Neurocomputing Lab

Department                           Neuroscience

Principal investigator          Mario Negrello

E-mail address                       m.negrello@erasmusmc.nl

Website                                   https://neurocomputinglab.com/

Mixing and matching: how to create a homeostatic neural network

Neurons alter both their intrinsic and synaptic properties with plasticity mechanisms such as long term potentiation (LTP) and depression (LTD). These alterations in the neuron’s properties lead to changes in the neuronal response (i.e. firing frequency). Homeostatic networks are able to maintain a target level of neuronal response. The question is, how do we get there?

Further reading (click to link to article)

Functional consequences of inhibitory plasticity: homeostasis, the excitation-inhibition balance and beyond – Sprekeler, H. (2017)

Local Field Potential measurements of hippocampal networks (reproducing biological behavior)

A key step in all computational and theoretical neuroscience is model validation with experimental data. While our models are defined at the cellular level, we don’t often have experimental electrophysiological data from large sets of individual cells. We do have local field potential measures and we’d like to use these to validate and optimize our large scale networks to these experimental data. Your task will be to recover local field potential information from large scale simulations and compare these with published experimental data.

Further reading (click to link to article)

Multimodal Modeling of Neural Network Activity: Computing LFP, ECoG, EEG, and MEG Signals With LFPy 2.0