Results 171 to 180 of about 1,420 (216)
Some of the next articles are maybe not open access.
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
openaire +2 more sources
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
openaire +2 more sources
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
openaire +1 more source
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
openaire +1 more source
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
openaire +2 more sources
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.
openaire +2 more sources
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.
openaire +2 more sources
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
openaire +1 more source
Spiking neuron models for regular-spiking, intrinsically bursting, and fast-spiking neurons
ICONIP'99. ANZIIS'99 & ANNES'99 & ACNN'99. 6th International Conference on Neural Information Processing. Proceedings (Cat. No.99EX378), 2003Simplified models are proposed as variants of the integrate-and-fire-model for an intrinsically bursting (IB) neuron and for a fast-firing (FS) neuron taking refractory periods into consideration. A model of a regular-spiking neuron is also described in the conventional manner for the sake of comparison.
S. Inawashiro, S. Miyake, M. Ito
openaire +1 more source
Selective spiking in neuronal populations
2017 American Control Conference (ACC), 2017The use of extrinsic stimulation to control activity in neuronal networks i.e., neurocontrol, is a key problem in control engineering and neuroscience. Here, we study the general problem of selective spiking in a population of neurons. The goal is to use an input stimulus in order to induce a spike in a specific neuron of a population while keeping all
Anirban Nandi +2 more
openaire +1 more source
The 2013 International Joint Conference on Neural Networks (IJCNN), 2013
A biologically realistic spiking neuron model has been proposed which contains a novel non linear spiking function. Proposed neuron model contains a lower order spike generating function in contrast to the spike generating function of Quadratic integrate fire neuron model.
B. Chandra 0001 +1 more
openaire +1 more source
A biologically realistic spiking neuron model has been proposed which contains a novel non linear spiking function. Proposed neuron model contains a lower order spike generating function in contrast to the spike generating function of Quadratic integrate fire neuron model.
B. Chandra 0001 +1 more
openaire +1 more source
SpikeCell: a deterministic spiking neuron
Neural Networks, 2002We present a model of spiking neuron that emulates the output of the usual static neurons with sigmoidal activation functions. It allows for hardware implementations of standard feedforward networks, trained off-line with any classical learning algorithm (i.e. back-propagation and its variants). The model is validated on hand-written digits recognition,
Christelle Godin +2 more
openaire +2 more sources