*PSY-221F is the new course number for PSY-265 formerly taught by Greg Ashby*

### Course Description

This is a lecture course that surveys computational neuroscience, which is a branch of neuroscience that employs mathematical models, theoretical analysis, and abstractions of the brain to understand the principles that govern development, structure, physiology, and cognitive abilities of the nervous system.

By the end of the course, you should be able to stimulate model neurons and neural networks as well as analyze basic spike train data. More generally, you should gain the ability to produce a simple model of a dynamical biological system with appropriate differential equations, to write code that will solve the model through time, and interpret the meaning & relevance of the resulting outputs.

### Prerequisites

The formal prerequisite is PSY-221B, but the only part of that course that is necessary is the introduction to matrix algebra.

The actual necessary background includes:

- calculus,
- some prior exposure to matrix algebra,
- some prior exposure to Python.

Desirable, but not strictly necessary:

- prior exposure to differential equations,
- basic knowledge of neuroscience.

### Syllabus

This is a work in progress. We may draw material from Dayan & Abbott (2001) and Neuromatch Academy, tailored to PBS.

Things that will definitely be covered:

- Intro to CompNeuro: concepts, properties of neurons, cell types, …
- Single neuron models: Perceptron, firing rate model, spiking neurons, …
- Network models: 80-20 nets, Hopfield nets, liquid state machines, …
- Plasticity & learning: short & long-term plasticity, …
- Deep learning: CNNs, RNNs, …
- Applications: vision, language, decision-making, …

There will be some light Python programming (example applications, maybe a homework or two). The beginning of the course will see some programming & math tutorials to get everyone up to speed.

I have yet to decide whether there will be a final project or a final exam.

Expect more updates in W2022.