Connectionism and cognitive architecture: a critical analysis
Connections and symbols
CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Induction of finite-state languages using second-order recurrent networks
Neural Computation
Dynamic Memory: A Theory of Reminding and Learning in Computers and People
Dynamic Memory: A Theory of Reminding and Learning in Computers and People
Towards Novel Neuroscience-Inspired Computing
Emergent Neural Computational Architectures Based on Neuroscience - Towards Neuroscience-Inspired Computing
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Connectionist modeling and neuroscience have little common ground or mutual influence. Despite impressive algorithms and analysis within connectionism and neural networks, there has been little influence on neuroscience, which remains primarily an empirical science. This chapter advocates two strategies to increase the interaction between neuroscience and neural networks: (1) focus on emergent properties in neural networks that are apparently "cognitive", (2) take neuroimaging data seriously and develop neural models of dynamics in the both spatial and temporal dimensions.