Abstract:

Dendrites are thin processes that extend from the cell body of neurons and receive the vast majority of synaptic input. Their biophysical, anatomical and plasticity properties allow them to shape incoming signals in complex ways and have thus been suggested to serve as key players in learning and memory functions. In my presentation I will discuss how computational modelling has helped us illuminate dendritic function. I will present the main findings of a number of projects in lab dealing with dendritic nonlinearities in excitatory and inhibitory neurons and their consequences on memory formation, the role of dendrites in solving nonlinear problems in human neurons and recent efforts to adopt dendritic features in order to improve learning in artificial systems.

Relevant references:

[1] Panayiota Poirazi & Athanasia Papoutsi. Illuminating dendritic function with computational models. Nature Reviews Neuroscience, 11 May 2020 | DOI: 10.1038/s41583-020-0301-7

[2] Gidon A, Zolnik TA, Fidzinski P, Bolduan F, Papoutsi A, Poirazi P, Holtkamp M, Vida I, Larkum ME. Dendritic action potentials and computation in human layer 2/3 cortical neurons. Science. 2020 Jan 3;367(6473):83-87. doi: 10.1126/science.aax6239.

[3] Chavlis S, Poirazi P. Drawing inspiration from biological dendrites to empower artificial neural networks. Current Opinion in Neurobiology, Oct 2021. doi: 10.1016/j.conb.2021.04.007

[4] Malakasis N, Chavlis S, Poirazi P. Synaptic turnover promotes efficient learning in bio-realistic spiking neural networks. BioRxiv, doi: https://doi.org/10.1101/2023.05.22.541722

[5] Makarov R, Pagkalos M, Poirazi P. Dendrites and efficiency: Optimizing performance and resource utilization. Current Opinion in Neurobiology, Dec 2023 | DOI: 10.1016/j.conb.2023.102812