Lectures related to Analyzing Hebbian Learning Rule

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

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

Synaptic Transmission: Electrical vs Chemical

Compares electrical and chemical synapses, emphasizing their properties and behavior, including the neuromuscular junction and synapses in the CNS.

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

Network Simulation and Activity Dynamics

Explores neural network simulation, activity dynamics, and validation processes to ensure accurate predictions.

Hippocampus Single Cell Models

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.

Engineering Neurons: Light, Chemicals, Sound

Explores optogenetics, chemogenetics, and sonogenetics to engineer neural activity using light, chemicals, and sound.

Synaptic Transmission: Electrical vs. Chemical

Explores the differences between electrical and chemical synapses, synaptic plasticity, and receptor 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

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

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

Explores Hebbian learning, synaptic plasticity, and models for long-term plasticity.

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Covers stylized facts of asset returns, summary statistics, testing for normality, Q-Q plots, and efficient market hypothesis.

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Covers modern neuroscience synthesis, biophysical modeling, and brain building blocks with practical applications.