Dear All,

I would like to inform you that today's BCNF event has been cancelled due to unforeseen travel complications encountered by our speaker.

We sincerely apologize for any inconvenience this may cause. We are planning to reschedule the event and welcome Nikola Milićević at a later date.

Thank you for your understanding.

Best regards,

Ildikó Varga

Department Coordinator (Budapest)

Department of Cognitive Science

H-1051 Budapest

Nádor u. 15. FT room 404.

tel: +36-1 327-3000 2941

http://www.ceu.edu

http://cognitivescience.ceu.edu

From: Ildiko Zsoka Varga <VargaI@ceu.edu>
Sent: Tuesday, March 12, 2024 11:19 AM
To: 'talks@cogsci.ceu.edu (talks@cogsci.ceu.edu)' <talks@cogsci.ceu.edu>
Subject: Tomorrow: Budapest Computational Neuroscience Forum - March 13.

Dear All,

The CEU Department of Cognitive Science and the Center for Cognitive Computation (CCC) invites you to the upcoming event of the Budapest Computational Neuroscience Forum.

Speaker: Nikola Milićević, Pennsylvania State University

Title: Sensory systems and combinatorial neural codes

Abstract: Neural activity in sensory areas of the brain is shaped both by the stimulus and by the internal neural dynamics. When the stimulus space is known we can compute receptive fields of neurons. Receptive fields of individual neurons are convex in a number of brain regions (such as the hippocampus, and the visual cortex). The combinatorial neural code are the subsets of co-active neurons for some input to the neural network. Not any combinatorial code is compatible with convex receptive fields. This raises a natural question: how do recurrent networks produce convex codes? Towards this end, we study a recurrent neural network with the Dale’s law architecture.

We describe the combinatorics of equilibria and steady states of neurons in threshold-linear networks that satisfy Dale's law. The combinatorial code of a Dale network is characterized in terms of two conditions: (i) a condition on the network connectivity graph, and (ii) a spectral condition on the synaptic matrix. In the weak synaptic coupling regime, the combinatorial code depends only on the connectivity graph, and not on the synaptic strengths. Moreover, we prove that the combinatorial code of a weakly coupled network is a sublattice, and we provide a learning rule for encoding a sublattice in a weakly coupled excitatory network. Surprisingly, we find that the architecture of a Dale network “enforces” convex code output, in both strong and weak coupling regimes. Finally, we introduce a method inspired by game theory for inferring receptive fields, when the stimulus space is unknown or at least no consensus has been reached as in the case of olfactory systems.

Time: 17:00, Wednesday, 13 March, 2024.

Location: CEU Budapest (1051 Budapest, Nádor u. 15.) N15. room 104.

Zoom: Meeting ID: 931 3000 7576 Passcode: 142434

Should you have any inquiries about the series, please contact Mihály Bányai.

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