About this book
This thesis addresses one of the most fundamental challenges for modern science: how can the brain as a network of neurons process information, how can it create and store internal models of our world, and how can it infer conclusions from ambiguous data? The author addresses these questions with the rigorous language of mathematics and theoretical physics, an approach that requires a high degree of abstraction to transfer results of wet lab biology to formal models.
The thesis offers an essential resource for newcomers to the field and an inspiration for scientists working in theoretical neuroscience and the future of computing.
Theoretical Neuroscience Computational Neuroscience Neuromorphic Hardware Neural Network Theory Neuronal Dynamics Abstract Spiking Neuron Models Spike and Rate Codes Neural Sampling Bayesian Inference Deep Learning Architectures
- DOI https://doi.org/10.1007/978-3-319-39552-4
- Copyright Information Springer International Publishing Switzerland 2016
- Publisher Name Springer, Cham
- eBook Packages Physics and Astronomy
- Print ISBN 978-3-319-39551-7
- Online ISBN 978-3-319-39552-4
- Series Print ISSN 2190-5053
- Series Online ISSN 2190-5061
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