Dynamic population coding with recurrent networks of integrate and fire neurons
نویسندگان
چکیده
منابع مشابه
Evolving networks of integrate-and-fire neurons
This paper addresses the following question: ‘‘What neural circuits can emulate the monosynaptic correlogram generated by a direct connection between two neurons?’’ The search for answers to that question has been tackled in two steps: (1) we incorporated into an integrate-and-fire (IAF) neuron model those aspects of neuronal physiology that can influence cross-correlated activity; (2) we evolv...
متن کاملIntegrate-and-Fire Neurons and Networks
Most biological neurons communicate by short electrical pulses, called action potentials or spikes. In contrast to the standard neuron model used in artificial neural networks, integrate-and-fire neurons do not rely on a temporal average over the pulses. In integrate-and-fire and similar spiking neuron models, the pulsed nature of the neuronal signal is taken into account and considered as pote...
متن کاملModeling Networks with VLSI (linear) Integrate-and-Fire Neurons
We analyse in detail all the statistical properties of a \canon-ical" integrate-and-re VLSI neuron with a linear integrator 1]. We show that a network of such elements can maintain both stable spontaneous activity and selective delay activity, contrary to current opinion. The spike statistics appears to be qualitatively the same as in networks of conventional (exponential) integrate-and-re neur...
متن کاملIntegrate and Fire Neurons
SpikeNET is a simulator for modeling large networks of asynchronously spiking neurons. It uses simple integrate-and-fire neurons which undergo step-like changes in membrane potential when synaptic inputs arrive. If a threshold is exceeded, the potential is reset and the neuron added to a list to be propagated on the next time step. Using such spike lists greatly reduces the computations associa...
متن کاملSpeed of feedforward and recurrent processing in multilayer networks of integrate-and-fire neurons.
The speed of processing in the visual cortical areas can be fast, with for example the latency of neuronal responses increasing by only approximately 10 ms per area in the ventral visual system sequence V1 to V2 to V4 to inferior temporal visual cortex. This has led to the suggestion that rapid visual processing can only be based on the feedforward connections between cortical areas. To test th...
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ژورنال
عنوان ژورنال: Frontiers in Neuroscience
سال: 2010
ISSN: 1662-453X
DOI: 10.3389/conf.fnins.2010.03.00119