that, in our network, odor-like stimuli evoke synchronization of excitatory cells, phase-locked to the oscillations of the local field potential. As revealed by a mathematical analysis, the phase-locking probability of excitatory cells is given by an inverted-U function and the firing probability of inhibitory cells is well described by a sigmoid function. These neural response functions are used to reduce the spiking model to a more abstract model with discrete-time dynamics (oscillatory cycles) and binary-state neurons (phase-locked or not). An iterative map, built for explaining the dynamics of the binary model, reveals that it converges to fixed point attractors similar to those obtained with the spiking model. This result is consistent with odor-specific attractors found in recent experimental studies. It also provides insights for designing bio-inspired olfactory associative memories applicable for data analysis in electronic noses.     

Neural computations underlying the exertion of force: a model

We have developed a model that simulates possible mechanisms by which supraspinal neuronal signals coding forces could converge in the spinal cord and provide an ongoing integrated signal to the motoneuronal pools whose activation results in the exertion of force. The model consists of a three-layered neural network connected to a two-joint-six-muscle model of the arm. The network layers represent supraspinal populations, spinal cord interneurons, and motoneuronal pools. We propose an approach to train the network so that, after the synaptic connections between the layers are adjusted, the performance of the model is consistent with experimental data obtained on different organisms using different experimental paradigms: the stiffness characteristics of human arm; the structure of force fields generated by the stimulation of the frog's spinal cord; and a correlation between motor cortical activity and force exerted by monkey against an immovable object. The model predicts a specific pattern of connections between supraspinal populations coding forces and spinal cord interneurons: the weight of connection should be correlated with directional preference of interconnected units. Finally, our simulations demonstrate that the force generated by the sum of neural signals can be nearly equal to the vector sum of forces generated by each signal independently, in spite of the complex nonlinearities intervening between supraspinal commands and forces exerted by the arm in response to these commands.     

Slow Feature Analysis on Retinal Waves Leads to V1 Complex Cells

The developing visual system of many mammalian species is partially structured and organized even before the onset of vision. Spontaneous neural activity, which spreads in waves across the retina, has been suggested to play a major role in these prenatal structuring processes. Recently, it has been shown that when employing an efficient coding strategy, such as sparse coding, these retinal activity patterns lead to basis functions that resemble optimal stimuli of simple cells in primary visual cortex (V1). Here we present the results of applying a coding strategy that optimizes for temporal slowness, namely Slow Feature Analysis (SFA), to a biologically plausible model of retinal waves. Previously, SFA has been successfully applied to model parts of the visual system, most notably in reproducing a rich set of complex-cell features by training SFA with quasi-natural image sequences. In the present work, we obtain SFA units that share a number of properties with cortical complex-cells by training on simulated retinal waves. The emergence of two distinct properties of the SFA units (phase invariance and orientation tuning) is thoroughly investigated via control experiments and mathematical analysis of the input-output functions found by SFA. The results support the idea that retinal waves share relevant temporal and spatial properties with natural visual input. Hence, retinal waves seem suitable training stimuli to learn invariances and thereby shape the developing early visual system such that it is best prepared for coding input from the natural world.     

Information maximization for exploring neural coding in hippocampus and lateral septum

A central problem in neural coding is to understand what are the features of the stimulus that are encoded by the neural activity. Assuming that neuronal coding is optimized for information transmission, we can use mutual information maximization for extracting the relevant features encoded in certain activity patterns. We show that this algorithm can be successfully applied to the study of different encoding strategies for location and direction of movement in hippocampal and lateral septal cells. Using this approach, we find that in lateral septum, a significant amount of information about location can be encoded in patterns that are not place-fields.     

Dynamic characteristics of the auditory cortex of guinea pigs observed with multichannel optical recording

The spatiotemporal characteristics of neural activity in the guinea pig auditory cortex are investigated to determine their importance in neural processing and coding of the complex sounds. A multi-channel optical recording system has been developed for observing the cortical field of the mammalian brain in vivo. Using the voltage-sensitive dye: RH795, optical imaging was used to visualize neural activity in the guinea pig auditory cortex. Experimental results reveal a boomerang-shaped pattern of movement of activated neural cell regions for the evoked response to click as complex sounds. Parallel and sequential neural processing structure was observed. Although the exact frequency selectivities of single cells and tonotopical organization observed using microelectrode were not visible, the similar feature to the microelectrode evidences was imaged by extracting the strongly response field from the optical data.     

Modeling the dishabituation hierarchy: The role of the primordial hippocampus

We present a neural model for the organization and neural dynamics of the medial pallium, the toad's homolog of mammalian hippocampus. A neural mechanism, called cumulative shrinking, is proposed for mapping temporal responses from the anterior thalamus into a form of population coding referenced by spatial positions. Synaptic plasticity is modeled as an interaction of two dynamic processes which simulates acquisition and both short-term and long-term forgetting. The structure of the medial pallium model plus the plasticity model allows us to provide an account of the neural mechanisms of habituation and dishabituation. Computer simulations demonstrate a remarkable match between the model performance and the original experimental data on which the dishabituation hierarchy was based. A set of model predictions is presented, concerning mechanisms of habituation and cellular organization of the medial palliumThe research described in this paper was supported in part by grant no. 1RO1 NS 24926 from the National Institutes of Health (M.A.A., Principal Investigator)     

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.     

The systems analysis approach to mechanosensory coding

An important problem in neuroscience is to obtain quantitative knowledge of how information is represented, or encoded, in the signals that nerve cells process and transmit. Sensory receptors have provided important models for the study of neural coding because their inputs can often be relatively easily controlled and measured, while the resultant activity is recorded. A variety of engineering concepts have been successfully applied to physiological sciences, particularly those related to control of dynamic systems. Linear systems analysis was one of the earliest methods used to probe sensory coding, and measurements such as step responses and frequency responses have become standard tools for describing sensory functions. Modern systems analysis has evolved to provide accurate and efficient linear identification of encoding in sensory receptors that use either graded potentials or action potentials. It has also led to nonlinear systems analysis, the creation of parametric nonlinear models, and measures of information coding by sensory neurons. These methods promise to provide important new knowledge about sensory systems in the future, especially when complemented with parallel biophysical and molecular studies of sensory neurons. Mechanoreceptors provided some of the earliest preparations for the investigation of neural coding, and both the linear and nonlinear properties of wide variety of vertebrate and invertebrate mechanoreceptors continue to be explored. This article is part of a special issue on Neuronal Dynamics of Sensory Coding.     

Visual perception of ambiguous figures: synchronization based neural models

We develop and study two neural network models of perceptual alternations. Both models have a star-like architecture of connections with a central element connected to a set of peripheral elements. A particular perception is simulated in terms of partial synchronization between the central element and some sub-group of peripheral elements. The first model is constructed from phase oscillators and the mechanism of perceptual alternations is based on chaotic intermittency under fixed parameter values. Similar to experimental evidence, the distribution of times between perceptual alternations is represented by the gamma distribution. The second model is built of spiking neurons of the Hodgkin–Huxley type. The mechanism of perceptual alternations is based on plasticity of inhibitory synapses which increases the inhibition from the central unit to the neural assembly representing the current percept. As a result another perception is formed. Simulations show that the second model is in good agreement with behavioural data on switching times between percepts of ambiguous figures and with experimental results on binocular rivalry of two and four percepts. This article is part of a special issue on Neuronal Dynamics of Sensory Coding. This special issue is in honour of Professor Pepe Segundo who is one of the pioneers in the study of neural coding. Pepe has been an active participant in many Neural Coding Workshops sharing his great knowledge and experience of research in this field. I (R. Borisyuk) was very happy to meet Pepe for the first time in Prague when attending the first Neural Coding Workshop in 1995. From that time we regularly met at Neural Coding Workshops and these meetings have always been very stimulating and fruitful for my research. Remarkably, the first paper I studied at the beginning of my scientific career was a seminal paper by Moore et al. (1970). For me, this paper provided a great opportunity to learn the basic statistical techniques for the analysis of multiple spike trains and neural coding. According to the Institute of Scientific Information, this paper has been cited 380 times! This exciting paper has inspired my research into the synaptic and functional connectivity of neural circuits derived from spike-train recordings (Borisyuk et al. 1985; Stuart et al. 2005) and guided my search for new ideas on neural coding.