Modeling the role of hippocampus in spatial navigation

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Related lectures (32)

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Sequence to Sequence Models: Overview and Attention Mechanisms

Explores sequence to sequence models, attention mechanisms, and their role in addressing model limitations and improving interpretability.

Nonlinear Supervised Learning

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

Neural Networks: Hierarchical Models and Odor Taxis

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

Transformers: Pretraining and Decoding Techniques

Covers advanced transformer concepts, focusing on pretraining and decoding techniques in NLP.

Liquid Networks for Learning Control

Explores Liquid Networks for Learning Control in autonomous systems, emphasizing end-to-end learning and robust performance.

Language Models: From Theory to Computation

Explores the mathematics of language models, covering architecture design, pre-training, and fine-tuning, emphasizing the importance of pre-training and fine-tuning for various tasks.

Neural Integral Equations: Modeling Real-World Systems

Explores Neural Integral Equations for modeling real-world systems using non-local functional equations and deep neural networks.

Pre-Training: BiLSTM and Transformer

Delves into pre-training BiLSTM and Transformer models for NLP tasks, showcasing their effectiveness and applications.

Learning to Find a Goal

Delves into a biologically inspired version of Reinforcement Learning, focusing on maze navigation and the implementation of spiking neurons.

Understanding Machine Learning: Exactly Solvable Models

Explores the statistical mechanics of learning, focusing on neural networks' mysteries and computational challenges.