title: Temporal binding as an inducer for connectionist recruitment learning over delayed lines.
abstract: The temporal correlation hypothesis proposes using distributed synchrony for the binding of different stimulus features. However, synchronized spikes must travel over cortical circuits that have varying length pathways, leading to mismatched arrival times. This raises the question of how initial stimulus-dependent synchrony might be preserved at a destination binding site. Earlier, we proposed constraints on tolerance and segregation parameters for a phase-coding approach, within cortical circuits, to address this question [Proceedings of the International Joint Conference on Neural Networks, Washington, DC, 2001]. The purpose of the present paper is twofold. First, we conduct simulation experiments to test the proposed constraints. Second, we explore the practicality of temporal binding to drive a process of long-term memory formation based on a recruitment learning method [Biol. Cybernet. 46 (1982) 27]. A network based on Valiant's neuroidal architecture [Circuits of the mind, 1994] is used to demonstrate the coalition between temporal binding and recruitment. Complementing similar approaches, we implement a continuous-time learning procedure allowing computation with spiking neurons. The viability of the proposed binding scheme is investigated by conducting simulation studies which examine binding errors. In the simulation, binding errors cause the perception of illusory conjunctions among features belonging to separate objects. Our results indicate that when tolerance and segregation parameters obey our proposed constraints, the assemblies of correct bindings are dominant over assemblies of spurious bindings in reasonable operating conditions.
authors.affiliation: Center for Advanced Computer Studies, University of Louisiana at Lafayette, Lafayette, LA 70504, USA. cengiz@ull.edu
authors.forename: Cengiz
authors.initials: C
authors.lastname: Günay
authors.forename: Anthony S
authors.initials: AS
authors.lastname: Maida
mesh_terms: D007858:Learning / Q000502:physiology; D008959:Models, Neurological