Generalized activity equations for spiking neural network dynamics
نویسندگان
چکیده
منابع مشابه
Generalized activity equations for spiking neural network dynamics
Much progress has been made in uncovering the computational capabilities of spiking neural networks. However, spiking neurons will always be more expensive to simulate compared to rate neurons because of the inherent disparity in time scales-the spike duration time is much shorter than the inter-spike time, which is much shorter than any learning time scale. In numerical analysis, this is a cla...
متن کاملComputational Dynamics of a Spiking Neural Network
The goal of this project is to understand the computational characteristics of a spiking neural network (SNN) that behaves as an associative memory. The basic SNN model, which we call a pathway, consists of a set of input neurons mapping to a set of output neurons. We first perform a training procedure so that the pathway achieves a specified input/output mapping. We then explore the temporal d...
متن کاملSpiking Neural Network Architecture
ARM microprocessors are found in nearly every consumer device, from smartphones to gameboxes to e-readers and digital televisions. But did you know that, combined, these same ARM microprocessor cores can simulate the human brain? The Spiking Neural Network Architecture (SpiNNaker), a massively parallel neurocomputer architecture, aims to use more than one million ARM microprocessor cores to mod...
متن کاملNumerical solution of fuzzy differential equations under generalized differentiability by fuzzy neural network
In this paper, we interpret a fuzzy differential equation by using the strongly generalized differentiability concept. Utilizing the Generalized characterization Theorem. Then a novel hybrid method based on learning algorithm of fuzzy neural network for the solution of differential equation with fuzzy initial value is presented. Here neural network is considered as a part of large eld called ne...
متن کاملStochastic Dynamics of a Finite-Size Spiking Neural Network
We present a simple Markov model of spiking neural dynamics that can be analytically solved to characterize the stochastic dynamics of a finite-size spiking neural network. We give closed-form estimates for the equilibrium distribution, mean rate, variance, and autocorrelation function of the network activity. The model is applicable to any network where the probability of firing of a neuron in...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Frontiers in Computational Neuroscience
سال: 2013
ISSN: 1662-5188
DOI: 10.3389/fncom.2013.00162