A (Somewhat) New Solution to the Variable Binding Problem

To perform automatic, unconscious inference, the human brain must solve the binding problem by correctly grouping properties with objects. Temporal binding models like SHRUTI already suggest much of how this might be done in a connectionist and localist way by using temporal synchrony. We propose a set of alternatives to temporal synchrony mechanisms that instead use short signatures. This serves two functions: it allows us to explore an additional biologically plausible alternative, and it allows us to extend and improve the capabilities of these models. These extensions model the human ability to both perform unification and handle multiple instantiations of logical terms. To verify our model's feasibility, we simulate it with a computer system modeling simple, neuron-like computations.

[1]  Scott E. Fahlman Marker-Passing Inference in the Scone Knowledge-Base System , 2006, KSEM.

[2]  G. A. Miller The magical number seven plus or minus two: some limits on our capacity for processing information. , 1956, Psychological review.

[3]  M. Page Connectionist modelling in psychology: A localist manifesto , 2000, Behavioral and Brain Sciences.

[4]  Dana H. Ballard Parallel Logical Inference and Energy Minimization , 1986, AAAI.

[5]  Z. Harris,et al.  Foundations of Language , 1940 .

[6]  F. van der Velde,et al.  Neural blackboard architectures of combinatorial structures in cognition , 2006, Behavioral and Brain Sciences.

[7]  A. Pouget,et al.  Neural correlations, population coding and computation , 2006, Nature Reviews Neuroscience.

[8]  Ron Sun On Variable Binding in Connectionist Networks , 1992 .

[9]  John K. Tsotsos,et al.  Modeling Visual Attention via Selective Tuning , 1995, Artif. Intell..

[10]  N. Cowan The magical number 4 in short-term memory: A reconsideration of mental storage capacity , 2001, Behavioral and Brain Sciences.

[11]  Lokendra Shastri,et al.  Multiple instantiation and rule mediation in SHRUTI , 2004, Connect. Sci..

[12]  M. Young,et al.  Correlations, feature‐binding and population coding in primary visual cortex , 2003, Neuroreport.

[13]  Michael N. Shadlen,et al.  Synchrony Unbound A Critical Evaluation of the Temporal Binding Hypothesis , 1999, Neuron.

[14]  Joachim Diederich,et al.  Connectionist Recruitment Learning , 1988, ECAI.

[15]  T. Sejnowski,et al.  Correlated neuronal activity and the flow of neural information , 2001, Nature Reviews Neuroscience.

[16]  Jerome Feldman,et al.  Ecological expected utility and the mythical neural code , 2009, Cognitive Neurodynamics.

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

[18]  R. Jackendoff Foundations of Language: Brain, Meaning, Grammar, Evolution , 2002 .

[19]  Lokendra Shastri Types and Quantifiers in SHRUTI: A Connectionist Model of Rapid Reasoning and Relational Processing , 1998, Hybrid Neural Systems.

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

[21]  C. Chabris,et al.  Gorillas in Our Midst: Sustained Inattentional Blindness for Dynamic Events , 1999, Perception.

[22]  E. Spelke,et al.  Spatiotemporal continuity, smoothness of motion and object identity in infancy , 1995 .

[23]  Leslie G. Valiant,et al.  Memorization and Association on a Realistic Neural Model , 2005, Neural Computation.

[24]  P. Milner A model for visual shape recognition. , 1974, Psychological review.

[25]  Antony Browne,et al.  Connectionist variable binding , 1999, Expert Syst. J. Knowl. Eng..

[26]  Hilbert J. Kappen,et al.  Dynamic linking in stochastic networks , 1995 .

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

[28]  Lokendra Shastri,et al.  A computational model of episodic memory formation in the hippocampal system , 2001, Neurocomputing.

[29]  Lokendra Shastri,et al.  Advances in SHRUTI—A Neurally Motivated Model of Relational Knowledge Representation and Rapid Inference Using Temporal Synchrony , 1999, Applied Intelligence.

[30]  P. Glimcher decisions, uncertainty and the brain , 2003 .

[31]  Lokendra Shastri,et al.  Comparing the neural blackboard and the temporal synchrony-based SHRUTI architectures , 2006, Behavioral and Brain Sciences.

[32]  Jerome A. Feldman,et al.  A (Somewhat) New Solution to the Binding Problem , 2006 .