Self-organization using synaptic plasticity

• Neural activity self-regulates to prevent neural circuits from becoming hyperor hypoactive by means of homeostatic processes [9]. • Optimal information processing in complex systems is attained at a critical point, near a transition between an ordered and an unordered regime of dynamics [5, 3, 8, 6]. • Self-Organized Criticality (SOC) [1, 2] has been proposed as a mechanism for neural systems which evolve naturally to a critical state without external tuning. • Regulation mechanism may be provided by synaptic plasticity, as proposed in [7]. In this work we analytically derive a local synaptic rule that can drive and maintain a neural network near the critical state. According to the proposed rule, synapses are either strengthened or weakened whenever a postsynaptic neuron receives either more or less input from the population than the required to fire at its natural frequency. This simple principle is enough for the network to selforganize at a critical region where the dynamic range is maximized. We illustrate this using a model of non-leaky spiking neurons with delayed coupling.