A description of some concepts explored in an advanced computational neuroscience course. Additionally, includes practical projects from sessions, code is built upon what was provided.
Matlab was the prominent language used throughout the course.
Main professor: Bruno Delord
Partner in projects: Ferdinand Bujanowski
In short, neuroscience is cool, computational neuroscience is cool, both are complex, and what we don't know is greater than what we do know. ...(more to come)
A general list of concepts explored throughout the course, whether theoretical or practical:
- Different types of Modelling, choices, architecture principles
- Computing and learning in biological spiking neural networks (engrams, attractors, hopfield networks)
- Observables of network dynamics (rates, variability, synchrony,..)
- Global dynamics in RNNs (sleeping, pathological, irregular asynchronous regimes)
- Exploring Recurrent and Feed forward Neural Networks (the OG's in the brain not artificial)
- Object long-term memories (static network attractors)
- Lapique's Leaky integrate and fire
- Synaptic plasticity, STDP, BTSP
- Hebbian assemblies and hebbian learning, and different regimes (spontaneous, bistable, saturated, ...)
- Excitation/Inhibition interaction (GabaA, Ampa, Nmda, GabaB)
- Behavioral attractors, decision-making, phasic dopamine
Throughout this course we explored several projects and built upon them continuously.
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Assessing the interaction between pre-synaptic activity and weight distributions
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Simulating activity in an RNN and looking at STDP, learning, selectivity
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Modelling an RNN with fast synaptic conductances, then augmenting it with slow conductances
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Assessing RNN asynchronous irregular dynamics in a Hebbian assembly
Grid Search of Parameter effects on Network dynamics
Exploring different regimes
Note: I do not take full credit for all code in this repository, most of which has been built upon from practical sessions, credit will be added and others linked in upcoming updates