Ulises Pereira Obilinovic

Understanding how the dynamics of neural activity patterns across multiple brain regions give rise to behavior is one of the fundamental questions in systems neuroscience. The field seeks an understanding that goes beyond merely simulating and predicting behavior and neural activity —it aims to uncover how neuronal activity, shaped by synaptic interactions, can be distilled into interpretable models with a few key variables that bridge microscopic and mesoscopic scales.

As a Scientist at the Allen Institute for Neural Dynamics, my research focuses on building data-driven models of neural computations using large-scale multimodal datasets. By leveraging statistical physics, dynamical systems, and machine learning, I aim to develop biologically constrained, mechanistic models that uncover the computational principles underlying decision-making, memory, and large-scale neural coordination.

My work integrates theoretical modeling with experimental data, collaborating closely with neuroscientists to derive testable mechanistic theories of brain function. Through this approach, I aim to bridge the gap between high-dimensional neural and anatomical data and fundamental principles governing cognition.

Education

Google scholar and GitHub

Contact: ulises.pereira.o[at]alleninstitute.org

Publications & Preprints

* Denotes co-first authors
  1. U. Pereira-Obilinovic, S. Froudist-Walsh, X.-J. Wang. Cognitive networks interactions through communication subspaces in large-scale models of the neocortex. bioRxiv. [preprint]
  2. K. Mohan, U. Pereira-Obilinovic, S. Srednyak, Y. Amit, N. Brunel, D. Freedman. Visual image familiarity learning at multiple timescales in the primate inferotemporal cortex. bioRxiv. [preprint]
  3. X.-J. Wang, J. Jiang, U. Pereira-Obilinovic. Bifurcation in space: How does functional modularity arise in the cortex made with repeats of a canonical local circuit?. bioRxiv. [preprint]
  4. L. Kuśmierz, U. Pereira-Obilinovic, Z. Lu, D. Mastrovito, S. Mihalas. Hierarchy of chaotic dynamics in random modular networks. Acepted in Physical Review Letters. [paper][featured]
  5. U. Pereira-Obilinovic, H. Hou, K. Svoboda, X.-J. Wang. Brain mechanism of foraging: Reward-dependent synaptic plasticity versus neural integration of values. PNAS, 121 (14) e2318521121, 2024. [paper][code]
  6. U. Pereira-Obilinovic, J. Aljadeff, and N. Brunel. Forgetting leads to chaos in attractor networks. Physical Review X, 13(1), p.011009, 2023. [paper][code][featured]
  7. S. Recanatesi*, U. Pereira-Obilinovic*, M. Murakami, Z. Mainen✝, L. Mazzucato✝. Metastable attractors explain the variable timing of stable behavioral action sequences. Neuron, 110(1):139–153, 2022. [paper][code][featured]
  8. J. Aljadeff, M. Gillett, U. Pereira-Obilinovic, and N. Brunel. From synapse to network: models of informationstorage and retrieval in neural circuits. Current Opinion in Neurobiology, 2021. [paper]
  9. X.-J. Wang, U. Pereira, M.G.P. Rosa, and H. Kennedy. Brain connectomes come of age. Current Opinion in Neurobiology, 2020 [paper]
  10. M. Gillett, U. Pereira and N. Brunel. Characteristics of sequential activity in networks with temporally asymmetric Hebbian learning. PNAS, 2020 [paper][code]
  11. U. Pereira and N. Brunel. Unsupervised learning of persistent and sequential activity. Frontiers in computational neuroscience, 13:97, 2020 [paper][code]
  12. J. Vera, U. Pereira, B. Reynaert, J. Bacigalupo, and M. Sanhueza. Modulation of frequency preference in heterogeneous populations of theta-resonant neurons. Neuroscience, 426:13–32, 2020 [paper]
  13. U. Pereira and N. Brunel. Attractor dynamics in networks with learning rules inferred from in vivo data. Neuron, 99(1):227–238, 2018 [paper][code]
  14. U. Pereira, P. Coullet, and E. Tirapegui. The bogdanov–takens normal form: A minimal model for single neuron dynamics. Entropy, 17(12):7859–7874, 2015 [paper]
  15. J. Vera, M. Pezzoli, U. Pereira, J. Bacigalupo, and M. Sanhueza. Electricalresonance in the θ frequency range in olfactory amygdala neurons. PLoS One, 9(1):e85826, 2014 [paper]