Results 111 to 120 of about 36,106 (159)
Spiking neural models for decision-making tasks with learning. [PDF]
J Math BiolJaffard S +3 more
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Functional implications of atypical action potential generation in the (patho)physiological brain: from developmental program to glioma. [PDF]
Front Neural CircuitsTong T, Korshøj AR, Hou WH.
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Physical Review E, 2001
We propose two control strategies for achieving desired firing patterns in a physiologically realistic model neuron. The techniques are powerful, efficient, and robust, and we have applied them successfully to obtain a range of targeted spiking behaviors.
S, Sinha, W L, Ditto
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We propose two control strategies for achieving desired firing patterns in a physiologically realistic model neuron. The techniques are powerful, efficient, and robust, and we have applied them successfully to obtain a range of targeted spiking behaviors.
S, Sinha, W L, Ditto
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2009 IEEE International Symposium on Information Theory, 2009
The information transfer through a single neuron is a fundamental information processing in the brain. This paper studies the information-theoretic capacity of a single neuron by treating the neuron as a communication channel. Two different models are considered.
Shiro Ikeda, Jonathan H. Manton
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The information transfer through a single neuron is a fundamental information processing in the brain. This paper studies the information-theoretic capacity of a single neuron by treating the neuron as a communication channel. Two different models are considered.
Shiro Ikeda, Jonathan H. Manton
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Decoding spikes in a spiking neuronal network
Journal of Physics A: Mathematical and General, 2004Summary: We investigate how to reliably decode the input information from the output of a spiking neuronal network. A maximum likelihood estimator of the input signal, together with its Fisher information, is rigorously calculated. The advantage of the maximum likelihood estimation over the 'brute-force rate coding' estimate is clearly demonstrated. It
Feng, Jianfeng, Ding, Mingzhou
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A Spiking Neuron as Information Bottleneck
Neural Computation, 2010Neurons receive thousands of presynaptic input spike trains while emitting a single output spike train. This drastic dimensionality reduction suggests considering a neuron as a bottleneck for information transmission. Extending recent results, we propose a simple learning rule for the weights of spiking neurons derived from the information bottleneck (
Buesing L., Maass W.
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To spike or not to spike: A probabilistic spiking neuron model
Neural Networks, 2010Spiking neural networks (SNN) are promising artificial neural network (ANN) models as they utilise information representation as trains of spikes, that adds new dimensions of time, frequency and phase to the structure and the functionality of ANN.
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Approximation of Spike-trains by Digital Spiking Neuron
2007 International Joint Conference on Neural Networks, 2007A digital spiking neuron (DSN) consists of shift registers and can generate spike-trains with various patterns of inter-spike intervals. In this paper we present a learning algorithm for the DSN in order to approximate given spike-trains. We study a case where a student DSN accepts a spike-train from a teacher DSN.
Hiroyuki Torikai +2 more
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