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Computational neuroscience

1. Historical Development of Computational Neuroscience: – Term ‘computational neuroscience’ introduced by Eric L. Schwartz in 1985 – First annual open international meeting on Computational […]

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1. Historical Development of Computational Neuroscience:
– Term ‘computational neuroscience’ introduced by Eric L. Schwartz in 1985
– First annual open international meeting on Computational Neuroscience organized in 1989
– First graduate educational program in computational neuroscience at California Institute of Technology in 1985
– Historical roots trace back to work of Lapicque, Hodgkin & Huxley, Hubel and Wiesel, and David Marr
– David Marr focused on interactions between neurons for information processing

2. Major Topics in Computational Neuroscience:
– Single-neuron modeling including Hodgkin and Huxley’s model
– Modeling neuron-glia interactions and glial cells’ role in regulating neuronal activity
– Development, axonal patterning, and guidance
– Sensory processing, motor control, memory, and synaptic plasticity
– Behaviors of networks and visual attention, identification, and categorization

3. Technology and Tools in Computational Neuroscience:
– Neuromorphic computing and its advantages
– Key figures in computational neuroscience and their contributions
– Neural network research and important research papers
– Resources and tools like BRIAN, GENESIS, and NEST for neural simulation

4. Computational Psychiatry and Neuroimaging:
– Predicting neurological outcomes after cardiac arrest
– Combining transient statistical markers to predict brain sensitivity to anesthesia
– Multiscale brain modeling and predicting Alzheimer’s disease
– Frameworks for consciousness and synaptic connectivity
– The Human Brain Project and neuromorphic computing

5. Neural Dynamics, Systems, and Future Directions:
– Neuronal dynamics and theoretical neuroscience
– Understanding vision and membrane current
– AREADNE conferences promoting collaboration and knowledge exchange
– Key topics in computational neuroscience and its impact on brain function
– Future directions including AI integration, interdisciplinary collaborations, brain-computer interfaces, and ethical considerations

Computational neuroscience (Wikipedia)

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

Computational neuroscience employs computational simulations to validate and solve mathematical models, and so can be seen as a sub-field of theoretical neuroscience; however, the two fields are often synonymous. 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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