Pedro J. Gonçalves

Experience shapes the transformation of olfactory representations along the cortico-hippocampal pathway

The paper examines how experience transforms olfactory representations along the cortico-hippocampal pathway in mice, showing increased odorant selectivity and population-level representations in hippocampal structures.

Jaxley: Differentiable simulation enables large-scale training of detailed biophysical models of neural dynamics

The paper introduces JO_SCPLOWAXLEYC_SCPLOW, a framework for differentiable simulation of biophysical neuron models, enabling efficient parameter optimization and task-driven training of large-scale models.

An ion channel omnimodel for standardized biophysical neuron modelling

The paper introduces a standardized ion channel model based on Hodgkin-Huxley formulation, demonstrating its ability to fit most voltage-gated models and enable parameter comparison and inference.

Combined statistical-mechanistic modeling links ionchannel genes to physiology of cortical neuron types

The paper develops a hybrid modeling approach to link ion channel gene expression to the electrophysiological properties of cortical neuron types using statistical and mechanistic models.

Memory by a thousand rules: Automated discovery of multi-type plasticity rules reveals variety and degeneracy at the heart of learning.

This study uses automated search algorithms to discover diverse synaptic plasticity rules that support memory tasks and network stability.

Uncertainty mapping and probabilistic tractography using Simulation-Based Inference in diffusion MRI: A comparison with classical Bayes

This paper introduces an SBI framework for diffusion MRI analysis, showing it provides fast and accurate parameter estimation and uncertainty mapping compared to classical Bayesian methods.

Modeling conditional distributions of neural and behavioral data with masked variational autoencoders

The paper introduces a VAE-based method to model conditional distributions between neural and behavioral data, validated on fly and monkey datasets.

Loom response in mouse superior colliculus depends on sensorimotor context

The paper investigates how mice distinguish self-motion from object motion using virtual reality and neural recordings from the superior colliculus, finding that different layers of the superior colliculus encode these signals differently.

Deep inverse modeling reveals dynamic-dependent invariances in neural circuit mechanisms

The paper introduces AutoMIND, a method for inverse modeling of neural circuits, revealing dynamic-dependent invariances in parameter configurations across different neural dynamics.

Decoding movie content from neuronal population activity in the human medial temporal lobe

The paper decodes movie features like characters, settings, and visual transitions from neuronal population activity in the human medial temporal lobe, showing region-specific information encoding.

Indistinguishable network dynamics can emerge from unalike plasticity rules

The paper shows that multiple distinct plasticity rules can produce similar network dynamics, highlighting the need to consider degenerate mechanisms in plasticity studies.

Energy efficient network activity from disparate circuit parameters

The paper investigates how metabolic cost influences neural circuit parameters in the crab pyloric network, finding energy-efficient circuits with diverse conductance combinations.

Robustness in spiking networks: a geometric perspective

The paper explains how spiking neural networks maintain robustness through low-dimensional representations and bounding box integrity under parameter changes.

Sustained Activation of PV+ Interneurons in Core Auditory Cortex Enables Robust Divisive Gain Control for Complex and Naturalistic Stimuli

The paper investigates how sustained activation of parvalbumin-positive interneurons in auditory cortex affects divisive gain control across different stimulus types.

Training deep neural density estimators to identify mechanistic models of neural dynamics

This paper introduces a machine learning method using deep neural density estimators to perform Bayesian inference on neural dynamics models, demonstrating its application to various neural systems.

Integration of sleep homeostasis and navigation in Drosophila

The paper develops a model integrating sleep homeostasis and head direction systems in Drosophila to explain how navigation remains stable despite sleep-related plasticity.