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N H Farhat

Publications and source records attributed to N H Farhat.

2 recordsLinked to original sources

The Bifurcating Neuron network 1.

The Bifurcating Neuron (BN), a chaotic integrate-and-fire neuron, is a model of a neuron augmented by coherent modulation from its environment. The BN is mathematically equivalent to the sine-circle map, and this equivalence relationship allowed us to apply the mathematics of one-dimensional maps to the design of BN networks. The study of symmetry in the BN revealed that the BN can be configured to exhibit bistability that is controlled by attractor-merging crisis. Also, the symmetry of the bistability can be controlled by the introduction of a sinusoidal fluctuation to the threshold level of the BN. These two observations led us to the design of the BN Network 1 (BNN-1), a chaotic pulse-coupled neural network exhibiting associative memory. In numerical simulations, the BNN-1 showed a better performance than the continuous-time Hopfield network, as far as the spurious-minima problem is concerned and exhibited many biologically plausible characteristics.

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The double queue method: a numerical method for integrate-and-fire neuron networks.

Numerical methods for initial-value problems based on finite-differencing of differential equations (FDM) are not well suited for the simulation of an integrate-and-fire neuron network (IFNN) due to the discontinuities implied by the firing condition of the neurons. The Double Queue Method (DQM) is an event-queue based numerical method designed for the simulation of an IFNN that can deal with such discontinuities properly. In the DQM, the states of individual neurons at the next predicted discontinuous points are determined by an analytic solution, meaning an optimal performance in both accuracy and speed. A comparison study with the FDM demonstrates the superiority of the DQM, and provides some examples where the FDM gives inaccurate results that can possibly lead to a false conclusion about the dynamics of an IFNN.

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