Neuronal Dynamics: Aims and Challenges

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Brain Intelligence: Continual Learning of Representational Models

Delves into the continual learning of representational models after deployment, highlighting the limitations of current artificial neural networks.

Leaky Integrate-and-Fire Model

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

Spatial Continuum: Cortex

Explores cortical fields, orientation columns, solution types, and perception in computational neuroscience.

Feedback Mechanisms in Visual Intelligence and Adaptation

Explores the significance of feedback mechanisms in visual intelligence and adaptation processes.

Neuronal Dynamics: Random Networks

Explores the dynamics of neuronal populations, emphasizing random networks and mean-field arguments for connectivity.

Reduction to 2 dimensions: Exploiting similarities

Explores reducing the Hodgkin-Huxley model to 2 dimensions by exploiting similarities between variables and discussing the nonlinear integrate-and-fire model.

Computational Neuroscience: Biophysics & Modeling

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

Introduction primary visual cortex

Introduces the primary visual cortex and explores visual perception and behavior through cells and circuits.

Single Neuron Data Collection Techniques

Covers the methods for studying single neurons, including morphology and electrophysiology profiling.

Neurons and Synapses: Overview

Covers computational neuroscience basics, single neuron models, synapses, and neural variability.