Projects per year
Abstract
Gaseous neurotransmitters such as nitric oxide (NO) provide a unique and often overlooked mechanism for neurons to communicate through diffusion within a network, independent of synaptic connectivity. NO provides homeostatic control of intrinsic excitability. Here we conduct a theoretical investigation of the distinguishing roles of NO-mediated diffusive homeostasis in comparison with canonical non-diffusive homeostasis in cortical networks. We find that both forms of homeostasis provide a robust mechanism for maintaining stable activity following perturbations. However, the resulting networks differ, with diffusive homeostasis maintaining substantial heterogeneity in activity levels of individual neurons, a feature disrupted in networks with non-diffusive homeostasis. This results in networks capable of representing input heterogeneity, and linearly responding over a broader range of inputs than those undergoing non-diffusive homeostasis. We further show that these properties are preserved when homeostatic and Hebbian plasticity are combined. These results suggest a mechanism for dynamically maintaining neural heterogeneity, and expose computational advantages of non-local homeostatic processes.
Original language | English |
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Article number | e1004389 |
Journal | PLoS Computational Biology |
Volume | 11 |
Issue number | 7 |
DOIs | |
Publication status | Published - Jul 2015 |
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Dive into the research topics of 'A Diffusive Homeostatic Signal Maintains Neural Heterogeneity and Responsiveness in Cortical Networks'. Together they form a unique fingerprint.Projects
- 1 Finished
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Computational models of interactions between developmental and homeostatic processes during nervous system development
1/10/09 → 30/09/14
Project: Research
Profiles
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Matthias Hennig
- School of Informatics - Reader
- Institute for Adaptive and Neural Computation
- Edinburgh Neuroscience
- Data Science and Artificial Intelligence
Person: Academic: Research Active