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» Fractionally Predictive Spiking Neurons
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NIPS
2004
13 years 7 months ago
Bayesian inference in spiking neurons
We propose a new interpretation of spiking neurons as Bayesian integrators accumulating evidence over time about events in the external world or the body, and communicating to oth...
Sophie Deneve
IJON
2007
79views more  IJON 2007»
13 years 6 months ago
Predicting neuronal activity with simple models of the threshold type: Adaptive Exponential Integrate-and-Fire model with two co
An adaptive Exponential Integrate-and-Fire (aEIF) model was used to predict the activity of layer-V-pyramidal neurons of rat neocortex under random current injection. A new protoc...
Claudia Clopath, Renaud Jolivet, Alexander Rauch, ...
SYNASC
2005
IEEE
97views Algorithms» more  SYNASC 2005»
13 years 11 months ago
A Reinforcement Learning Algorithm for Spiking Neural Networks
The paper presents a new reinforcement learning mechanism for spiking neural networks. The algorithm is derived for networks of stochastic integrate-and-fire neurons, but it can ...
Razvan V. Florian
IWANN
2005
Springer
13 years 11 months ago
Real-Time Spiking Neural Network: An Adaptive Cerebellar Model
Abstract. A spiking neural network modeling the cerebellum is presented. The model, consisting of more than 2000 conductance-based neurons and more than 50 000 synapses, runs in re...
Christian Boucheny, Richard R. Carrillo, Eduardo R...
IJON
2007
82views more  IJON 2007»
13 years 6 months ago
Inhibitory conductance dynamics in cortical neurons during activated states
During activated states in vivo, neocortical neurons are subject to intense synaptic activity and high-amplitude membrane potential ðVmÞ fluctuations. These ‘‘high-conducta...
Martin Pospischil, Zuzanna Piwkowska, Michelle Rud...