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Computational Neuroscience Journal Club

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  • UserGeorgia Turner and Falvia Mancini
  • ClockTuesday 23 November 2021, 13:30-15:00
  • HouseOnline on Zoom.

If you have a question about this talk, please contact Jake Stroud.

Please join us for our fortnightly journal club online via zoom where two presenters will jointly present a topic together. The next topic is ‘Inference and knowledge transfer of latent task structure’ presented by Georgia Turner and Flavia Mancini.

Zoom information: Meeting ID: 849 5832 1096 Passcode: 506576

Summary: In order to decide how to act, we need first to identify the underlying causal structure of our environment. However, this underlying structure is not directly observable. An agent must first infer which hidden state it is in from noisy sensory information, and secondly, represent this state in a way which enables inference of the relevant action policy, and re-use this knowledge when in a new situation with the same underlying causal structure. Here, we present two papers that study how humans infer and represent latent states. The first paper shows how hidden structure is inferred using information from a temporal sequence, and may then be used in inductive reasoning (Pudhiyidath et al., 2021). The second paper shows how structural knowledge may be represented as abstract basis sets, which can then be re-deployed when a new situation requiring a similar basis set arises (Mark et al., 2020).

Further reading: Pudhiyidath, A., Morton, N. W., Duran, R. V., Schapiro, A. C., Momennejad, I., Hinojosa-Rowland, D. M., ... & Preston, A. R. (2021). Representations of temporal community structure in hippocampus and precuneus predict inductive reasoning decisions. bioRxiv.

Mark, S., Moran, R., Parr, T., Kennerley, S. W., & Behrens, T. E. (2020). Transferring structural knowledge across cognitive maps in humans and models. Nature communications, 11(1), 1-12.

Baram, A. B., Muller, T. H., Nili, H., Garvert, M. M., & Behrens, T. E. J. (2021). Entorhinal and ventromedial prefrontal cortices abstract and generalize the structure of reinforcement learning problems. Neuron, 109(4), 713-723.

This talk is part of the Computational Neuroscience series.

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