Multidimensional encoding strategy of spiking neurons.
ABSTRACT Neural responses in sensory systems are typically triggered by a multitude of stimulus features. Using information theory, we study the encoding accuracy of a population of stochastically spiking neurons characterized by different tuning widths for the different features. The optimal encoding strategy for representing one feature most accurately consists of narrow tuning in the dimension to be encoded, to increase the single-neuron Fisher information, and broad tuning in all other dimensions, to increase the number of active neurons. Extremely narrow tuning without sufficient receptive field overlap will severely worsen the coding. This implies the existence of an optimal tuning width for the feature to be encoded. Empirically, only a subset of all stimulus features will normally be accessible. In this case, relative encoding errors can be calculated that yield a criterion for the function of a neural population based on the measured tuning curves.
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ABSTRACT: Population coding is a coding scheme which is ubiquitous in neural systems, and is also of more general use in coding stimuli, for example in vision problems. A population of responses to a stimulus can be used to represent not only the value of some variable in the environment, but a full probability distribution for that variable. The information is held in a distributed and encoded form, which may in some situations be more robust to noise and failures than conventional representations. Gabor filters are a popular choice for detecting edges in the visual field for several reasons. They are easily tuned for a variety of edge widths and orientations, and are considered a close model of the edge filters in the human visual system. In this paper, we consider population codes of Gabor filters with different orientations. A probabilistic model of Gabor filter responses is presented. Based on the analytically derived orientation tuning function and a parametric mixture model of the filter responses in the presence of local edge structure with single or multiple orientations a probability density function (pdf) of the local orientation in any point (x, y) can be extracted through a parameter estimation procedure. The resulting pdf of the local contour orientation captures not only angular information at edges, corners or T-junctions but also describes the certainty of the measurement which can be characterized in terms of the entropy of the individual mixture components.IEEE Transactions on Neural Networks 02/2003; 14(4):794-803. · 2.95 Impact Factor
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ABSTRACT: For a network of spiking neurons that encodes information in the timing of individual spike-times, we derive a supervised learning rule, SpikeProp, akin to traditional error-backpropagation and show how to overcome the discontinuities introduced by thresholding. With this algorithm, we demonstrate how networks of spiking neurons with biologically reasonable action potentials can perform complex non-linear classification in fast temporal coding just as well as rate-coded networks. We perform experiments for the classical XOR-problem, when posed in a temporal setting, as well as for a number of other benchmark datasets. Comparing the (implicit) number of spiking neurons required for the encoding of the interpolated XOR problem, it is demonstrated that temporal coding requires significantly less neurons than instantaneous rate-coding. 2000 Mathematics Subject Classification: 82C32, 68T05, 68T10, 68T30, 92B20. 1998 ACM Computing Classification System: C.1.3, F.1.1, I.2.6, I.5.1. Keywords...02/2001;