A Neuron Model for FPGA Spiking Neuronal Network Implementation
نویسندگان
چکیده
منابع مشابه
A Solid-State Neuron for Spiking Neural Network Implementation
This paper presents a compact analog neuron cell incorporating an array of charge-coupled synapses connected via a common output terminal. The novel silicon synapse is based on a two stage charge-coupled device where the weighting functionality can be integrated into the first stage. A presynaptic spike to the second gate allows the charge under the first gate to drift onto the floating diffusi...
متن کاملAn Efficient Implementation of a Realistic Spiking Neuron Model on an FPGA
Hardware implementations of spiking neuron models have been studied over the years mainly in researches focused on bio-inspired systems and computational neuroscience. This introduced considerable challenges for researchers particularly in terms of the requirements to realise a efficient embedded solution which may provide artificial devices adaptability and performance in real-time environment...
متن کاملA microscopic spiking neuronal network for the age-structured model
We introduce a microscopic spiking network consistent with the age-structured/renewal equation proposed by Pakdaman, Perthame and Salort. It is a jump process interacting through a global activity variable with random delays. We show the well-posedness of the particle system and the mean-field equation. Moreover, by studying the tightness of the empirical measure, we prove the propagation of ch...
متن کاملFPGA implementation of a biological neural network based on the Hodgkin-Huxley neuron model
A set of techniques for efficient implementation of Hodgkin-Huxley-based (H-H) model of a neural network on FPGA (Field Programmable Gate Array) is presented. The central implementation challenge is H-H model complexity that puts limits on the network size and on the execution speed. However, basics of the original model cannot be compromised when effect of synaptic specifications on the networ...
متن کاملSpiking neuron network Helmholtz machine
An increasing amount of behavioral and neurophysiological data suggests that the brain performs optimal (or near-optimal) probabilistic inference and learning during perception and other tasks. Although many machine learning algorithms exist that perform inference and learning in an optimal way, the complete description of how one of those algorithms (or a novel algorithm) can be implemented 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: Advances in Electrical and Computer Engineering
سال: 2011
ISSN: 1582-7445,1844-7600
DOI: 10.4316/aece.2011.04005