To spike or not to spike: from a change of the excitability type in single neurons to exploring coding properties in network models
Keywords
Loading...
Authors
Issue Date
2022-08-13
Language
en
Document type
Journal Title
Journal ISSN
Volume Title
Publisher
Title
ISSN
Volume
Issue
Startpage
Endpage
DOI
Abstract
From single neurons to large neural networks, the brain is constantly carrying and transforming
information. Neural coding is a core aspect of information representation, which relies on the stimulusresponse
relationship. However, the neural coding properties at a single neuron and network level are
still not fully understood.
At a single neuron level, the distinction between type 1 (integrator) and type 2 (resonator) neuron
excitability is well known and relates to what causes a neuron to spike. However, it is unclear whether
the excitability type of a single neuron can shift from type 1 to type 2 due to Acetylcholine (ACh)
neuromodulatory effects.
Pushing this further, understanding how such single neuron coding properties could arise and
interact at a network level is a great challenge considering the available network recording techniques.
Here, network modeling is crucial for understanding network behaviour in the brain. With such
models, one can study larger populations of neurons and still not lose information about single neuron
properties. For instance, with the Poisson Balanced Spiking network (Poisson BSN) model, one can
reproduce bio-like network spiking behaviour and still be able to observe single neuron properties. Yet,
how the coding properties of neurons from network models arise and are affected by network properties
is still unknown.
To study the coding properties in single neurons, we used the spike-triggered average (STA) analysis.
With this, we investigated the stimulus-response relationship from single neuron in vitro recordings
and neurons from network model simulations. From the experimental data analysis, we found that
ACh did not cause a shift from type 1 to type 2 excitability. And regarding the network modelling
we found that in the Poisson BSN model, network size, spiking cost, and time delays had an effect on
changing the neurons’ stimulus-response relationship given by the STA analysis.
Our results will allow for future research to model networks with neurons with different coding
properties (such as type 1 and type 2) and account for how the interactions between neurons might
affect these coding properties. Thus, this is one more step to developing and understanding network
models to make them more biologically plausible and more insightful for studying and reproducing
how the brain represents information.
Description
Citation
Supervisor
Faculty
Faculteit der Sociale Wetenschappen