On the computational power of circuits of spiking neurons
Journal of Computer and System Sciences
Neural Systems as Nonlinear Filters
Neural Computation
Synchrony State Generation in Artificial Neural Networks with Stochastic Synapses
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
A hypothetical free synaptic energy function and related states of synchrony
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
On the capacity of transient internal states in liquid-state machines
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
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This is a follow up paper that integrates our recent published work discussing the implementation of brain-inspired information processing system by means of finite-state machines. Using a formerly presented implementation of the liquid-state machines framework using a novel synaptic model, this study shows that such a network represents and processes input information internally using transitions among a set of discrete and finite neural temporal states. The introduced framework is coined the temporal finite-state machine (tFSM). The proposed work involves a new definition for a "neural state" within a dynamic network and it discusses the computational capacity of the tFSM. This paper presents novel perspectives and open new avenues in importing the behaviour of spiking neural networks into the classical computational model of finite-state machines.