Computational Neuroscience Essentials

This module introduces the concept of associative memory and the Hopfield model in computational neuroscience. Students will learn about the mathematical foundations of neural networks that can store and retrieve patterns, simulating memory processes in the brain.

This module explores attractor networks and their implementation using spiking neurons. Students will learn how to model more biologically realistic neural networks that exhibit complex dynamics and can maintain stable states, relating to memory and cognitive processes.

This module covers the analysis of large neuronal populations using mean-field theory. Students will learn techniques to simplify the complex dynamics of many interacting neurons into manageable mathematical models, providing insights into emergent behaviors in neural systems.

This module focuses on mathematical models of perception using cortical field models. Students will learn how to represent and analyze the spatiotemporal dynamics of neural activity in the cortex, relating these patterns to perceptual processes.

This module explores mathematical models of decision-making processes in the brain. Students will learn about competitive dynamics in neural networks and how they can lead to decision-making behaviors, integrating concepts from previous modules to understand higher cognitive functions.

This final module covers synaptic plasticity and learning in neural networks. Students will learn about mathematical models of synaptic changes and how they relate to learning and memory formation, tying together the concepts from previous modules to understand adaptive brain function.