Ulises Pereira

The paper introduces a mechanistic model to infer control parameters of neural circuits from single-trial activity, showing how internal states shape dynamics across trials.

The paper shows that neural activity in non-task-coding dimensions can drive behavior through transient amplification, challenging the assumption that only task-relevant dimensions are behaviorally significant.

The paper develops a model of neocortex-wide neural activity to explain cognitive network interactions and dynamic routing during cognition.

The paper investigates how neural activity in the primate inferotemporal cortex changes with visual familiarity over multiple timescales, finding that both slow and fast plasticity rules contribute to learning.

The paper shows that bifurcations in cortical space create functional modules for specific cognitive tasks, with timescale patterns matching experimental observations.

This study compares two models of action value encoding in foraging behavior and finds the synaptic model more robust based on experimental predictions.

The paper uses hidden Markov models to show metastable attractors explain variable timing in stable behavioral action sequences in rats.

The paper investigates how temporally asymmetric Hebbian learning in neural networks generates sequential activity that matches experimental observations.

This paper investigates how a neural network with plastic synapses can learn persistent and sequential activity through unsupervised learning rules based on input statistics.

The paper infers learning rules and pattern distributions from in vivo data to model attractor dynamics in inferior temporal cortex, revealing optimized storage of attractor states.

The paper shows that neuronal resonance in the theta frequency range is dynamically modulated by changes in input resistance, affecting firing frequency and timing.