Neuronal Dynamics: Decisions and Simulations

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Surprise, Curiosity and Reward: An Evolutionary Perspective

Explores the evolutionary perspective on surprise, curiosity, and reward, focusing on the role of primary and secondary reward signals.

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.

Statistical Learning: Fundamentals

Introduces the fundamentals of statistical learning, covering supervised learning, decision theory, risk minimization, and overfitting.

Introduction to the simulation of the microcircuit

Introduces the simulation of a hippocampal microcircuit and its key aspects.

Nonlinear Supervised Learning

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

Challenge of even bigger models

Explores simulating large-scale neural network models and optimizing memory efficiency in neural simulations using NEURON and CoreNEURON.

Universal Construction: Algorithm Explanation

Explores a simple universal construction algorithm using ConsentsObjects, highlighting its log-free nature and the uncertainty in operation termination.

Kernel Methods: Neural Networks

Covers the fundamentals of neural networks, focusing on RBF kernels and SVM.

Neural Networks: Multilayer Perceptrons

Covers Multilayer Perceptrons, artificial neurons, activation functions, matrix notation, flexibility, regularization, regression, and classification tasks.

In Silico Neuroscience: Network Simulation

Delves into simulating network dynamics in in silico neuroscience, covering spontaneous and evoked activity, in-vitro and in-vivo simulations, and sensitivity analysis.