Computational Neuroscience Stocks List
Symbol | Grade | Name | % Change | |
---|---|---|---|---|
ALAI | A | Alger AI Enablers & Adopters ETF | 1.12 | |
CRNC | F | Cerence Inc. | -3.86 | |
LTRN | F | Lantern Pharma Inc. | -2.50 | |
GFAI | F | Guardforce AI Co., Limited | -0.89 | |
VHAI | F | Vocodia Holdings Corp. | 0.00 | |
BTAI | F | BioXcel Therapeutics, Inc. | -3.57 |
Related Industries: Biotechnology Security & Protection Services Software - Application Software - Infrastructure
Related ETFs - A few ETFs which own one or more of the above listed Computational Neuroscience stocks.
Symbol | Grade | Name | Weight | |
---|---|---|---|---|
ROBT | D | First Trust Nasdaq Artificial Intelligence and Robotics ETF | 1.41 | |
XSW | B | SPDR S&P Software & Services ETF | 0.17 | |
CARZ | A | First Trust NASDAQ Global Auto Index Fund | 0.1 | |
BOTZ | C | Global X Robotics & Artificial Intelligence Thematic ETF | 0.06 | |
NUSC | D | NuShares ESG Small-Cap ETF | 0.04 |
Compare ETFs
- Computational Neuroscience
Computational neuroscience (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, computational neuroscience 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.Computational neuroscience 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, 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.
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