A New Approach towards Vision Suggested by Biologically Realistic Neural Microcircuit Models
BMCV '02 Proceedings of the Second International Workshop on Biologically Motivated Computer Vision
On the computational power of circuits of spiking neurons
Journal of Computer and System Sciences
Stability and topology in reservoir computing
MICAI'10 Proceedings of the 9th Mexican international conference on Artificial intelligence conference on Advances in soft computing: Part II
Survey: Reservoir computing approaches to recurrent neural network training
Computer Science Review
Simple model of spiking neurons
IEEE Transactions on Neural Networks
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The Liquid State Machine (LSM) is a method of computing with temporal neurons, which can be used amongst other things for classifying intrinsically temporal data directly unlike standard artificial neural networks. It has also been put forward as a natural model of certain kinds of brain functions. There are two results in this paper: (1) We show that the Liquid State Machines as normally defined cannot serve as a natural model for brain function. This is because they are very vulnerable to failures in parts of the model. This result is in contrast to work by Maass et al. which showed that these models are robust to noise in the input data. (2) We show that specifying certain kinds of topological constraints (such as ''small world assumption''), which have been claimed are reasonably plausible biologically, can restore robustness in this sense to LSMs.