Temporal binding as an inducer for connectionist recruitment learning over delayed lines

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.

[1]  Cengiz Günay,et al.  Using Temporal Binding for Robust Connectionist Recruitment Learning over Delayed Lines ∗ , 2003 .

[2]  Ad Aertsen,et al.  Stable propagation of synchronous spiking in cortical neural networks , 1999, Nature.

[3]  C. Gunay,et al.  The required measures of phase segregation in distributed cortical processing , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[4]  Wulfram Gerstner,et al.  Spiking neurons , 1999 .

[5]  V. Lamme,et al.  The distinct modes of vision offered by feedforward and recurrent processing , 2000, Trends in Neurosciences.

[6]  W A Wickelgren,et al.  Chunking and consolidation: a theoretical synthesis of semantic networks, configuring in conditioning, S--R versus congenitive learning, normal forgetting, the amnesic syndrome, and the hippocampal arousal system. , 1979, Psychological review.

[7]  W. Kinzel Physics of Neural Networks , 1990 .

[8]  Martin Schneider,et al.  Activity-Dependent Development of Axonal and Dendritic Delays, or, Why Synaptic Transmission Should Be Unreliable , 2002, Neural Computation.

[9]  Stefan Wermter,et al.  Emergent Neural Computational Architectures Based on Neuroscience , 2001, Lecture Notes in Computer Science.

[10]  Michael E Hasselmo,et al.  From biophysics to behavior , 2003, Neuroinformatics.

[11]  Leslie G. Valiant,et al.  Circuits of the mind , 1994 .

[12]  C. Koch,et al.  A brief history of time (constants). , 1996, Cerebral cortex.

[13]  J E Lisman,et al.  Storage of 7 +/- 2 short-term memories in oscillatory subcycles , 1995, Science.

[14]  Andreas Knoblauch,et al.  Pattern separation and synchronization in spiking associative memories and visual areas , 2001, Neural Networks.

[15]  A. Treisman The binding problem , 1996, Current Opinion in Neurobiology.

[16]  R. O’Reilly,et al.  Three forms of binding and their neural substrates: Alternatives to temporal synchrony , 2003 .

[17]  M. Abeles,et al.  Firing Rates and Weil-Timed Events in the Cerebral Cortex , 1994 .

[18]  O Jensen,et al.  Novel lists of 7 +/- 2 known items can be reliably stored in an oscillatory short-term memory network: interaction with long-term memory. , 1996, Learning & memory.

[19]  C. Malsburg Binding in models of perception and brain function , 1995, Current Opinion in Neurobiology.

[20]  W Singer,et al.  Visual feature integration and the temporal correlation hypothesis. , 1995, Annual review of neuroscience.

[21]  Lokendra Shastri,et al.  Biological Grounding of Recruitment Learning and Vicinal Algorithms in Long-Term Potentiation , 2001, Emergent Neural Computational Architectures Based on Neuroscience.

[22]  Axel Cleeremans,et al.  The Unity of Consciousness: Binding, Integration, and Dissociation , 2003 .

[23]  Wayne A. Wickelgren,et al.  Chunking and consolidation: A theoretical synthesis of semantic networks configuring in conditioning , 1979 .

[24]  Wolfgang Maass,et al.  Spiking Neurons , 1998, NC.

[25]  Peter König,et al.  Binding by temporal structure in multiple feature domains of an oscillatory neuronal network , 1994, Biological Cybernetics.

[26]  C. Gunay,et al.  Using temporal binding for connectionist recruitment learning over delayed lines , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..

[27]  Walter Schneider,et al.  Controlled and automatic human information processing: II. Perceptual learning, automatic attending and a general theory. , 1977 .

[28]  David Bailey,et al.  Layered Hybrid Connectionist Models for Cognitive Science , 1998, Hybrid Neural Systems.

[29]  Jerome A. Feldman,et al.  Dynamic connections in neural networks , 1990, Biological Cybernetics.

[30]  L. Shastri,et al.  From simple associations to systematic reasoning: A connectionist representation of rules, variables and dynamic bindings using temporal synchrony , 1993, Behavioral and Brain Sciences.

[31]  Deliang Wang,et al.  Global competition and local cooperation in a network of neural oscillators , 1995 .