Programming molecular systems to emulate a learning spiking neuron

no code implementations • 9 May 2022 • Jakub Fil, Neil Dalchau, Dominique Chu

Hebbian theory seeks to explain how the neurons in the brain adapt to stimuli, to enable learning.

Parameter Inference with Bifurcation Diagrams

1 code implementation • NeurIPS 2021 • Gregory Szep, Neil Dalchau, Attila Csikasz-Nagy

Estimation of parameters in differential equation models can be achieved by applying learning algorithms to quantitative time-series data.

Time Series Time Series Analysis

Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems

1 code implementation • 28 May 2019 • Geoffrey Roeder, Paul K. Grant, Andrew Phillips, Neil Dalchau, Edward Meeds

Our model class is a generalisation of nonlinear mixed-effects (NLME) dynamical systems, the statistical workhorse for many experimental sciences.

Bayesian Inference Zero-Shot Learning