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Towards simulations of long-term behavior of neural networks: Modeling synaptic plasticity of connections within and between human brain regions
- Source :
- Neurocomputing
- Publication Year :
- 2020
- Publisher :
- Elsevier BV, 2020.
-
Abstract
- Highlights • Development of a biological neural network model that allows long term simulation of brain activity. • Optimization of the model using multiple techniques that led to a speed-up of X200. • Presentation of alternative simulation frameworks for long term simulations.<br />Simulations of neural networks can be used to study the direct effect of internal or external changes on brain dynamics. However, some changes are not immediate but occur on the timescale of weeks, months, or years. Examples include effects of strokes, surgical tissue removal, or traumatic brain injury but also gradual changes during brain development. Simulating network activity over a long time, even for a small number of nodes, is a computational challenge. Here, we model a coupled network of human brain regions with a modified Wilson-Cowan model representing dynamics for each region and with synaptic plasticity adjusting connection weights within and between regions. Using strategies ranging from different models for plasticity, vectorization and a different differential equation solver setup, we achieved one second runtime for one second biological time.
- Subjects :
- Brain simulation
Optimization
0209 industrial biotechnology
Brain development
Traumatic brain injury
Computer science
Cognitive Neuroscience
02 engineering and technology
Plasticity
020901 industrial engineering & automation
Artificial Intelligence
0202 electrical engineering, electronic engineering, information engineering
medicine
Quantitative Biology::Neurons and Cognition
Artificial neural network
Human brain
medicine.disease
Computer Science Applications
Brief Papers
medicine.anatomical_structure
Biological neural network modeling
Synaptic plasticity
Neural mass model
020201 artificial intelligence & image processing
Neuroscience
Subjects
Details
- ISSN :
- 09252312
- Volume :
- 416
- Database :
- OpenAIRE
- Journal :
- Neurocomputing
- Accession number :
- edsair.doi.dedup.....a7c0a84fc4e46cbce95f983a73e602a1