I. Demo Description Traditionally, neuromorphic ICs have integrated only reduced subsets of the rich repertoire of plasticity seen in biological preparations [1], [2]. The focus with respect to long term plasticity has been mostly on Spike-Time-Dependent Plasticity (STDP) [1]. Several ICs have also implemented forms of presynaptic short term dynamics, which filter synaptic pulse input, but have no influence on other timescales of plasticity. Here, we demonstrate an IC that implements short-term-, long-term-, and metaplasticity in an integrated way following [3], where these three different timescales interact to form the overall weight at the synapse. Fig. 1 shows an example presynaptic pattern with depression and the membrane trace as input for learning [3]. The resulting analog weight state shows the influence of presynaptic depression in the step increases, comparable to [1]. Also, different settings for the learning threshold exhibit a bias towards weight increase/decrease on a metaplastic (i.e. slow) timescale similar to [2]. The overall setup features several Maple-ICs of each 16 neurons and 512 of the above synapses, interlinked via FPGA-based pulse transmission. This allows network sizes of up to 200 neurons, sufficient to demonstrate the necessity for this type of learning for a range of computational neuroscience models.
展开▼
