Lectures related to Attractor Networks: Hopfield Model Generalizations

Explores attractor networks, Hopfield model generalizations, and memory dynamics in computational neuroscience.

Synaptic Plasticity: Online Memory Learning

Explores synaptic plasticity, spike-timing models, and online memory learning challenges in computational neuroscience.

Explores the Stochastic Hopfield model, noisy neurons, firing probabilities, memory retrieval, and overlap equations in attractor networks.

Attractor Networks and Spiking Neurons

Explores attractor networks, spiking neurons, memory data, and realistic networks in neural dynamics.

Scientific Computing in Neuroscience

Explores the history and tools of scientific computing in neuroscience, emphasizing the simulation of neurons and networks.

Introduction to Systems Neuroscience: Memory Systems Overview

Introduces systems neuroscience, focusing on neural circuits, memory systems, and course logistics.

Parameter estimation

Explores parameter estimation in neuron models, focusing on quadratic optimization and linear fit.

Computational Neuroscience: Biophysics & Modeling

Covers the fundamentals of computational neuroscience, focusing on biophysics and modeling.

Generalized Integrate-and-Fire Models

Explores the Generalized Integrate-and-Fire Model and the Nonlinear Integrate-and-Fire Model.

Data-Driven Modeling in Neuroscience: Meenakshi Khosla

By Meenakshi Khosla explores data-driven modeling in large-scale naturalistic neuroscience, focusing on brain activity representation and computational models.

Memory Encoding: Principles and Techniques

Discusses memory encoding principles and techniques to enhance retention and recall.

Modeling Electrophysiology: Different Scales

Covers modeling electrophysiology at different scales, discussing ion channels, single neurons, and microcircuits.

Models and data

Covers the optimization of neuron models for coding and decoding in computational neuroscience.

MathDetour 1: Separation of time scales

Explores the concept of separation of time scales in computational neuroscience and the reduction of detail in two-dimensional neuron models.

AdEx model: Firing patterns and phase plane analysis

Explores the AdEx neuron model, analyzing firing patterns and phase planes.

Stress and Learning: Effects and Implications

Examines the effects of stress on learning and the importance of teacher-student relationships on engagement and achievement.

Introduction to synaptic plasticity

Covers synaptic plasticity, types of synapses, Hebb's postulate, LTP, LTD, and network oscillations in the hippocampus.

Spike-Timing Models of plasticity

Explores Spike-Timing Models of plasticity, including STDP and synaptic traces.

Three definitions of rate code

Discusses three definitions of rate code in computational neuroscience, emphasizing temporal averaging, interspike intervals, and FANO factor.

Leaky Integrate-and-Fire Model

Explores the Leaky Integrate-and-Fire Model in computational neuroscience, emphasizing single neuron dynamics.