Attention through Self-Synchronisation in the Spiking Neuron Stochastic Diffusion Network

The paper discusses ensemble behaviour in the Spiking Neuron Stochastic Diffusion Network, SNSDN, a novel network exploring biologically plausible information processing based on higher order temporal coding. SNSDN was proposed as an alternative solution to the binding problem [1]. SNSDN operation resembles Stochastic Diffusion Search, SDS, a nondeterministic search algorithm able to rapidly locate the best instantiation of a target pattern within a noisy search space ([3], [5]). In SNSDN, relevant information is encoded in the length of interspike intervals. Although every neuron operates in its own time, ‘attention’ to a pattern in the search space results in self-synchronised activity of a large population of neurons. When multiple patterns are present in the search space, ‘switching of attention’ results in a change of the synchronous activity. The qualitative effect of attention on the synchronicity of spiking behaviour in both time and frequency domain will be discussed. 1 Stochastic Diffusion Search Stochastic Diffusion Search ([3], [4], [5], [6], [7], [8]) is a parallel, non-deterministic pattern matching algorithm. It is capable of rapidly locating a specified pattern – or its best instantiation – in a noisy search space. Its operation is most easily explained by analogy. 1.1 Ant Search Analogy Consider the following example of hypothetical ant-like creatures searching for a good nutrient source in a dynamic environment. Each ant seeks to locate some food and return it to the nest. The colony as a whole seeks to maximise the rate of return of food or the minimum expenditure of energy. ∗[email protected]