Neural Coding Lab

Computational Neuroscience &
Statistical Analysis of Neural Data

Computational models of neurons and networks. Neural Computation. Learning. Electrophysiology. Calcium imaging.

Recent Project News

Currently hiring (Ph.D and Post-doc positions)

The lab is currently looking for Ph. D. students and postdocs to join a collaboration between the Naud, Sachs and Beique labs at the University of Ottawa. Please contact Richard Naud with a research statement and a CV to begin a discussion. We are mostly looking for individuals with a background in engineering, physics or related field to work on neural coding, brain-computer interface and/or deep learning approaches. -- Posted Sept 2022.

A perspective on "Burstprop"

Published in Nature Neuroscience by Weinan Sun, Xinyu Zhao and Nelson Spruston. -- Posted Sept 2022.

Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits

Published in Nature Neuroscience. -- Posted Sept 2022.

Parallel and Recurrent Cascade Models as a Unifying Force for Understanding Subcellular Computation

Published in Neuroscience. -- Posted Sept 2022.

Capsule Deep Generative Model That Forms Parse Trees

Published in IJCNN, a collaboration with Yifeng Li. The link sends to a pre-print version published as a IJCNN conference proceeding. -- Posted Sept 2022.

Linear-nonlinear cascades capture synaptic dynamics

Published in PLoS Computational Biology. A statistical model to capture different types of synaptic dynamics. -- Posted March 2021.

Perirhinal input to neocortical layer 1 controls learning

Published in Science. A collaboration with the lab of Matthew Larkum; peri-rhinal inputs controls plasticity in S1 through apical dendrites and bursting. See perspective from Science - perspective from uOttawa -- Posted December 2020.

A synthetic likelihood solution to the silent synapse estimation problem

Accepted to Cell Reports! We use synthetic likelihood to improve estimations of silent synapses. -- Posted May 2020.

Synaptic Dynamics as Convolutional Units

A flexible and efficient modeling approach to short-term plasticity.-- Posted May 2020.

Burst Dependent Plasticity can Coordinate Learning in Hierarchical Circuits

Combining burst-dependence of synaptic plasiticy with multiplexing can solve the credit assignement problem.-- Posted April 2020.

A deep learning framework for neuroscience

Published in Nature Neuroscience-- Posted December 2019.

Classes of dendritic information processing

Published in Current Opinion in Neurobiology-- Posted September 2019.

Perirhinal input to neocortical layer 1 controls learning

A collaboration with the lab of Matthew Larkum; peri-rhinal inputs controls plasticity in S1 through apical dendrites and bursting.-- Posted June 2019.

Parsing out the variability of transmission at central synapses using optical quantal analysis

Published in Frontiers in Synaptic Neuroscience -- Posted June 2019.

Linking Demyelination to Compound Action Potential Dispersion with a Spike-Diffuse-Spike Approach

Published in the Journal of Mathematical Neuroscience. -- Posted June 2019.

Sparse bursts optimize informatiomation transmission in a multiplexed code

We proposed a neural code able to represent two streams of information simultanesously and showed that it is optimal for sparse bursting, as observed in vivo. The neural code has implications for interpreting the microcircuitry of the neocortex.-- Posted December 2018.

Noise Gating in Dendrite-Soma Interactions published in Phys. Rev. X

Brains process information reliably despite the presence of noise introduced by their molecular machinery. New computer simulations of neurons show that these cells might use noise to their advantage, by employing dendrites to detect low intensity signals and the main cell body to process more powerful signals. -- Posted August 2017.

Contact

rnaud at uottawa.ca
TEL: 613 562-5800 ext. 1850
Office 3230F, Roger Guindon, 451 Smyth Rd., Ottawa, Canada