Synchrony Detection by Analogue VLSI Neurons with Bimodal STDP Synapses

We present test results from spike-timing correlation learning experiments carried out with silicon neurons with STDP (Spike Timing Dependent Plasticity) synapses. The weight change scheme of the STDP synapses can be set to either weight-independent or weight-dependent mode. We present results that characterise the learning window implemented for both modes of operation. When presented with spike trains with di#erent types of synchronisation the neurons develop bimodal weight distributions. We also show that a 2-layered network of silicon spiking neurons with STDP synapses can perform hierarchical synchrony detection

Neural Hardware: beyond ones and zeros

Abstract. An overview of research on the implementation of neural systems is presented in this paper. We focus on implementations where the algorithms and their physical support are tightly coupled. First, we concentrate on the potential of probabilistic algorithms to compensate for hardware non-idealities. Then, electronic circuits which aim to reproduce the structure of neurobiological systems in hardware are introduced. Finally, we extend to neuroengineering whose focus is placed on interfacing artificial devices with biological systems.