Synchrony detection and amplification by silicon neurons with STDP synapses.
Spike-timing dependent synaptic plasticity (STDP) is a form of plasticity driven by precise spike-timing differences between presynaptic and postsynaptic spikes. Thus, the learning rules underlying STDP are suitable for learning neuronal temporal phenomena such as spike-timing synchrony. It is well known that weight-independent STDP creates unstable learning processes resulting in balanced bimodal weight distributions. In this paper, we present a neuromorphic analog very large scale integration (VLSI) circuit that contains a feedforward network of silicon neurons with STDP synapses. The learning rule implemented can be tuned to have a moderate level of weight dependence. This helps stabilise the learning process and still generates binary weight distributions. From on-chip learning experiments we show that the chip can detect and amplify hierarchical spike-timing synchrony structures embedded in noisy spike trains. The weight distributions of the network emerging from learning are bimodal.
Bofill-i-petit
Adria
A
School of Engineering and Electronics, University of Edinburgh, Edinburgh EH93JL, UK. adria.bofill@ee.ed.ac.uk
Murray
Alan F
AF
D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D001185:Artificial Intelligence; D001921:Brain / Q000502:physiology; D006801:Humans; D007858:Learning / Q000502:physiology; D008838:Microcomputers; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D009434:Neural Pathways / Q000502:physiology; D009473:Neuronal Plasticity / Q000502:physiology; D009474:Neurons / Q000502:physiology*; D009435:Synaptic Transmission / Q000502:physiology*; D013997:Time Factors