Budapest Computational Neuroscience Forum

The Budapest Computational Neuroscience Forum is a series of informal monthly meetings of Budapest-based computational neuroscientists and computational cognitive scientists with the aim of facilitating discussion and cooperation among researchers working in different institutes and giving an opportunity to students to present their work and get to know the comunity. Originally started in 2007, restarted in 2017 and then again in 2023 the Forum is now regularly hosted by Central European University, and followed by a social event, both open to anyone interested.

Events of the Forum are advertised on a mailing list. If you wish to be on this list or have any inquiries about the series, contact Mihály Bányai.

Upcoming meeting:

Time: 17:00, March 13, 2024. 

Location: CEU, 1051 Bp. Nádor u. 15, Room 104

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.

Recent meetings of the Forum:

January 31, 2024. Zoltán Somogyvári (Wigner Institute): Seeing beyond the spikes: reconstructing the complete spatiotemporal membrane potential distribution from paired intra- and extracellular recordings

December 12, 2023. Máté Lengyel (Cambridge/CEU): Optimal information loading into working memory explains dynamic coding in prefrontal cortex. Recording here.

November 15, 2023. András Ecker (EPFL): Long-term plasticity induces sparse and specific synaptic changes in a biophysically detailed cortical model

October 25, 2023. Ferenc Csikor (Wigner Institute): Top-down perceptual inference shaping the activity of early visual cortex

October 4, 2023. Gábor Lengyel (University of Rochester): A General Method for Testing Bayesian Models Using Neural Data

September 1, 2023. Emmanuel Procyk (INSERM Lyon): Prefrontal neuronal dynamics, timescales, and behavioural flexibility

May 31, 2023. Atilla B. Kelemen (KOKI): Geometry of remapping in the hippocampus reflects task structure

April 26, 2023. Anna Székely (Wigner Institute): Identifying transfer learning in the reshaping of inductive biases

March 29, 2023. Merse Előd Gáspár (CEU): Discovering the internal predictive model of infants based on eye movements