Recent studies of biological auditory processing have revealed that sophisticated spectrotemporal analyses are performed by central auditory systems of various animals. The analysis is typically well matched with the ...
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Recent studies of biological auditory processing have revealed that sophisticated spectrotemporal analyses are performed by central auditory systems of various animals. The analysis is typically well matched with the statistics of relevant natural sounds, suggesting that it produces an optimal representation of the animal's acoustic biotope. We address this topic using simulated neurons that learn an optimal representation of a speech corpus. As input, the neurons receive a spectrographic representation of sound produced by a peripheral auditory model. The output representation is deemed optimal when the responses of the neurons are maximally sparse. Following optimization, the simulated neurons are similar to real neurons in many respects. Most notably, a given neuron only analyzes the input over a localized region of time and frequency. In addition, multiple subregions either excite or inhibit the neuron, together producing selectivity to spectral and temporal modulation patterns. This suggests that the brain's solution is particularly well suited for coding natural sound;therefore, it may prove useful in the design of new computational methods for processing speech.
Neural activity manifests itself as complex spatiotemporal activation patterns in cell populations. Even for local neural circuits, a comprehensive description of network activity has been impossible so far. Here we d...
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Neural activity manifests itself as complex spatiotemporal activation patterns in cell populations. Even for local neural circuits, a comprehensive description of network activity has been impossible so far. Here we demonstrate that two-photon calcium imaging of bulk-labeled tissue permits dissection of local input and output activities in rat neocortex in vivo. Besides astroglial and neuronal calcium transients, we found spontaneous calcium signals in the neuropil that were tightly correlated to the electrocorticogram. This optical encephalogram (OEG) is shown to represent bulk calcium signals in axonal structures, thus providing a measure of local input activity. Simultaneously, output activity in local neuronal populations could be derived from action potential-evoked calcium transients with single-spike resolution. By using these OEG and spike activity measures, we characterized spontaneous activity during cortical Up states. We found that (i) spiking activity is sparse (< 0.1 Hz);(it) on average, only approximate to 10% of neurons are active during each Up state;(iii) this active subpopulation constantly changes with time;and (iv) spiking activity across the population is evenly distributed throughout the Up-state duration. Furthermore, the number of active neurons directly depended on the amplitude of the OEG, thus optically revealing an input-output function for the local network. We conclude that spontaneous activity in the neocortex is sparse and heterogeneously distributed in space and time across the neuronal population. The dissection of the various signal components in bulk-loaded tissue as demonstrated here will enable further studies of signal flow through cortical networks.
An important approach in visual neuroscience considers how the function of the early visual system relates to the statistics of its natural input. Previous work has shown how the classical receptive fields and the org...
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An important approach in visual neuroscience considers how the function of the early visual system relates to the statistics of its natural input. Previous work has shown how the classical receptive fields and the organization (topography) of the primary visual cortex can be viewed as efficient coding of natural images. Here, we extend the framework by considering how the responses of complex cells could be efficiently coded by a higher-order neural layer. This leads to the sparse coding of contours in natural images, and can explain certain extra-classical properties of receptive fields. (C) 2002 Elsevier Science B.V. All rights reserved.
Probability distributions of macaque complex cell responses to a large set of images were determined. Measures of selectivity were based on the overall shape of the response probability distribution, as quantified by ...
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Probability distributions of macaque complex cell responses to a large set of images were determined. Measures of selectivity were based on the overall shape of the response probability distribution, as quantified by either kurtosis or entropy. We call this non-parametric selectivity, in contrast to parametric selectivity, which measures tuning curve bandwidths. To examine how receptive field properties affected non-parametric selectivity, two models of complex cells were created. One was a standard Gabor energy model, and the other a slight variant constructed from a Gabor function and its Hilbert transform. Functionally, these models differed primarily in the size of their DC responses. The Hilbert model produced higher selectivities than the Gabor model, with the two models bracketing the data from above and below. Thus we see that tiny changes in the receptive field profiles can lead to major changes in selectivity. While selectivity looks at the response distribution of a single neuron across a set of stimuli, sparseness looks at the response distribution of a population of neurons to a single stimulus. In the model, we found that on average the sparseness of a population was equal to the selectivity of cells comprising that population, a property we call ergodicity. We raise the possibility that high sparseness is the result of distortions in the shape of response distributions caused by non-linear, information-losing transforms, unrelated to information theoretic issues of efficient coding. (c) 2004 Elsevier Ltd. All rights reserved.
We investigate storage capacity of two types of fully connected layered neural networks with sparse coding when binary patterns are embedded into the networks by a Hebbian learning rule. One of them is a layered netwo...
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We investigate storage capacity of two types of fully connected layered neural networks with sparse coding when binary patterns are embedded into the networks by a Hebbian learning rule. One of them is a layered network. in which a transfer function of even layers is different from that of odd layers. The other is a layered network with intra-layer connections, in which the transfer function of inter-layer is different from that of intra-layer, and inter-layered neurons and intra-layered neurons are updated alternately, We derive recursion relations Cor order parameters by means of the signal-to-noise ratio method, and then apply the self-control threshold method proposed by Dominguez and Bolle to both layered networks with monotonic transfer functions. We find that a critical value alpha(C), of storage capacity is about 0. 11\a In a\ (a much less than 1 ) Cor both layered networks, where a is a neuronal activity. It turns out that the basin of attraction is larger for both layered networks when the self-control threshold method is applied. (C) 2002 Elsevier Science B.V. All rights reserved.
Recent algorithms for sparse coding and independent component analysis (ICA) have demonstrated how localized features can be learned from natural images. However, these approaches do not take image transformations int...
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ISBN:
(纸本)0262025507
Recent algorithms for sparse coding and independent component analysis (ICA) have demonstrated how localized features can be learned from natural images. However, these approaches do not take image transformations into account. As a result, they produce image codes that are redundant because the same feature is learned at multiple locations. We describe an algorithm for sparse coding based on a bilinear generative model of images. By explicitly modeling the interaction between image features and their transformations, the bilinear approach helps reduce redundancy in the image code and provides a basis for transformation-invariant vision. We present results demonstrating bilinear sparse coding of natural images. We also explore an extension of the model that can capture spatial relationships between the independent features of an object, thereby providing a new framework for parts-based object recognition.
Linear expansions of images find many applications in image processing and computer vision. Over-complete expansions are often desirable, as they are better models of the image-generation process. Such expansions lead...
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Linear expansions of images find many applications in image processing and computer vision. Over-complete expansions are often desirable, as they are better models of the image-generation process. Such expansions lead to the use of sparse codes. However, minimizing the number of non-zero coefficients of linear expansions is an unsolved problem. In this article, a generative-model framework is used to analyze the requirements, the difficulty, and current approaches to sparse image coding.
We investigated the properties of mixed states in a sparsely encoded associative memory model with a structural learning method. When mixed states are made of s memory patterns, s types of mixed states, which become e...
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We investigated the properties of mixed states in a sparsely encoded associative memory model with a structural learning method. When mixed states are made of s memory patterns, s types of mixed states, which become equilibrium states of the model, can be generated. To investigate the properties of s types of the mixed states, we analyzed them using the statistical mechanical method. We found that the storage capacity of the memory pattern and the storage capacity of only a particular mixed state diverge at the sparse limit. We also found that the threshold value needed to recall the memory pattern is nearly equal to the threshold value needed to recall the particular mixed state. This means that the memory pattern and the particular mixed state can be made to easily coexist at the sparse limit. The properties of the model obtained by the analysis are also useful for constructing a transform-invariant recognition model. (C) 2003 Elsevier Ltd. All rights reserved.
Current models of cortical computation are based on analog quantities instead of single spikes. This paper extends the predictive coding model (Nature Neurosci. 2(1) (1999) 79) to the level of neural signaling. Neuron...
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Current models of cortical computation are based on analog quantities instead of single spikes. This paper extends the predictive coding model (Nature Neurosci. 2(1) (1999) 79) to the level of neural signaling. Neurons in our model use a mixed strategy to transmit information. Spikes are not only messages of computation, but also carriers of information with analog quantities encoded in their phases. Computation is shared among cells both in time and in space, such that information is signaled probabilistically in a distributed synchronous fashion. Contrary to "noise other than signal" interpretation of irregularity of neural signaling, our model proposes a computational role of such variability. (C) 2004 Published by Elsevier B.V.
We demonstrate that spiking neural networks encoding information in the timing of single spikes are capable of computing and learning clusters from realistic data. We show how a spiking neural network based on spike-t...
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We demonstrate that spiking neural networks encoding information in the timing of single spikes are capable of computing and learning clusters from realistic data. We show how a spiking neural network based on spike-time coding and Hebbian learning can successfully perform unsupervised clustering on real-world data, and we demonstrate how temporal synchrony in a multilayer network can induce hierarchical clustering. We develop a temporal encoding of continuously valued data to obtain adjustable clustering capacity and precision with an efficient use of neurons: input variables are encoded in a population code by neurons with graded and overlapping sensitivity profiles. We also discuss methods for enhancing scale-sensitivity of the network and show how the induced synchronization of neurons within early RBF layers allows for the subsequent detection of complex clusters.
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