Skip to main content
Graph
Search
fr
en
Login
Search
All
Categories
Concepts
Courses
Lectures
MOOCs
People
Quizes
Exercises
Publications
Startups
Units
Show all results for
Home
Lecture
Analyzing Hebbian Learning Rule
Graph Chatbot
Related lectures (34)
Neuromorphic Computing: Concepts and Hardware Implementations
Covers neuromorphic computing, challenges in ternary and binary computing, hardware simulations of the brain, and new materials for artificial brain cells.
Neuronal Plasticity: Synaptic Changes and Brain Functioning
Explores neuronal plasticity, synaptic changes, brain functioning, and unconscious states through various mechanisms.
Introduction to synaptic plasticity
MOOC: Simulating a hippocampus micro-circuit
Covers synaptic plasticity, types of synapses, Hebb's postulate, LTP, LTD, and network oscillations in the hippocampus.
Building Physical Neural Networks
Discusses challenges in building physical neural networks, focusing on depth, connections, and trainability.
Hippocampus Single Cell Models
MOOC: Simulating a hippocampus micro-circuit
Delves into the modeling of CA1 pyramidal cells in the hippocampus, revealing their complex computational abilities.
Engineering Neurons: Optogenetics
Explores the engineering of neurons using light, chemicals, and sound to modulate neural activity and behavior.
Modeling Neuronal Activity
Explores modeling neuronal activity, including firing rates, responses to stimuli, and network behavior.
Understanding Synaptic Transmission
Explores synaptic transmission, neurotransmitters, and neural plasticity principles.
Nonlinear Supervised Learning
Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.
Modeling synaptic transmission: Stochastic Dynamics
MOOC: Simulation Neurocience
Explores stochastic synaptic transmission modeling, parameters, inhibitory effects, and synaptic reliability.
Deep Neural Networks: Training and Optimization
Explores deep neural network training, optimization, preventing overfitting, and different network architectures.
Introduction into the module
MOOC: Simulation Neurocience
Introduces nerve cells' electrical activity, focusing on spikes and synaptic potentials.
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.
Models of Long-Term Plasticity: Hebbian Learning
MOOC: Neuronal Dynamics 2- Computational Neuroscience: Neuronal Dynamics of Cognition
Explores Hebbian learning, synaptic plasticity, and models for long-term plasticity.
Modeling the synaptic potential
MOOC: Simulation Neurocience
Explores the modeling of synaptic potentials and the factors influencing their amplitude.
Synaptic transmission: Structure and Mechanisms
MOOC: Simulation Neurocience
Explores the structure and function of synapses, including neurotransmitter diversity and the role of AMPA and NMDA receptors.
Learning and synaptic plasticity
Covers sensory stimulation, learning, synaptic plasticity, and Hebb's postulate in neurorobotics.
Synaptic Plasticity: Online Memory Learning
MOOC: Neuronal Dynamics 2- Computational Neuroscience: Neuronal Dynamics of Cognition
Explores synaptic plasticity, spike-timing models, and online memory learning challenges in computational neuroscience.
Neural Networks: Training and Activation
Explores neural networks, activation functions, backpropagation, and PyTorch implementation.
Introduction to Machine Learning
Provides an overview of Machine Learning, including historical context, key tasks, and real-world applications.
Previous
Page 1 of 2
Next