Dan Goodman

Unifying principles of intelligent systems.

Marcus Ghosh

Multisensory integration in biological and simulated neural networks.

Danyal Akarca

Principles of biological and artificial cognition.

Yang Chu

Principles of neural computation and simulation techniques

Gabriel Béna

Modularity of neural systems.

Swathi Anil

Models of temporal multisensory processing (visiting PhD student).

Greta Horvathova

Information bottlenecks in intelligent systems.

Jatin Sharma

Interpretable machine learning and neuroscience.

The Neural Reckoning Group is led by Dan Goodman at Imperial College London.

We aim to find unifying principles underlying intelligent systems, including biological systems such as the brain and artificial systems. We use theoretical and computational approaches.

We are particularly interested in spiking neural networks and the role they play in sensory processing. Machine learning is essential to our work, as we believe that only by understanding how the brain copes with messy, real-world complexity can we hope to understand what makes it unique. A key part of our work is neuroinformatics, building open source software packages to make our methods freely available to all. For a brief overview of our interests, see the selection of papers, software and organisations below.

A good place to start to get a feel for our current topics is Dan Goodman's Brain Inspired podcast interview.

We maintain a short list of resources for learning computational neuroscience that might be useful to students and people new to the field.