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- Title
Adaptive STDP-based on-chip spike pattern detection.
- Authors
Gautam, Ashish; Takashi Kohno
- Abstract
A spiking neural network (SNN) is a bottom-up tool used to describe information processing in brain microcircuits. It is becoming a crucial neuromorphic computational model. Spike-timing-dependent plasticity (STDP) is an unsupervised brain-like learning rule implemented in many SNNs and neuromorphic chips. However, a significant performance gap exists between ideal model simulation and neuromorphic implementation. The performance of STDP learning in neuromorphic chips deteriorates because the resolution of synaptic efficacy in such chips is generally restricted to 6 bits or less, whereas simulations employ the entire 64-bit floating-point precision available on digital computers. Previously, we introduced a bio-inspired learning rule named adaptive STDP and demonstrated via numerical simulation that adaptive STDP (using only 4-bit fixedpoint synaptic efficacy) performs similarly to STDP learning (using 64-bit floatingpoint precision) in a noisy spike pattern detection model. Herein, we present the experimental results demonstrating the performance of adaptive STDP learning. To the best of our knowledge, this is the first study that demonstrates unsupervised noisy spatiotemporal spike pattern detection to perform well and maintain the simulation performance on a mixed-signal CMOS neuromorphic chip with lowresolution synaptic efficacy. The chip was designed in Taiwan Semiconductor Manufacturing Company (TSMC) 250 nm CMOS technology node and comprises a soma circuit and 256 synapse circuits along with their learning circuitry.
- Subjects
TAIWAN Semiconductor Manufacturing Co. Ltd.; TEXAS Instruments Inc.; ARTIFICIAL neural networks; SEMICONDUCTOR design
- Publication
Frontiers in Neuroscience, 2023, p1
- ISSN
1662-4548
- Publication type
Article
- DOI
10.3389/fnins.2023.1203956