Learning structured representations

Abstract SHRUTI is a connectionist model that demonstrates how a network of neuron-like elements can encode a large body of semantic, episodic, and causal knowledge, and rapidly make decisions and perform explanatory and predictive reasoning. To further ground this model in the functioning of the brain it must be shown that components of the model can be learned in a neurally plausible manner. Previous work has already demonstrated the rapid learning of episodic facts via cortico-hippocampal interactions. Here we discuss how other SHRUTI representations such as causal rules, statistical and semantic knowledge, and categories might be learned.

[1]  Lokendra Shastri,et al.  Combining belief and utility in a structured connectionist agent architecture , 2019, Proceedings of the Twenty-Fourth Annual Conference of the Cognitive Science Society.

[2]  G. Bi,et al.  Synaptic modification by correlated activity: Hebb's postulate revisited. , 2001, Annual review of neuroscience.

[3]  Lokendra Shastri,et al.  A computationally efficient abstraction of long-term potentiation , 2002, Neurocomputing.

[4]  Gary F. Marcus,et al.  Plasticity and Nativism: Towards a Resolution of an Apparent Paradox , 2001, Emergent Neural Computational Architectures Based on Neuroscience.

[5]  Patrick D. Roberts,et al.  Computational Consequences of Temporally Asymmetric Learning Rules: I. Differential Hebbian Learning , 1999, Journal of Computational Neuroscience.

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

[7]  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.

[8]  L. Shastri.  Episodic memory and cortico–hippocampal interactions , 2002, Trends in Cognitive Sciences.

[9]  Lokendra Shastri,et al.  Semantic Networks: An Evidential Formalization and Its Connectionist Realization , 1988 .

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

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

[12]  Lokendra Shastri,et al.  From transient patterns to persistent structures: A model of episodic memory formation via cortico-hippocampal interactions , 2002 .

[13]  Lokendra Shastri,et al.  Probabilistic Inference and Learning in a Connectionist Causal Network , 2000 .

[14]  R. Nicoll,et al.  Long-term potentiation--a decade of progress? , 1999, Science.