# Computational Neuroscience

> Computational neuroscience constructs mathematical and computational models of neural circuits and systems to understand how the brain processes information, generates behavior, and learns. At the single-neuron level, conductance-based models capture the biophysics of spike generation; at the network level, attractor dynamics explain working memory and decision-making. Large-scale connectomics projects map the synaptic wiring of entire neural circuits at electron-microscope resolution, providing ground-truth data for model validation. Modern recording technologies capturing thousands of neurons simultaneously are driving a renaissance in understanding distributed computation in the cortex, with implications for brain-computer interfaces and AI architectures.

*Source: [https://selltoscientists.com/fields/computational-neuroscience/](https://selltoscientists.com/fields/computational-neuroscience/)*

**Researcher count:** 19,000
**Average funding:** $580K

## Subfields

- Neural Coding
- Network Dynamics
- Synaptic Plasticity Models
- Sensory Processing
- Decision-Making Circuits

## Key technologies

- Multi-Electrode Arrays
- Two-Photon Calcium Imaging
- Hodgkin-Huxley Models
- Spiking Neural Networks
- Connectomics

## Top institutions

- Salk Institute
- Champalimaud Centre
- EPFL Blue Brain Project
- Allen Institute for Brain Science
- University College London

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