Analogue Front-End Model for Developing Neural Spike Sorting Systems

A behavioural model for neural recording Analogue Front Ends (AFEs) based on a Matlab GUI. Source code available via Mathworks FileExchange at: https://uk.mathworks.com/matlabcentral/fileexchange/65433-analogue-front-end-model-for-developing-neural-spike-sorting-systems

For further details, see: Barsakcioglu D, Liu Y, Bhunjun P, Navajas J, Eftekhar A, Jackson A, Quian Quiroga R, Constandinou TG, 2014, An Analogue Front-End Model for Developing Neural Spike Sorting Systems, IEEE Transactions on Biomedical Circuits and Systems, Vol: 8, Pages: 216-227 

Bayesian Adaptive Kernel Smoother (BAKS)

BAKS is a method for estimating firing rate from spike train data that uses kernel smoothing technique with adaptive bandwidth determined using a Bayesian approach. Source code available on Github at: https://github.com/nurahmadi/BAKS

For further details, see: Ahmadi N, Constandinou TG, Bouganis C.-S., 2017, Estimation of neuronal firing rate using Bayesian Adaptive Kernel Smoother (BAKS), bioRxiv 204818.

Finite difference time domain bi-dimensional model for simulating Optical Coherence Tomography (OCT)

A finite difference time domain (FDTD) model for computation of A line scans in time domain optical coherence tomography (OCT) for a myelinated peripheral nerve. Source code available on Github at: https://github.com/FTroiani/2D-FDTD-OCT

For further details, see: Troiani F, Nikolic K, Constandinou, TG, 2017, Simulating optical coherence tomography for observing nerve activity: a finite difference time domain bi-dimensional model, arXiv:1711.05644

Compact Standalone Platform for Neural Recording with Real-Time Spike Sorting and Data Logging

We have developed the NGNI platform - an end-to-end solution for on-node, real-time spike sorting. By using a compact, onboard (template based) spike sorting engine, together with offline training (WaveClus-based), a low power real-time solution is achievable. Technical resources (code, PCB designs), user manual, etc available on GitHub at: https://github.com/ImperialCollegeLondon/NGNIv1-Platform

For further details, see the NGNI resource webpage and bioRxiv pre-print: Luan S, Williams Y, Maslik M, Liu Y, Carvalho F, Jackson A, Quiroga RR, Constandinou, TG, 2017, Compact Standalone Platform for Neural Recording with Real-Time Spike Sorting and Data Logging, bioRxiv:186627