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Computational neuroscience is a multidisciplinary field that employs mathematical models, theoretical analysis, and computer simulations to understand the structure, dynamics, and functions of the nervous system and the brain. Here's a comprehensive list of various branches and topics within computational neuroscience:

1. Neural Network Models¶

Artificial Neural Networks
Spiking Neural Networks
Recurrent Neural Networks
Convolutional Neural Networks
Self-Organizing Maps
Hopfield Networks

2. Neuronal Dynamics Models¶

Hodgkin-Huxley Model
Integrate-and-Fire Models
FitzHugh-Nagumo Model
Leaky Integrate-and-Fire Model
Izhikevich Neuron Model
Wilson-Cowan Model

3. Synaptic Plasticity and Learning¶

Hebbian Learning
Spike-Timing-Dependent Plasticity (STDP)
Synaptic Scaling and Homeostasis
Long-Term Potentiation and Depression (LTP/LTD)

4. Neural Coding and Information Theory¶

Rate Coding and Temporal Coding
Population Coding
Information Theory in Neural Systems
Neural Decoding

5. Computational Models of Sensory Systems¶

Visual System Modeling
Auditory System Modeling
Somatosensory System Modeling
Olfactory System Modeling
Gustatory System Modeling

6. Systems and Integrative Neuroscience Models¶

Thalamocortical Circuitry
Basal Ganglia Models
Cerebellar Models
Hippocampal Models
Cortical Column and Network Models

7. Cognitive and Behavioral Neuroscience Models¶

Decision-Making Models
Memory and Learning Models
Attention and Executive Function Models
Emotion and Motivation Models
Language and Semantic Processing Models

8. Computational Neuroanatomy¶

Neural Network Topology
Connectomics
Brain Parcellation and Segmentation
Diffusion Tensor Imaging (DTI) Analysis

9. Neuroinformatics¶

Databases and Data Mining in Neuroscience
Tools for Neural Data Analysis
Brain Atlases and Mapping
Neurogenomics and Transcriptomics

10. Computational Psychopathology¶

Models of Neural Disorders
Computational Psychiatry
Neural Basis of Mental Disorders

11. Neuropharmacology Modeling¶

Computational Models of Drug Effects
Neurotransmitter Systems Modeling
Receptor and Ion Channel Modeling

12. Brain-Computer Interfaces and Neuroprosthetics¶

Decoding Neural Signals
Brain Stimulation and Modulation Models
Neural Control Interfaces

13. Computational Developmental Neuroscience¶

Neural Development and Growth Models
Axonal Pathfinding and Synaptogenesis
Cortical Development and Plasticity

14. Machine Learning and AI in Neuroscience¶

Deep Learning Applications in Neuroscience
Reinforcement Learning and Neural Control
Machine Learning for Neuroimaging Analysis

15. Multi-Scale Modeling¶

Bridging Molecular, Cellular, and Systems-Level Models
Integrative Modeling of Brain Function

Computational neuroscience is a highly interdisciplinary field, leveraging techniques and concepts from neuroscience, physics, mathematics, computer science, and engineering. It plays a crucial role in translating experimental findings into quantitative theories and in driving forward our understanding of the nervous system and brain.

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