Complex dynamics in simple neural circuits
AIP Conference Proceedings 151 on Neural Networks for Computing
AIP Conference Proceedings 151 on Neural Networks for Computing
Oscillations and synchronization in neural networks: an exploration of the labeling
International Journal of Neural Systems
Absence of cycles in symmetric neural networks
Neural Computation
Convergence properties of the softassign quadratic assignment algorithm
Neural Computation
Parameter space structure of continuous-time recurrent neural networks
Neural Computation
Journal of Computational and Applied Mathematics
Computer simulations of exponentially convergent networks with large impulses
Mathematics and Computers in Simulation
| Hi-index | 0.00 |
We consider some neural networks which have interesting oscillatory dynamics and analyze stability and bifurcation properties. The neurons are of the sigmoidal type (i.e., analog elements which have states in a real interval, X). The dynamics is discrete-time and synchronous. Thus, for an m-neuron network, it is given by iteration of a map F:X^m - X^m. The study of such discrete-time continuum-state systems is motivated both by the difference equations which arise in numerical simulation of differentiable neural networks and independently by the possibility of constructing VLSI circuits with clocking techniques to implement such neural networks having prescribed fixed-points or periodic orbits.