Bio-inspired computing

Applied sciences
Information engineering
Machine learning
Artificial neural networks

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Related lectures (12)

Bio-Inspired Learning: Neural Networks, Genetic Algorithms

Explores bio-inspired learning with neural networks and genetic algorithms, covering structure, training, and practical applications.

Biologically Inspired Computation

Explores the history and challenges of biologically inspired computation, focusing on building intelligent machines and understanding brain function.

General Introduction to Artificial Neural Networks

Covers the history and inspiration behind artificial neural networks, the structure of neurons, learning through synaptic connections, and the mathematical description of artificial neurons.

Challenge of even bigger models

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

Neural Architectures for Embodied AI and Cognition

Explores neural architectures for embodied AI, cognitive systems, and the integration of computing and robotics.

Neuromorphic Computing: Challenges & Future

Compares digital and neuromorphic computing, highlighting challenges and future prospects in the field.

Integrative Benchmarking: Advancing System Models of Human Intelligence

Explores advancing system models of human intelligence through integrative benchmarking and the importance of Brain-Score for fair model comparisons.

Building Physical Neural Networks

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

Engineering in the Age of AI: Innovations and Challenges

Examines the transformative impact of AI on engineering disciplines and the associated challenges.

Computer Vision: Historical Insights and Project Inspirations

Explores the historical development of computer vision and inspires innovative project ideas.

Leveraging Big Data Analytics

Explores key strategies for using big data analytics in managing urban infrastructures.

Learning to Find a Goal

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

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