Code-specific policy gradient rules for spiking neurons
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Maximization of information transmission by a spiking-neuron model predicts changes of synaptic connections that depend on timing of pre- and postsynaptic spikes and on the postsynaptic membrane potential. Under the assumption of Poisson firing statistics, ...
In the present paper we propose a novel method for the identification and modeling of neural networks using extracellular spike recordings. We create a deterministic model of the effective network, whose dynamic behavior fits experimental data. The network ...
In timing-based neural codes, neurons have to emit action potentials at precise moments in time. We use a supervised learning paradigm to derive a synaptic update rule that optimizes via gradient ascent the likelihood of postsynaptic firing at one or sever ...
A neuron that is stimulated repeatedly by the same time-dependent stimulus exhibits slightly different spike timing at each trial. We compared the exact solution of the time-dependent firing rate for a stochastically spiking neuron model with refractorines ...
In timing-based neural codes, neurons have to emit action potentials at precise moments in time. We use a supervised learning paradigm to derive a synaptic update rule that optimizes via gradient ascent the likelihood of postsynaptic firing at one or sever ...
Device comprising storage means (51) for storing a genotypic representation (3) of a spiking neural network comprising spiking neurons (1) and input neurons (2) connected by synapses, and computer program portions for performing the steps of mutating said ...
We investigate generic models for cortical microcircuits, i.e., recurrent circuits of integrate-and-fire neurons with dynamic synapses. These complex dynamic systems subserve the amazing information processing capabilities of the cortex, but are at the pre ...
Small assemblies of Fitzhugh-Nagumo neural oscillators are used to study a type of threshold coupling coming from biology where it is used to model synapses. Phase locking and coincidence detection is modelled in the two most simple coupling configurations ...
We investigate the performance of sparsely-connected networks of integrate-and-fire neurons for ultra-short term information processing. We exploit the fact that the population activity of networks with balanced excitation and inhibition can switch from an ...
A key challenge for neural modeling is to explain how a continuous stream of multimodal input from a rapidly changing environment can be processed by stereotypical recurrent circuits of integrate-and-fire neurons in real time. We propose a new computationa ...