Computational Modeling

A new framework for neural processing of dynamic stimuli

Our temporal predictive coding model provides a biologically plausible explanation for how the brain processes and predicts dynamic sensory inputs, bridging the gap between normative theories and neural mechanisms.

Computational Pipeline for Large-Scale Histology

End-to-end pipeline for mult-scale modelling

Statistical Finite Element Analysis

Building a Computational Models for the Autonomic Nervous System Community

Computational Pipeline for Large-Scale Histology

This report proposes a straightforward extension of the predictive coding model to handle dynamic (time-varying) stimuli, using a neural network architecture that remains biologically plausible and simple. The model can infer hidden variables and learn their dynamics from sensory input, as demonstrated on a toy example, and suggests directions for further development and application.

Building a Multi-Scale Model of the COVID-19 Pandemic