Title :
An adaptive neuron circuit for signal compression
Author :
Sheng-Feng Yen ; Harris, J.G.
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Florida, Gainesville, FL, USA
fDate :
May 30 2010-June 2 2010
Abstract :
We present a low-bandwidth analog circuit for implementing an adaptive biphasic leaky integrate-and-fire neuron. This neuron circuit is targeted for signal compression in neural recording applications. Unlike other adaptive neuron circuits, this adaptive integrate-and-fire neuron supports signal reconstruction with known threshold voltages. Matlab simulations show promising bandwidth reduction comparing to an integrate-and-fire neuron without the adaptive feature. We quantify the circuit performance in terms of the tradeoff between signal reconstruction accuracy and bandwidth.
Keywords :
analogue circuits; bandwidth compression; neural nets; signal reconstruction; adaptive biphasic leaky integrate-and-fire neuron; adaptive neuron circuit; bandwidth reduction; low-bandwidth analog circuit; neural recording application; signal compression; signal reconstruction; threshold voltage; Analog circuits; Bandwidth; Biological information theory; Capacitors; Neurons; Pulse circuits; Pulse generation; Signal reconstruction; Switches; Threshold voltage;
Conference_Titel :
Circuits and Systems (ISCAS), Proceedings of 2010 IEEE International Symposium on
Conference_Location :
Paris
Print_ISBN :
978-1-4244-5308-5
DOI :
10.1109/ISCAS.2010.5537008