10 citations · 24 across the 5 of their papers we have counts for
6 papers
Programming molecular systems to emulate a learning spiking neuron
Jakub Fil, Neil Dalchau, Dominique Chu
Hebbian theory seeks to explain how the neurons in the brain adapt to stimuli, to enable learning. An interesting feature of Hebbian learning is that it is an unsupervised method a…
Parameter Inference with Bifurcation Diagrams
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. However, sometimes it is only possible to…
Turing Patterning in Stratified Domains
Andrew L. Krause, Václav Klika, Jacob Halatek +4
Reaction-diffusion processes across layered media arise in several scientific domains such as pattern-forming E. coli on agar substrates, epidermal-mesenchymal coupling in developm…
Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems
Geoffrey Roeder, Paul K. Grant, Andrew Phillips +2
We introduce a flexible, scalable Bayesian inference framework for nonlinear dynamical systems characterised by distinct and hierarchical variability at the individual, group, and…
Beyond activator-inhibitor networks: the generalised Turing mechanism
Stephen Smith, Neil Dalchau
The Turing patterning mechanism is believed to underly the formation of repetitive structures in development, such as zebrafish stripes and mammalian digits, but it has proved diff…
Synthesizing and tuning chemical reaction networks with specified behaviours
Neil Dalchau, Niall Murphy, Rasmus Petersen +1
We consider how to generate chemical reaction networks (CRNs) from functional specifications. We propose a two-stage approach that combines synthesis by satisfiability modulo theor…