A VLSI implementation of a calcium-based plasticity learning model

A key feature of autonomous systems is the ability to solve computationally intensive tasks while adapting to changes in the environment. The learning capabilities required for this feature are underdeveloped in artificial systems, especially when compared to those of humans and animals. We aim at the implementation of biologically inspired learning algorithms to be embedded in full-custom VLSI spiking neural networks with the long term goal of constructing compact real-time low-power learning systems. Molecular studies have demonstrated a key role of calcium ions for long term synaptic plasticity. These experimental results have inspired mathematical models and hardware implementations of calcium based learning algorithms. Here we present a novel VLSI implementation of a recently proposed calcium-based learning algorithm, its simulation results and comparison with the mathematical model.