Probabilistic synaptic weighting in a reconfigurable network of VLSI integrate-and-fire neurons
نویسندگان
چکیده
We present a scheme for implementing highly-connected, reconfigurable networks of integrate-and-fire neurons in VLSI. Neural activity is encoded by spikes, where the address of an active neuron is communicated through an asynchronous request and acknowledgement cycle. We employ probabilistic transmission of spikes to implement continuous-valued synaptic weights, and memory-based look-up tables to implement arbitrary interconnection topologies. The scheme is modular and scalable, and lends itself to the implementation of multi-chip network architectures. Results from a prototype system with 1024 analog VLSI integrate-and-fire neurons, each with up to 128 probabilistic synapses, demonstrate these concepts in an image processing task.
منابع مشابه
Analog VLSI spiking neural network with address domain probabilistic synapses
We present an analog VLSI address-event transceiver containing an array of integrate-and-fire neurons and a scheme for implementing a reconfigurable, scalable neural network with probabilistic synapses in the address domain. Neural “spikes” are transmitted through address-event representation, in which the address of the sending neuron is communicated through an asynchronous request and acknowl...
متن کاملRole of STDP in regulation of neural timing networks in human: a simulation study
Many physiological events require an accurate timing signal, usually generated by neural networks called central pattern generators (CPGs). On the other hand, properties of neurons and neural networks (e.g. time constants of neurons and weights of network connections) alter with time, resulting in gradual changes in timing of such networks. Recently, a synaptic weight adjustment mechanism has b...
متن کاملRole of STDP in regulation of neural timing networks in human: a simulation study
Many physiological events require an accurate timing signal, usually generated by neural networks called central pattern generators (CPGs). On the other hand, properties of neurons and neural networks (e.g. time constants of neurons and weights of network connections) alter with time, resulting in gradual changes in timing of such networks. Recently, a synaptic weight adjustment mechanism has b...
متن کاملA VLSI reconfigurable network of integrate-and-fire neurons with spike-based learning synapses
We present a VLSI device comprising an array of leaky integrate–and– fire (I&F) neurons and adaptive synapses with spike–timing dependent plasticity (STDP). The neurons transmit spikes off chip and the synapses receive spikes from external devices using a communication protocol based on the “Address– Event Representation” (AER). We studied the response properties of the neurons in the array to ...
متن کاملLearning to classify complex patterns using a VLSI network of spiking neurons
We propose a compact, low power VLSI network of spiking neurons which can learn to classify complex patterns of mean firing rates on–line and in real–time. The network of integrate-and-fire neurons is connected by bistable synapses that can change their weight using a local spike–based plasticity mechanism. Learning is supervised by a teacher which provides an extra input to the output neurons ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- Neural networks : the official journal of the International Neural Network Society
دوره 14 6-7 شماره
صفحات -
تاریخ انتشار 2001