Neural Networks: Hierarchical Models and Odor Taxis

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (32)

Page 3 of 4

Building Physical Neural Networks

Discusses challenges in building physical neural networks, focusing on depth, connections, and trainability.

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.

Machine Learning Models for Neuroscience

Explores machine learning models for neuroscience, focusing on understanding brain function and core object recognition through convolutional neural networks.

Engineering Neurons: Optogenetics

Explores the engineering of neurons using light, chemicals, and sound to modulate neural activity and behavior.

Neural Networks: Logic and Applications

Explores the logic of neuronal function, the Perceptron model, deep learning applications, and levels of abstraction in neural models.

The Microcircuits of Striatum in Silico

Explores the reconstruction of a full-scale mouse striatal cellular level model to integrate and interpret striatal data.

The visual system

Explores perceptrons in neural networks and their analogy with the visual system.

Vision: systems & circuits

Explores receptive fields, visual brain areas, evolution-driven differences in visual pathways, and neural mechanisms underlying escape behavior.

Neuronal Dynamics: Random Networks

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