Towards trainable bio-robots

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Machine Learning Models for Neuroscience

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

Deep Learning: Convolutional Neural Networks

Covers Convolutional Neural Networks, standard architectures, training techniques, and adversarial examples in deep learning.

Deep Neural Networks

Covers the back-propagation algorithm for deep neural networks and the importance of locality in CNN.

The Hidden Convex Optimization Landscape of Deep Neural Networks

Explores the hidden convex optimization landscape of deep neural networks, showcasing the transition from non-convex to convex models.

Kernel Methods: Neural Networks

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

Miniaturized CMOS Interfaces

Explores miniaturized CMOS interfaces for neural signals processing and neurostimulation.

Ring Attractor Dynamics in Drosophila Brain

Explores ring attractor networks in the Drosophila brain for maintaining heading direction during navigation.

Neural Networks: Training and Activation

Explores neural networks, activation functions, backpropagation, and PyTorch implementation.

Neural Networks Convergence

Explores neural networks convergence through parameter fitting and alternating regret, with a focus on the NeurIPS 2023 event at EPFL.

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.