New preprint: Neural Spiking for Causal InferenceHow do neurons learn their effect on downstream reward, so that they can update their synaptic weights? An important problem known as the credit assignment p...

Causal inference algorithms for learning in neural networksA number of challenges in both machine learning (ML) and neuroscience are related to causation. In ML, causality relates to issues of transfer and generaliza...

What is computational neuroscience?Recently I had the opportunity to give a talk to the undergraduate student physics group at Western University. It was a fun exercise to reflect my area of r...

New paper: Towards biologically plausible deep learning: learning to solve the credit assigment problemExcited to present this paper in collaboration with Prashanth Prakash and Konrad Kording at ICLR 2020 main meeting:

New paper: How to get AI to apply causal reasoning like us – transferring from observation to actionA super important problem in reinforcement learning, and machine learning more generally, is how to improve causal reasoning. I think a key part of the solut...

New paper: Reconfiguring motor circuits for a joint manual and BCI taskWas excited to collaborate with Adrienne Fairhall and Chet Moritz at UW on advancing BCI technology with this ‘dual control’ BCI prototype.

New preprint: Optimizing policies based on thresholdsHere Sofia Triantafillou, Konrad Kording and I present a new method for optimizing policies that are based on simple thresholds, using theory from contextual...

The intertwined histories of computer science and the brainDoing the rounds a while was an article posted on Aeon about how the brain is not a computer (here). How there is a fundamental difference between computers ...

Introduction to statistical causal inferenceA brief overview of how causal inference and causal effects are formalized.

MathJax, Jekyll and github pagesIntegrating MathJax with Jekyll is a very convenient way of typesetting mathematics in a blog hosted on github pages. There are a few guides online, which we...

On the relation between maximum likelihood and KL divergenceIn this post I describe some of the theory of maximum likelihood estimation (MLE), highlighting its relation to information theory. In a later post I will de...

Installing NEURON with python and NeuronvisioSince it took some time, I’m going to describe the steps I took to install NEURON with support for python and the 3D visualization tool neuronvisio. I’m runn...

Path integrals and SDEs in neuroscience – part twoIn the previous post we defined path integrals through a simple ‘time-slicing’ approach. And used them to compute moments of simple stochastic DEs. In this f...

Path integrals and SDEs in neuroscienceAn introduction to the relation between path integrals and stochastic differential equations, and how to use Feynman diagrams.

New paper: Reaction-diffusion models of spontaneous neural activity in the developing retinaA novel reaction-diffusion model, using non-linear wave theory, to better understand how retinal waves form and spread in the developing retina.

New paper: Modelling a bistable switch underlying commitment to apoptosisThe Bcl-2 family of 15 or more proteins are key regulators of the intrinsic apoptosis pathway. Determining the mechanism two of these proteins (Bak and Bax) ...

New preprint: Compuational gene prediction with genomic tiling microarray dataThe genome of a higher organism is a complex entity. It is not merely comprised of the genes it encodes, but also of many other contributing elements. Elucid...