Synaptic Delay Learning in Pulse-Coupled Neurons

Hüning, Harald; Glünder, Helmut; Palm, Günther

doi:10.1162/089976698300017665

Cited by 33 publications

(21 citation statements)

References 14 publications

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“…One mechanism ("delay shift") assumes that the transmission delays are altered [5,6]. This mechanism is possible because transmission velocities in the nervous system can be altered, for example, by changing the length and thickness of dendrites and axons, the extent of myelination of axons, or the density and type of ion channels.…”

mentioning

confidence: 99%

Dynamics of Self-Organized Delay Adaptation

Eurich

Pawelzik

Ernst³

et al. 1999

Phys. Rev. Lett.

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Adaptation of interaction delays is essential for the functioning of many natural and technical systems. We introduce a novel framework for studying the dynamics of delay adaptation in systems which optimize coincidence of inputs. For the important case of periodically modulated input we derive conditions for the existence and stability of solutions which constrain the set of mechanisms for reliable delay adaptation. Using numerical examples we show that our approach is applicable to more general than periodic input patterns such as Poissonian point processes with coordinated rate fluctuations.[S0031-9007(99)

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mentioning

confidence: 99%

Dynamics of Self-Organized Delay Adaptation

Eurich

Pawelzik

Ernst³

et al. 1999

Phys. Rev. Lett.

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“…The delay between mGluR stimulation and calcium response can vary considerably among cells, and this variation in delays has been suggested to underlie the adaptive timing of the classically conditioned eye-blink response (Fiala et al 1996;Steuber & Willshaw 1997, 2004 and the recognition of temporal parallel fibre patterns (Steuber & Willshaw 1999, 2004. In systems other than the cerebellum, delays have also been implicated in processes such as sound localisation and temporal pattern recognition in general (Hopfield 1995;Gerstner et al 1996;Napp-Zinn et al 1996;Eurich et al 1997Eurich et al , 1998Hüning et al 1998;Natschläger & Ruf 1998).…”

Section: Introductionmentioning

confidence: 99%

Generation of time delays: Simplified models of intracellular signalling in cerebellar Purkinje cells

Steuber

Willshaw

Ooyen

2006

Network: Computation in Neural Systems

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In many neuronal systems, information is encoded in temporal spike patterns. The recognition and storage of temporal patterns requires the generation and modulation of time delays between inputs and outputs. In cerebellar Purkinje cells, stimulation of metabotropic glutamate receptors (mGluRs) results in a delayed calcium and voltage response that has been implicated in classical conditioning and temporal pattern recognition. Here, we analyse and simplify a complex model of the intracellular signalling network that has been proposed as a substrate for this delayed response. We systematically simplify the original model, present a minimal model of time delay generation, and show that a delayed response can be produced by the combination of negative feedback and autocatalysis, without any intervening signalling steps that would contribute additive delays. The minimal model is analysed using phase plane methods, and classified as an excitable system. We discuss the implication of excitability for computations performed by intracellular signalling networks in general.

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“…In addition to the tuning of the synaptic efficacies, the ability to adjust the transmission delays between neurons is a feature that is considered to be relevant for learning mechanisms [24]. Our model includes not only the classical synaptic weights as parameters, but also the transmission delays.…”

Section: Introductionmentioning

confidence: 99%

On the Sample Complexity of Learning for Networks of Spiking Neurons With Nonlinear Synaptic Interactions

Schmitt

2004

IEEE Trans. Neural Netw.

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We study networks of spiking neurons that use the timing of pulses to encode information. Nonlinear interactions model the spatial groupings of synapses on the neural dendrites and describe the computations performed at local branches. Within a theoretical framework of learning we analyze the question of how many training examples these networks must receive to be able to generalize well. Bounds for this sample complexity of learning can be obtained in terms of a combinatorial parameter known as the pseudodimension. This dimension characterizes the computational richness of a neural network and is given in terms of the number of network parameters. Two types of feedforward architectures are considered: constant-depth networks and networks of unconstrained depth. We derive asymptotically tight bounds for each of these network types. Constant depth networks are shown to have an almost linear pseudodimension, whereas the pseudodimension of general networks is quadratic. Networks of spiking neurons that use temporal coding are becoming increasingly more important in practical tasks such as computer vision, speech recognition, and motor control. The question of how well these networks generalize from a given set of training examples is a central issue for their successful application as adaptive systems. The results show that, although coding and computation in these networks is quite different and in many cases more powerful, their generalization capabilities are at least as good as those of traditional neural network models.

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Synaptic Delay Learning in Pulse-Coupled Neurons

Cited by 33 publications

References 14 publications

Dynamics of Self-Organized Delay Adaptation

Dynamics of Self-Organized Delay Adaptation

Generation of time delays: Simplified models of intracellular signalling in cerebellar Purkinje cells

On the Sample Complexity of Learning for Networks of Spiking Neurons With Nonlinear Synaptic Interactions

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