Open AccessProceedings Article
A Model for Real-Time Computation in Generic Neural Microcircuits
Wolfgang Maass,Thomas Natschläger,Henry Markram +2 more
- Vol. 15, pp 229-236
TLDR
A new computational model is proposed that is based on principles of high dimensional dynamical systems in combination with statistical learning theory and can be implemented on generic evolved or found recurrent circuitry.Abstract:
A key challenge for neural modeling is to explain how a continuous stream of multi-modal input from a rapidly changing environment can be processed by stereotypical recurrent circuits of integrate-and-fire neurons in real-time. We propose a new computational model that is based on principles of high dimensional dynamical systems in combination with statistical learning theory. It can be implemented on generic evolved or found recurrent circuitry.read more
Citations
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Survey: Reservoir computing approaches to recurrent neural network training
TL;DR: This review systematically surveys both current ways of generating/adapting the reservoirs and training different types of readouts, and offers a natural conceptual classification of the techniques, which transcends boundaries of the current ''brand-names'' of reservoir methods.
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2007 Special Issue: An experimental unification of reservoir computing methods
TL;DR: Three different uses of a recurrent neural network as a reservoir that is not trained but instead read out by a simple external classification layer are compared and a new measure for the reservoir dynamics based on Lyapunov exponents is introduced.
Proceedings Article
Adaptive Nonlinear System Identification with Echo State Networks
TL;DR: An online adaptation scheme based on the RLS algorithm known from adaptive linear systems is described, as an example, a 10-th order NARMA system is adaptively identified.
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An overview of reservoir computing: theory, applications and implementations
TL;DR: This tutorial will give an overview of current research on theory, applica- tion and implementations of Reservoir Computing, which makes it possible to solve complex tasks using just linear post-processing techniques.
Journal ArticleDOI
Isolated word recognition with the liquid state machine : a case study
TL;DR: A case study of the performance of the Liquid State Machine based on a recurrent spiking neural network by applying it to a well known and well studied problem: speech recognition of isolated digits and finds the biologically most realistic encoding performs far better than more conventional methods.
References
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Journal ArticleDOI
Real-time computing without stable states: a new framework for neural computation based on perturbations
TL;DR: A new computational model for real-time computing on time-varying input that provides an alternative to paradigms based on Turing machines or attractor neural networks, based on principles of high-dimensional dynamical systems in combination with statistical learning theory and can be implemented on generic evolved or found recurrent circuitry.
Journal ArticleDOI
Differential signaling via the same axon of neocortical pyramidal neurons
TL;DR: Differential signaling is a key mechanism in neocortical information processing, which can be regulated by selective synaptic modifications, andeterogeneity of synaptic transfer functions allows multiple synaptic representations of the same presynaptic action potential train and suggests that these synaptic representations are regulated in a complex manner.
Journal ArticleDOI
Organizing Principles for a Diversity of GABAergic Interneurons and Synapses in the Neocortex
TL;DR: It is suggested that inhibitory synapses could shape the impact of different interneurons according to their specific spatiotemporal patterns of activity and that GABAergic interneuron and synapse diversity may enable combinatorial inhibitory effects in the neocortex.
Journal ArticleDOI
Temporal Information Transformed into a Spatial Code by a Neural Network with Realistic Properties
TL;DR: It is demonstrated that known time-dependent neuronal properties enable a network to transform temporal information into a spatial code in a self-organizing manner, with no need to assume a spectrum of time delays or to custom-design the circuit.
Journal ArticleDOI
What is a moment? Transient synchrony as a collective mechanism for spatiotemporal integration
John J. Hopfield,Carlos D. Brody +1 more
TL;DR: The principles behind the operation of a network of simple integrate-and-fire neurons that contained output neurons selective for specific spatiotemporal patterns of inputs are presented and it is shown how the recognition is invariant to uniform time warp and uniform intensity change of the input events.
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