Syn re Graphs From Spike Patterns to Automata of Spiking Neurons

The concept of syn re chains has been proposed by Abeles as a reason able biophysical model for cortical long time correlations and replicating spike patterns in multi unit recordings Some recent computational modelling ap proaches extend the model into a functional direction proposing that the synchronization of syn re chains may help to solve the binding problem of cortical information processing In the present paper we investigate further computational aspects of syn re chains First we show how they can be used as spatio temporal feature stores capable to learn regenerate and recognize spatio temporal signals Thereby syn re chains introduce time into the static world of attractor neural networks as paradigms for cortical information pro cessing Then we extend the syn re chain model from linear autonomously evolving networks to graph like structures with external input signals Such syn re graphs can implement arbitrary deterministic and nondeterministic nite state automata We prove formally that syn re graphs consisting of time continuous spiking neurons can robustly process arbitrary long input words even if realistic postsynaptic potentials bounded background noise and spike timing jitter are taken into consideration A single syn re node may consist of a single spiking neuron or a larger set of cells In the latter case connec tions between two nodes can be diluted or have otherwise random synaptic e cacies The extension of syn re chains to syn re graphs introduces opera tional logical procedural cognitive components into common modelling of Hebbian cell assemblies and brain functioning