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Computational Neuroscience

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Computational neuroscience is the field of science in which mathematical tools and theories are used to investigate brain function. It can also incorporate multiple approaches from electrical engineering, computer science and physics in order to understand how the nervous system processes information in the brain.

Computational neuroscience is the only field that can help us understand how we’re able to think and process information in our brain. The overall goal of computational neuroscience is to explain how electrical and chemical signals are used in the brain to represent and process information. It explains the biophysical mechanisms of computation in neurons, computer simulations of neural circuits, and models of learning.

Computational neuroscience focuses on the description of biologically plausible neurons and their physiology and dynamics, and it is therefore not directly concerned with biologically unrealistic models used in connectionism, control theory, cybernetics, quantitative psychology, machine learning, artificial neural networks, artificial intelligence and computational learning theory; although mutual inspiration exists and sometimes there is no strict limit between fields, with model abstraction in computational neuroscience depending on research scope and the granularity at which biological entities are analyzed.

Models in theoretical neuroscience are aimed at capturing the essential features of the biological system at multiple spatial-temporal scales, from membrane currents, and chemical coupling via network oscillations, columnar and topographic architecture, nuclei, all the way up to psychological faculties like memory, learning and behavior. These computational models frame hypotheses that can be directly tested by biological or psychological experiments.

Advances in experimental techniques and computational power allowing researchers to gather anatomical and electrophysiological data at unprecedented levels of detail have fostered the development of increasingly complex models in computational neuroscience. Large-scale, biophysically detailed cell models pose a particular set of computational challenges, and this has led to the development of a number of domain-specific simulators. At the other level of detail, the ever growing variety of point neuron models increases the implementation barrier even for those based on the relatively simple integrate-and-fire neuron model. Independently of the model complexity, all modeling methods crucially depend on an efficient and accurate transformation of mathematical model descriptions into efficiently executable code. Neuroscientists usually publish model descriptions in terms of the mathematical equations underlying them. However, actually simulating them needs they be translated into the code. This can cause problems because errors may be introduced if this process is carried out by hand, and code written by neuroscientists may not be very computationally efficient. Furthermore, the translated code might be generated for different hardware platforms, operating system variants or even written in different languages and thus cannot easily be combined or even compared. 

There are many fields that depend on computational neuroscience; a few are enlisted below,

  • Deep Learning
  • Artificial Intelligence
  • Machine Learning
  • Human psychology
  • Medical sciences
  • Mental models
  • Computational anatomy
  • Information theory

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