Automatic gain control of ultra-low leakage synaptic scaling homeostatic plasticity circuits
Ning Qiao, Giacomo Indiveri, Chiara Bartolozzi

TL;DR
This paper introduces an ultra-low leakage circuit and an automatic gain control scheme for neuromorphic systems, enabling long-term synaptic gain adaptation without interfering with learning processes.
Contribution
It presents a novel ultra-low leakage cell and gain control method that operate on very long time scales for stable homeostatic plasticity in neuromorphic circuits.
Findings
Implemented in 180 nm CMOS process
Achieves time constants up to 25 kilo-seconds
Consumes approximately 10.8 nW power
Abstract
Homeostatic plasticity is a stabilizing mechanism that allows neural systems to maintain their activity around a functional operating point. This is an extremely useful mechanism for neuromorphic computing systems, as it can be used to compensate for chronic shifts, for example due to changes in the network structure. However, it is important that this plasticity mechanism operates on time scales that are much longer than conventional synaptic plasticity ones, in order to not interfere with the learning process. In this paper we present a novel ultra-low leakage cell and an automatic gain control scheme that can adapt the gain of analog log-domain synapse circuits over extremely long time scales. To validate the proposed scheme, we implemented the ultra-low leakage cell in a standard 180 nm Complementary Metal-Oxide-Semiconductor (CMOS) process, and integrated it in an array of dynamic…
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Taxonomy
TopicsAdvanced Memory and Neural Computing · Neuroscience and Neural Engineering · CCD and CMOS Imaging Sensors
