Lecture

Connectivity and motif analysis

Related lectures (32)

Neural Networks: Hierarchical Models and Odor Taxis

Covers neural function, hierarchical models, odor taxis behaviors, and disparate circuit parameters in 18 slides.

Network Simulation and Activity Dynamics

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

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.

Modeling Neuronal Activity

Explores modeling neuronal activity, including firing rates, responses to stimuli, and network behavior.

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.

Reinforcement Learning Concepts

Covers key concepts in reinforcement learning, neural networks, clustering, and unsupervised learning, emphasizing their applications and challenges.

Nonlinear Supervised Learning

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

Attractor Networks and Spiking Neurons

Explores attractor networks, spiking neurons, memory data, and realistic networks in neural dynamics.

Building Physical Neural Networks

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

Multi-layer Neural Networks

Covers the fundamentals of multi-layer neural networks and the training process of fully connected networks with hidden layers.

Deep Learning Fundamentals

Introduces deep learning, from logistic regression to neural networks, emphasizing the need for handling non-linearly separable data.

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.

Neural Networks: Regression and Classification

Explores neural networks for regression and classification tasks, covering training, regularization, and practical examples.

Neural Signal Compression

Explores analog-to-digital conversion, neural signal optimization, multichannel architectures, and on-chip compression techniques in neuroengineering.

Deep Learning Fundamentals

Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.

Neural Networks: Basics and Applications

Explores neural networks basics, XOR problem, classification, and practical applications like weather data prediction.

Deep Learning: Convolutional Neural Networks

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

Kernel Methods: Neural Networks

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

Neural Networks: Training and Activation

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

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.