About NeuroAI: The last decade has seen phenomenal advances in the fields of machine learning (e.g. deep learning, reinforcement learning, and AI). While these changes have already had considerable impact on most areas of science they hold a particular resonance for neuroscience.

Crucially, AI shares a common lineage with neuroscience and fundamentally machine learning and the brain employ similar computations to process and compress information. For these reasons AI provides a means to emulate neural functions and the circuits supporting them, providing insights to aid our understanding of the brain and cognition.

Equally, AI tools provide a means to discover, segment, and track distinct neural and behavioural states - yielding more efficient experiments and accelerating the pace of discovery. In turn, this understanding feeds back into the design of more effective AI architectures and models.

Up-coming Neuro AI Talk Series

Date : 13th November 2024

Time: 14:00- 15:00

Joining the event: Registration is now closed but you can join the seminar using the joining details below.

The talk will be hosted on Zoom: https://ucl.zoom.us/j/96154908614?pwd=JbnrcqfITAAYVnTfHsc3exMveEMUhT.1

Meeting ID: 961 5490 8614

Passcode: 769263

Speaker: Dr Kanaka Rajan

Talk Title: Brain-Wide Compositionality and Learning Dynamics in Biological Agents

Abstract: Biological agents continually reconcile the internal states of their brain circuits with incoming sensory and environmental evidence to evaluate when and how to act. The brains of biological agents, including animals and humans, exploit many evolutionary innovations, chiefly modularity—observable at the level of anatomically-defined brain regions, cortical layers, and cell types among others—that can be repurposed in a compositional manner to endow the animal with a highly flexible behavioral repertoire. Accordingly, their behaviors show their own modularity, yet such behavioral modules seldom correspond directly to traditional notions of modularity in brains. It remains unclear how to link neural and behavioral modularity in a compositional manner. We propose a comprehensive framework—compositional modes—to identify overarching compositionality spanning specialized submodules, such as brain regions. Our framework directly links the behavioral repertoire with distributed patterns of population activity, brain-wide, at multiple concurrent spatial and temporal scales.

Using whole-brain recordings of zebrafish brains, we introduce an unsupervised pipeline based on neural network models, constrained by biological data, to reveal highly conserved compositional modes across individuals despite the naturalistic (spontaneous or task-independent) nature of their behaviors. These modes provided a scaffolding for other modes that account for the idiosyncratic behavior of each fish. We then demonstrate experimentally that compositional modes can be manipulated in a consistent manner by behavioral and pharmacological perturbations. Our results demonstrate that even natural behavior in different individuals can be decomposed and understood using a relatively small number of neurobehavioral modules—the compositional modes—and elucidate a compositional neural basis of behavior. This approach aligns with recent progress in understanding how reasoning capabilities and internal representational structures develop over the course of learning or training, offering insights into the modularity and flexibility in artificial and biological agents.

Biography: Kanaka Rajan, PhD. is a Computational Neuroscientist, an Associate Professor in the Department of Neurobiology at Harvard Medical School, and founding faculty at the Kempner Institute for the Study of Natural and Artificial Intelligence at Harvard University. Her research seeks to understand how important cognitive functions —such as learning, remembering, and deciding— emerge from the cooperative activity of multi-scale neural processes. Using data from neuroscience experiments, Rajan applies computational frameworks adapted from engineering, mathematics, and physics to uncover integrative theories about the brain that bridge neurobiology and AI.

Contact:

If you have any questions about the event please email: neuroaievents@ucl.ac.uk.

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Previous speakers:

Please visit the UCL NeuroAI website to access recordings of previous talks.