Results 201 to 210 of about 40,549 (223)
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Bayesian Spiking Neurons II: Learning
Neural Computation, 2008In the companion letter in this issue (“Bayesian Spiking Neurons I: Inference”), we showed that the dynamics of spiking neurons can be interpreted as a form of Bayesian integration, accumulating evidence over time about events in the external world or the body.
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Orientation-selective aVLSI spiking neurons
Neural Networks, 2001We describe a programmable multi-chip VLSI neuronal system that can be used for exploring spike-based information processing models. The system consists of a silicon retina, a PIC microcontroller, and a transceiver chip whose integrate-and-fire neurons are connected in a soft winner-take-all architecture.
Liu, S. +5 more
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Bayesian Spiking Neurons I: Inference
Neural Computation, 2008We show that the dynamics of spiking neurons can be interpreted as a form of Bayesian inference in time. Neurons that optimally integrate evidence about events in the external world exhibit properties similar to leaky integrate-and-fire neurons with spike-dependent adaptation and maximally respond to fluctuations of their input.
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Spiking Neural Networks for Cortical Neuronal Spike Train Decoding
Neural Computation, 2010Recent investigation of cortical coding and computation indicates that temporal coding is probably a more biologically plausible scheme used by neurons than the rate coding used commonly in most published work. We propose and demonstrate in this letter that spiking neural networks (SNN), consisting of spiking neurons that propagate information by the ...
Fang, Huijuan, Wang, Yongji, He, Jiping
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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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2002
Neurons in the brain communicate by short electrical pulses, the so-called action potentials or spikes. How can we understand the process of spike generation? How can we understand information transmission by neurons? What happens if thousands of neurons are coupled together in a seemingly random network? How does the network connectivity determine the
Wulfram Gerstner, Werner M. Kistler
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Neurons in the brain communicate by short electrical pulses, the so-called action potentials or spikes. How can we understand the process of spike generation? How can we understand information transmission by neurons? What happens if thousands of neurons are coupled together in a seemingly random network? How does the network connectivity determine the
Wulfram Gerstner, Werner M. Kistler
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Simple model of spiking neurons
IEEE Transactions on Neural Networks, 2003A model is presented that reproduces spiking and bursting behavior of known types of cortical neurons. The model combines the biologically plausibility of Hodgkin-Huxley-type dynamics and the computational efficiency of integrate-and-fire neurons.
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Fast Sigmoidal Networks via Spiking Neurons
Neural Computation, 1997We show that networks of relatively realistic mathematical models for biological neurons in principle can simulate arbitrary feedforward sigmoidal neural nets in a way that has previously not been considered. This new approach is based on temporal coding by single spikes (respectively by the timing of synchronous firing in pools of neurons) rather ...
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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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Spiking Neurons Computing Platform
2005A computing platform is described for simulating arbitrary networks of spiking neurons in real time. A hybrid computing scheme is adopted that uses both software and hardware components. We focus on conductance-based models for neurons that emulate the temporal dynamics of the synaptic integration process.
Eduardo Ros +5 more
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