Building Blocks for Spikes Signals Processing

Neuromorphic engineers study models and implementations of systems that mimic neurons behavior in the brain. Neuro-inspired systems commonly use spikes to represent information. This representation has several advantages: its robustness to noise thanks to repetition, its continuous and analog information representation using digital pulses, its capacity of pre-processing during transmission time, ... , Furthermore, spikes is an efficient way, found by nature, to codify, transmit and process information. In this paper we propose, design, and analyze neuro-inspired building blocks that can perform spike-based analog filters used in signal processing. We present a VHDL implementation for FPGA. Presented building blocks take advantages of the spike rate coded representation to perform a massively parallel processing without complex hardware units, like floating point arithmetic units, or a large memory. Those low requirements of hardware allow the integration of a high number of blocks inside a FPGA, allowing to process fully in parallel several spikes coded signals.Junta de Andalucía P06-TIC-O1417Ministerio de Ciencia e Innovación TEC2009-10639-C04-02Ministerio de Ciencia e Innovación TEC2006-11730-C03-0

Real time unsupervised learning of visual stimuli in neuromorphic VLSI systems

Neuromorphic chips embody computational principles operating in the nervous system, into microelectronic devices. In this domain it is important to identify computational primitives that theory and experiments suggest as generic and reusable cognitive elements. One such element is provided by attractor dynamics in recurrent networks. Point attractors are equilibrium states of the dynamics (up to fluctuations), determined by the synaptic structure of the network; a `basin' of attraction comprises all initial states leading to a given attractor upon relaxation, hence making attractor dynamics suitable to implement robust associative memory. The initial network state is dictated by the stimulus, and relaxation to the attractor state implements the retrieval of the corresponding memorized prototypical pattern. In a previous work we demonstrated that a neuromorphic recurrent network of spiking neurons and suitably chosen, fixed synapses supports attractor dynamics. Here we focus on learning: activating on-chip synaptic plasticity and using a theory-driven strategy for choosing network parameters, we show that autonomous learning, following repeated presentation of simple visual stimuli, shapes a synaptic connectivity supporting stimulus-selective attractors. Associative memory develops on chip as the result of the coupled stimulus-driven neural activity and ensuing synaptic dynamics, with no artificial separation between learning and retrieval phases.Comment: submitted to Scientific Repor