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Topic: architecture

Seminar
2 seminars
Job
1 job

In Computational Neuroscience and Architectural Engineering

Job · Engineering

Doctoral Fellowships in Architecture and Technology

Closes

Pursue doctoral research connecting architecture and technology within the Institute of Technology in Architecture. Applicants should identify relevant professorships and demonstrate how their research interests fit the institute. The programme offers three and a half years of funding through full-time employment with a monthly salary. Incomplete application packages will not be considered. Apply through the ETH online recruitment portal. Application deadline: 2027-01-31.

Seminar · Deep Learning

Use case determines the validity of neural systems comparisons

Erin Grant · Gatsby Computational Neuroscience Unit & Sainsbury Wellcome Centre at University College London

Wed, Oct 16, 2024 · 13:00 UTC

Deep learning provides new data-driven tools to relate neural activity to perception and cognition, aiding scientists in developing theories of neural computation that increasingly resemble biological systems both at the level of behavior and of neural activity. But what in a deep neural network should correspond to what in a biological system? This question is addressed implicitly in the use of comparison measures that relate specific neural or behavioral dimensions via a particular functional form. However, distinct comparison methodologies can give conflicting results in recovering even a k

Seminar · Computational Neuroscience

Sparse expansion in cerebellum favours learning speed and performance in the context of motor control

Adriana Perez Rotondo · University of Cambridge

Wed, Apr 14, 2021 · 17:30 UTC

The cerebellum contains more than half of the brain’s neurons and it is essential for motor control. Its neural circuits have a distinctive architecture comprised of a large, sparse expansion from the input mossy fibres to the granule cell layer. For years, theories of how cerebellar architectural features relate to cerebellar function have been formulated. It has been shown that some of these features can facilitate pattern separation. However, these theories don’t consider the need for it to learn fast in order to control smooth and accurate movements. Here, we confront this gap. This talk w