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Computational Neuroscience

(Stanford University - Jaclyn Chen)


- Overview

Computational neuroscience (CNS) (also known as theoretical neuroscience or mathematical neuroscience) is a branch of neuroscience that uses mathematics, computer science, theoretical analysis, and brain abstraction to understand the processes that control the development, structure, physiology, and cognitive abilities of the nervous system. principle.

Computational neuroscience employs computational simulations to validate and solve mathematical models and can therefore be considered a subfield of theoretical neuroscience; however, the two fields are often synonymous. The term mathematical neuroscience is also sometimes used to emphasize the quantitative nature of the field.

  • Computational neuroscience (CNS) involves: 
  • Collecting data
  • Creating computer models based on the brain's electrical patterns and biological functions
  • Using mathematical multi-scale models
  • Performing theoretical analysis
  • Simulations of neural function


CNS can incorporate approaches from: Electrical engineering, Computer science, Physics, Theoretical analysis, Abstractions of the brain. 

CNS is used to understand: 

  • The development
  • Structure
  • Physiology
  • Cognitive abilities
  • Multi-scale models and theories of neural function
  • Cognition and behavior
Research areas in CNS include: Machine learning, Modeling, Artificial intelligence, Neural networks, Memory, Visual intelligence, Audition, Natural language processing, Learning theory. 

- The Human Brain

The human brain is a biological organ, weighing about three pounds (or 1.4 kg), that determines our behaviors, thoughts, emotions and consciousness. Although comprising only 2% of the total body weight, the brain consumes about 20% of the oxygen entering the body. With the expensive energy demand, the brain enables us to perceive and act upon the external world, as well as reflect on our internal thoughts and feelings. 

The brain is actually never at ‘rest’. Brain activities continue around the clock, ranging from functions enabling human–environment interactions to housekeeping during sleep, including processes such as synaptic homeostasis and memory formation. 

Whereas one could argue that sciences in the last century were dominated by physics and molecular biology, in the current century one of our major challenges is to elucidate how the brain works. A full understanding of brain functions and malfunctions is likely the most demanding task we will ever have.


- Computational Neuroscience: A Frontier of the 21st Century 

Computational neuroscience (CNS) (also known as theoretical neuroscience or mathematical neuroscience) is a branch of neuroscience which employs mathematical models, theoretical analysis and abstractions of the brain to understand the principles that govern the development, structure, physiology and cognitive abilities of the nervous system. 

In theory, CNS would be a sub-field of theoretical neuroscience which employs computational simulations to validate and solve the mathematical models. However, since the biologically plausible mathematical models formulated in neuroscience are in most cases too complex to be solved analytically, the two terms are essentially synonyms and are used interchangeably. The term mathematical neuroscience is also used sometimes, to stress the quantitative nature of the field. 

CNS focuses on the description of biologically plausible neurons (and neural systems) 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, AI and computational learning theory; although mutual inspiration exists and sometimes there is no strict limit between fields, with model abstraction in CNS 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.


- Artificial Intelligence (AI) and Neuroscience

AI and neuroscience have closely interacted and inspired each other since the beginning of AI. With the fast developments in deep learning over the last decade, AI has had a substantial impact on many applications in science and society. 

Advances in efficient computing hardware, which are in turn inspired by insights from neuroscience will be instrumental in moving AI beyond narrow applications. 

AI and neuroscience have a complex relationship:

  • AI helps neuroscience: AI can simulate the brain, which helps neuroscientists test hypotheses and detect neurological disorders early. AI can also help with interpreting large datasets and build better models of the human brain.
  • Neuroscience inspires AI: Neurosciences can inspire AI by developing neural networks that mimic the human brain structure. AI systems are modeled after the human brain, using neural networks as "virtual brains".
  • Cognitive science: Cognitive science is a field that combines AI, neuroscience, computer science, and other disciplines to study the human mind. 


AI has many uses in neurology, including:

  • Accurate epilepsy diagnosis
  • Brain mapping
  • Surgical planning
  • Enhancing the diagnosis and treatment of brain diseases 


[More to come ...]

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