Computational Neuroscience

Computational neuroscience is also known as theoretical neuroscience/ mathematical neuroscience. It is a branch of neuroscience that makes use of mathematical models, theoretical analysis and abstractions of brain to understand various principles that involve the development, structure, physiology and cognitive abilities of the nervous system. The main focus of computational neuroscience is on the description of biologically plausible neurons and neural systems and their physiology and dynamics, and hence it is not concerned with biologically unrealistic models that are used in connectionism, machine learning, artificial neuronal networks, artificial intelligence and computational learning. Models in theoretical are aim at capturing the essential features of the biological systems at multiple spatial-temporal scales, from membrane currents, and chemical coupling via network oscillations, columnar and topographic architecture and all the way up to the memory, learning and behaviour of the individual. These computational models help mostly in framing hypotheses that are directly tested by biological or psychological experiments

  • Neural Computation
  • Biological Neuron Models
  • Computational Anatomy
  • Neuroinformatics
  • Neural Coding and Decoding
  • Sensory Processing and Motor Control
  • Computational Clinical Neuroscience

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