Titlebook: Artificial Neural Networks — ICANN ’97; 7th International Co Wulfram Gerstner,Alain Germond,Jean-Daniel Nicoud Conference proceedings 1997

irreducible

变形

Correlation coding in stochastic neural networks,hastic nature of the input signal, but there is nevertheless a strong central peak in the correlation of the output spike trains. The experimental data and this simple model clearly demonstrate how even a noisy-looking spike train can convey basic information about a sensory stimulus in the relative spike timing between neurons.

领袖气质

https://doi.org/10.1007/978-1-4613-0079-3ecific locations. The degree to which the specific fragment extracted by each synapse, will influence the spiking activity of the neuron, depends the ongoing integration of input from other presynaptic neurons. It is therefore proposed that differential synaptic transmission enables the neocortex to

思乡病

The information content of action potential trains a synaptic basis,ecific locations. The degree to which the specific fragment extracted by each synapse, will influence the spiking activity of the neuron, depends the ongoing integration of input from other presynaptic neurons. It is therefore proposed that differential synaptic transmission enables the neocortex to

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Axillary

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acolyte

Cross-correlations in sparsely connected recurrent networks of spiking neurons,es, and in particular cross-Correlations (CC) between spike times of pairs of neurons using both numerical simulations and a recent theory. CCs exhibit damped oscillations with a frequency which depends on synaptic time constants. Individual CCs are shown to depend weakly on synaptic connectivity. T

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A comparative study of pattern detection algorithm and dynamical system approach using simulated spc attractors and Poisson processes. We show that both algorithms are able to detect a deterministic activity in the chaotic spike trains and they are tolerant to the presence of noise in input data. A method for noise filtering in input data series is proposed and its application is demonstrated for

arousal

Spatio-temporal pattern recognition with neural networks: Application to speech,l pattern recognition framework using neural networks in relation with the understanding of the peripheral auditory system. We propose a short-time structure representation of speech for speech analysis and recognition. We give examples of neural networks architecture and applications that are desig