Artificial Neural Networks as Models of Neural Information Processing

Front Cover
Marcel van Gerven, Sander Bohte
Frontiers Media SA, Feb 1, 2018

 Modern neural networks gave rise to major breakthroughs in several research areas. In neuroscience, we are witnessing a reappraisal of neural network theory and its relevance for understanding information processing in biological systems. The research presented in this book provides various perspectives on the use of artificial neural networks as models of neural information processing. We consider the biological plausibility of neural networks, performance improvements, spiking neural networks and the use of neural networks for understanding brain function.

 

Contents

Artificial Neural Networks as Models of Neural Information Processing
5
Computational Foundations of Natural Intelligence
7
Toward an Integration of Deep Learning and Neuroscience
31
Bridging the Gap between EnergyBased Models and Backpropagation
72
Models of Acetylcholine and Dopamine Signals Differentially Improve Neural Representations
85
Hierarchical Chunking of Sequential Memory on Neuromorphic Ar
103
On the Maximum Storage Capacity of the Hopfield Model
115
Representational Distance Learning for Deep Neural Networks
124
Estimating the Information Extracted by a Single Spiking Neuron from a Continuous Input Time Series
134
Implementing Signature Neural Networks with Spiking Neurons
149
Mechanisms of WinnerTakeAll and Group Selection in Neuronal Spiking Networks
166
A Case Study on Visual Space Coding
177
Hierarchical Neural Representation of Dreamed Objects Revealed by Brain Decoding with Deep Neural Network Features
194
Modeling the Dynamics of Human Brain Activity with Recurrent Neural Networks
205
Back Cover
219
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