Analog Implementation of Shunting Neural Networks
نویسندگان
چکیده
An extremely compact, all analog and fully parallel implementation of a class of shunting recurrent neural networks that is applicable to a wide variety of FET-based integration technologies is proposed. While the contrast enhancement, data compression, and adaptation to mean input intensity capabilities of the network are well suited for processing of sensory information or feature extraction for a content addressable memory (CAM) system, the network also admits a global Liapunov function and can thus achieve stable CAM storage itself. In addition the model can readily function as a front-end processor to an analog adaptive resonance circuit.
منابع مشابه
Implementation of a programmable neuron in CNTFET technology for low-power neural networks
Circuit-level implementation of a novel neuron has been discussed in this article. A low-power Activation Function (AF) circuit is introduced in this paper, which is then combined with a highly linear synapse circuit to form the neuron architecture. Designed in Carbon Nanotube Field-Effect Transistor (CNTFET) technology, the proposed structure consumes low power, which makes it suitable for the...
متن کاملComparative Study on Analog and Digital Neural Networks
For the last two decades, lot of research has been done on neural networks, resulting in many types of neural networks. These neural networks can be implemented in number of ways. Due to the revival of research interest in neural networks, some important technological developments have been made in VLSI. This paper discusses comparative study between analog implementation and digital implementa...
متن کاملWeight Perturbation: An Optimal Architecture and Learning Technique for Analog VLSI Feedforward and Recurrent Multilayer Networks
Previous work on analog VLSI implementation of multilayer perceptrons with on-chip learning has mainly targeted the implementation of algorithms such as back-propagation. Although back-propagation is efficient, its implementation in analog VLSI requires excessive computational hardware. It is shown that using gradient descent with direct approximation of the gradient instead of back-propagation...
متن کاملAnalog implementation of pulse-coupled neural networks
This paper presents a compact architecture for analog CMOS hardware implementation of voltage-mode pulse-coupled neural networks (PCNN's). The hardware implementation methods shows inherent fault tolerance specialties and high speed, which is usually more than an order of magnitude over the software counterpart. A computational style described in this article mimics a biological neural network ...
متن کاملJoining distributed pattern processing and homeostatic plasticity in recurrent on-center off-surround shunting networks: Noise, saturation, short-term memory, synaptic scaling, and BDNF
The activities of neurons vary within small intervals that are bounded both above and below, yet the inputs to these neurons may vary many-fold. How do networks of neurons process distributed input patterns effectively under these conditions? If a large number of input sources intermittently converge on a cell through time, then a serious design problem arises: if cell activities are sensitive ...
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:
دوره شماره
صفحات -
تاریخ انتشار 1988