Center-Crossing Recurrent Neural Networks for the Evolution of Rhythmic Behavior

Mathayomchan, Boonyanit; Beer, Randall D.

doi:10.1162/089976602320263999

Cited by 37 publications

(28 citation statements)

References 13 publications

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“…Large initial values would have probably resulted in networks where most neurons were saturated either fully on or off. A more general way to avoid this problem is given by Mathayomchan and Beer (2002).…”

Section: Appendix 1: Details Of the Genetic Algorithmmentioning

confidence: 99%

Evolving neural models of path integration

Vickerstaff

Paolo

2005

Journal of Experimental Biology

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We use a genetic algorithm to evolve neural models of path integration, with particular emphasis on reproducing the homing behaviour of Cataglyphis fortis ants. This is done within the context of a complete model system, including an explicit representation of the animal's movements within its environment. We show that it is possible to produce a neural network without imposing a priori any particular system for the internal representation of the animal's home vector. The best evolved network obtained is analysed in detail and is found to resemble the bicomponent model of Mittelstaedt.Because of the presence of leaky integration, the model can reproduce the systematic navigation errors found in desert ants. The model also naturally mimics the searching behaviour that ants perform once they have reached their estimate of the nest location. The results support possible roles for leaky integration and cosine-shaped compass response functions in path integration.

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Section: Appendix 1: Details Of the Genetic Algorithmmentioning

confidence: 99%

Evolving neural models of path integration

Vickerstaff

Paolo

2005

Journal of Experimental Biology

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“…In this so-called "centre crossing configuration" all nodes in a CTRNN interact at the centre of their sigmoid transfer functions (Mathayomchan and Beer, 2002), a mathematical property that bears close resemblance to the biological ideas highlighted by Turrigiano, amongst others. Williams also demonstrated that signal propagation was improved within such centre crossing networks.…”

Section: Signal Propagationmentioning

confidence: 81%

“…One possible reason for these results can be induced from the original work on centre crossing CTRNNs (Mathayomchan and Beer, 2002). Here, it was demonstrated that the generation of rhythmic patterns evolved more readily in such networks.…”

Section: Signal Propagationmentioning

confidence: 91%

Sensitivity and stability: A signal propagation sweet spot in a sheet of recurrent centre crossing neurons

Buckley

Bullock

2008

Biosystems

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a b s t r a c tIn this paper we demonstrate that signal propagation across a laminar sheet of recurrent neurons is maximised when two conditions are met. First, neurons must be in the so-called centre crossing configuration. Second, the network's topology and weights must be such that the network comprises strongly coupled nodes, yet lies within the weakly coupled regime. We develop tools from linear stability analysis with which to describe this regime in terms of the connectivity and weight strengths of a network. We use these results to examine the apparent tension between the sensitivity and instability of centre crossing networks.

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“…Many alternatives are available in the choice of the type of the kind of oscillator/neurons employed for the realization of the CPG, for instance sinusoidal [5], Hopf or adaptive frequency Hopf oscillator [6], Rayleigh [7], Van Der Pol [18], FitzHugh-Nagumo [3], Hopfield [19], Hopfield with synaptic depression [20] or Matsuoka's [21].…”

Section: Cpg Networkmentioning

confidence: 99%