How brains are built: Principles of computational neuroscience
Richard Granger

TL;DR
This paper discusses the principles of computational neuroscience, emphasizing understanding brain functions through simulation to achieve deep scientific insight and potential engineering applications.
Contribution
It articulates the foundational goals of computational neuroscience as a means to fully understand and simulate brain functions, bridging science and engineering.
Findings
Highlights the importance of simulation for understanding brain mechanisms.
Defines computational neuroscience as studying the principles underlying brain function.
Connects brain understanding to potential engineering and medical applications.
Abstract
'If I cannot build it, I do not understand it.' So said Nobel laureate Richard Feynman, and by his metric, we understand a bit about physics, less about chemistry, and almost nothing about biology. When we fully understand a phenomenon, we can specify its entire sequence of events, causes, and effects so completely that it is possible to fully simulate it, with all its internal mechanisms intact. Achieving that level of understanding is rare. It is commensurate with constructing a full design for a machine that could serve as a stand-in for the thing being studied. To understand a phenomenon sufficiently to fully simulate it is to understand it computationally. 'Computation' does not refer to computers per se. Rather, it refers to the underlying principles and methods that make them work. As Turing Award recipient Edsger Dijkstra said, computational science 'is no more about…
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Taxonomy
TopicsNeural dynamics and brain function · EEG and Brain-Computer Interfaces · Memory and Neural Mechanisms
