Latest results

We study two problems: collective behavior and new formal approaches to learning.

In collective behavior, we study which interations lead to collective decisions. We develop tracking systems, analysis tools and models.

Romero-Ferrero F, Bergomi MG, Hinz RC, Heras FJ, de Polavieja GG. idtracker. ai: tracking all individuals in small or large collectives of unmarked animals. Nature Methods. 2019 Feb;16(2):179-82.

[ journal ] [ pdf ] [ web ] [ gitlab ]

Building on our idea of tracking by identification, see idTracker, here we propose to do the identification using deep nets to scale better with the number of animals. One network self-learns from a video which image blobs are individuals and which crossings. A second network self-learns to distinguish each individual. It is a general purpose open source system of lab animal behavior in any species.

Heras FJ, Romero-Ferrero F, Hinz RC, de Polavieja GG. Deep attention networks reveal the rules of collective motion in zebrafish. PLoS computational biology. 2019 Sep 13;15(9):e1007354.

[ journal ] [ biorxiv ] [ gitlab ]

We use the power of deep nets to have a predictive model of collective behavior but avoid their black-box nature using a concrete modular structure that makes the model understandable. We checked that, when applied to artificial data obtained of agents moving according to some mathematical rules, the method recovers the underlying rules. When applied to data of fish collective behavior obtained with idtracker.ai, we learn that each fish dynamically focuses on different subgroups of other fish depending not only on where they are but also on what direction and speed they have. The focusing on very few animals (down to one) was predicted in our Bayesian framework, [ A. Perez-Escudero and G.G. de Polavieja, PLOS Comp Biol (2011) ]; see also [ S. Arganda, A. Perez-Escudero and G.G. de Polavieja, PNAS (2012) ].

We are also studying how to combine symbolic reasoning with learning from data.

Martin-Maroto F, de Polavieja GG. Algebraic Machine Learning.

[ ArXiv ]

We propose an approach to learning using Model Theory as a symbolic system that learns from data and formal knowledge.

Martin-Maroto F, de Polavieja GG. Finite Atomized Semilattices.

[ ArXiv ]

We give a set of theorems to study algebraic machine learning.

See Algebraic AI for our spin-off company for the algebraic approach to learning.

Useful internal contacts

Lab manager (ordering): Telma Carrilho, telma.carrilho@research.fchampalimaud.org

Human Resources (contract, card, e-mail address): Teresa Carona, teresa.carona@neuro.fchampalimaud.org

Pre-award (grant and fellowship applications): Andreia Tavares, andreia.tavares@research.fchampalimaud.org

Post-award (management of awarded grants): Vanda Vicente, vanda.vicente@fundacaochampalimaud.pt

Operations Manager (general): Catia Feliciano, catia.feliciano@neuro.fchampalimaud.org