Neurorobotics: Sensory Representations and Learning Principles

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Deep Neural Networks: Training and Optimization

Explores deep neural network training, optimization, preventing overfitting, and different network architectures.

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.

Analyzing Hebbian Learning Rule

Explores rate-based Hebbian learning, covariance rules, and weight vector growth in neural networks.

Nonlinear Supervised Learning

Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.

Building Physical Neural Networks

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

Spike-Timing Models of plasticity

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

Models of Long-Term Plasticity: Hebbian Learning

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

Financial Time Series Analysis

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

Neural Networks: Two Layers Neural Network

Covers the basics of neural networks, focusing on the development from two layers neural networks to deep neural networks.

Feed-forward Networks

Introduces feed-forward networks, covering neural network structure, training, activation functions, and optimization, with applications in forecasting and finance.