Titlebook: Connectionist Models of Learning, Development and Evolution; Proceedings of the S Robert M. French,Jacques P. Sougné Conference proceedings

elucidate

畫(huà)布

A Learning Algorithm for Synfire Chainsd synfire chains develop? What learning procedure could generate such precise temporal chains? How could a connectionist network of spiking neurons leam synfrre chains? An algorithm for a network of spiking neurons that learns synfire chains will be presented.

ALERT

A Biologically Plausible Maturation of an ART Networkpresentations. Presently, we extend Exact ART by adding biologically plausible evolution equations for activation dependent maturation of lateral connections in a SNN [14]. The resulting model is an epigenetic model of both morphological development and cognitive behavioral development.

Ingredient

Coordinating Multiple Sensory Modalities While Learning to Reachefficient reaching strategy, and (3) accelerates the remapping of vision and proprioception after perturbation of the multimodal map. Follow-up analyses of the model provide additional support for our hypothesis, and suggest that touch helps to coordinate vision and proprioception by providing a third, correlated information channel.

誘拐

Facet-Joints

Visual Crowding and Category-Specific Deficits: a Neural Network Modelies, unlike clothing and furniture. These results are in keeping with those from other experimental approaches. Thus, it would appear that the simple interplay between a SOM and 2D images provides a valuable model of one route in visual object recognition.

金盤是高原

Pseudopatterns and dual-network memory models: Advantages and shortcomingsd retrograde amnesia. We discuss a surprising result about how this class of models handles episodic (“snap-shot”) memory — namely, that they seem to be able to handle both episodic and abstract memory — and discuss two other promising areas of research involving these models.

Diastole

A Simple Model Exhibiting Scalar Timinged and shown to give rise to the scalar property. The model is an accumulator consisting of noisy, linear spiking neurons. It is analytically tractable and contains only three parameters. When coupled with simple reinforcement learning it simulates peak procedure experiments and temporal bisection.

媽媽不開(kāi)心

A Revival of Turing’s Forgotten Connectionist Ideas: Exploring Unorganized Machinesas. Turing’s unorganized machines are analyzed and new types of machines are proposed by the authors. An example of a pattern classification task is presented using a Turing net and genetic algorithms for building and training the networks.

變量

https://doi.org/10.1007/978-1-4020-6391-6ies, unlike clothing and furniture. These results are in keeping with those from other experimental approaches. Thus, it would appear that the simple interplay between a SOM and 2D images provides a valuable model of one route in visual object recognition.