VLSI implementation of a calcium-based plasticity learning model

Abstract

Los sistemas autónomos deben poder adaptarse a un entorno en constante cambio. Esta adaptabilidad requiere importantes recursos computacionales dedicados al aprendizaje, sin embargo, los sistemas artificiales actuales carecen de estos recursos en comparación con los humanos y los animales. Nuestro objetivo es producir redes neuronales con spikes con VLSI que presenten estructuras de aprendizaje similares a las de la biología con el objetivo de lograr el rendimiento y la eficiencia de los sistemas naturales. La literatura de neurociencia sugiere que los iones de calcio juegan un papel clave en la explicación de la dependencia de la plasticidad sináptica a largo plazo de múltiples factores, como el tiempo de los spikes y la frecuencia de los estímulos. Aquí presentamos una implementación VLSI novedosa de un modelo de plasticidad sináptica basado en calcio, comparaciones entre el modelo y las simulaciones de circuito, y mediciones del circuito fabricado.

This thesis aims at the implementation of biologically inspired learning algorithm to be embedded in full-custom VLSI spiking neural networks with the goal of constructing compact real-time low-power learning systems with potential application in computational neuroscience basic research investigation, and applications where input data is ambiguous such as in patter recognition.

The starting point of this research is based on recent studies that 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 I present two prototypes of a novel Very-large-scale Integration (VLSI) implementation of a recently proposed calcium-based learning algorithm, its circuital and computation model simulation results and comparison with the mathematical model. The second improved circuit corrects errors observed in the first chip and it is connected to a low-power neuron in a small array.

The elaboration of this learning system embedded in a chip provides insight and significant progress in the complex task to understand how to build brain-like integrated systems. This system can be used also as a tool for validating hypotheses arising from experimental observations of biological systems and computational models.