|Simulation Neuroscience Connectomics
|James B Isbister is a Postdoctoral Researcher in the Connectomics team in the Simulation Neuroscience Division.
James’ research aims to explore how information is represented by spiking activity in the cortex, through analysis of in vivo and in silico neural dynamics. During his PhD at the University of Oxford and with the Blue Brain Project, James demonstrated that precise cortical spike time patterns are time-warped (stretched/compressed) on single trials in vivo.
With the Blue Brain, James is aiming to use in silico cortical models to explore the cortical representation of sensory information and to make predictions about in vivo neural activity. Towards this, James develops tools for calibrating in silico cortical models and comparing their activity to in vivo as a validation. These tools have been used to guide data driven refinement and development of in silico cortical models. Overall, James hopes that his research into in vivo and in silico spiking activity can inform one another and help elucidate the rules by which information is encoded in the brain.
James joined Blue Brain in the final year of his PhD as a Junior Scientist. His PhD has considered questions of neural coding, feature binding and representation learning through spike-time-dependent plasticity. To address these questions, the PhD work analysed in vivo and in silico spiking activity and ran computer simulations of cortical activity and plasticity. Towards this, James contributed to “Spike” a GPU spiking neural network simulator, which was the fastest of its class.
Before starting his PhD James developed the first mobile app designed to aide users in building their own Memory Palace for the purposes of memorization. James previously worked as a software engineer in Bath, UK.
In addition to his PhD, James has a BSc Computer Science with Mathematics from the University of Bath, UK.
In his free time, James enjoys being active and playing music.
Isbister, J. B., Reyes-Puerta, V., Sun, J. J., Horenko, I., & Luhmann, H. J. (2021). Clustering and control for adaptation uncovers time-warped spike time patterns in cortical networks in vivo. Scientific Reports, 11(1), 1-20 https://www.nature.com/articles/s41598-021-94002-0
Ahmad, N., Isbister, J. B., Smithe, T. S. C., & Stringer, S. M. (2018). Spike: A gpu optimised spiking neural network simulator. bioRxiv, 461160. https://www.biorxiv.org/content/10.1101/461160v1