Description
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Sequence memories play an important role in biological systems. This work demonstrates how a sequence memory may be built from biologically plausible spiking neural components. The memory is incorporated in a sequence machine, an automaton that can perform on-line learning and prediction of sequences of symbols. The sequence machine comprises an associative memory which is a variant of Pentti Kanerva's Sparse Distributed Memory, together with a separate memory for storing the sequence context or history. The symbols constituting a sequence are encoded as rank-ordered N-of-M codes, each code being implemented as a burst of spikes emitted by a layer of neurons. When appropriate neural structures are used the spike bursts maintain coherence and stability as they pass through successive neural layers. The system is modelled using a representation of order that abstracts time, and the abstracted system is shown to perform equivalently to a low-level spiking neural system. The spiking neural implementation of the sequence memory model highlights issues that arise when engineering high-level systems with asynchronous spiking neurons as building blocks.
(Author portrait)
Bose, JoyJoy Bose completed his PhD with the University of Manchester in 2007. His research interests include spiking neural models, sequence learning and understanding, modelling and building systems out of neuron like components. He is currently working as a Software Engineer with DAI Limited.
