Neural coordination can be enhanced by occasional interruption of normal firing patterns: A self-optimizing spiking neural network model
This work addresses the challenge of transferring self-optimization principles to biologically plausible neural networks, though it is incremental as it builds on prior rate-based models.
The paper tackles the problem of enhancing neural coordination in spiking neural networks by showing that occasional interruption of normal firing patterns can improve self-optimization, achieving better basin enlargement in a Hopfield-like model.
The state space of a conventional Hopfield network typically exhibits many different attractors of which only a small subset satisfy constraints between neurons in a globally optimal fashion. It has recently been demonstrated that combining Hebbian learning with occasional alterations of normal neural states avoids this problem by means of self-organized enlargement of the best basins of attraction. However, so far it is not clear to what extent this process of self-optimization is also operative in real brains. Here we demonstrate that it can be transferred to more biologically plausible neural networks by implementing a self-optimizing spiking neural network model. In addition, by using this spiking neural network to emulate a Hopfield network with Hebbian learning, we attempt to make a connection between rate-based and temporal coding based neural systems. Although further work is required to make this model more realistic, it already suggests that the efficacy of the self-optimizing process is independent from the simplifying assumptions of a conventional Hopfield network. We also discuss natural and cultural processes that could be responsible for occasional alteration of neural firing patterns in actual brains