Nonequilibrium Behavior in Neural Networks: Criticality and Optimal Performance
We present a general theory which allows one to study the effects on emergent, cooperative behavior of a complex interplay between different dynamic processes that occur in actual systems at the neuron, synapse and network levels. We consider synaptic changes at different time scales from less than the millisecond to the scale of learning, and the possibility of finding a fraction of silent neurons. For some limits of interest, the fixed-point solutions or memories then loose stability and the system shows enhancement of its response to changing external stimuli for particular network topologies and dynamical memories. We observe at the edge of chaos that the network activity becomes critical in the sense that the relevant quantities show non-trivial, power-law distributions. We also describe the effect of activity-dependent synaptic processes on the network storage capacity.
Unstable dynamics Activity-dependent synaptic processes Dynamical memories Optimum network topology Criticality
J.J. Torres S. Johnson J.F. Mejias S. de Franciscis J. Marro
Institute Carlos I for Theoretical and Computational Physics, University of Granada, E-18071 Granada, Spain
国际会议
The Second International Conference on Cognitive Neurodynamics--2009(第二届国际认知神经动力学会议)
杭州
英文
597-606
2009-11-15（万方平台首次上网日期，不代表论文的发表时间）