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Surrogate population models for large-scale neural simulations.
1Department of Systems Design Engineering and Centre for Theoretical Neuroscience, University of Waterloo, Waterloo, Ontario N2L 3GI, Canada bptripp@uwaterloo.ca.
Neural Computation
|March 17, 2015
Summary
This study introduces efficient surrogate models for neural systems, significantly reducing computational demands while approximating population activity. These models enable faster simulations of large neural networks by modeling aggregate outputs instead of individual neuron spikes.
Area of Science:
- Computational Neuroscience
- Neural Engineering
- Systems Neuroscience
Background:
- Modeling small brain regions in isolation is unrealistic due to complex interconnections.
- Simulating large-scale neural systems in detail is computationally impractical.
- Existing models struggle to balance biological realism with computational efficiency.
Purpose of the Study:
- To develop a novel multiscale modeling approach for neural systems.
- To construct efficient surrogate models that approximate the aggregate outputs of neuron populations.
- To reduce computational demands for simulating large neural networks.
Main Methods:
- Developed surrogate models for populations of neuron models with correlated activity and specific connections.
- Modeled weighted sums of spikes by interpolating over latent variables in population activity.
- Utilized linear filters operating on Gaussian random variables to approximate spike-related fluctuations, specifically within Neural Engineering Framework (NEF) circuits.
Main Results:
- Surrogate models closely approximate network behavior with orders-of-magnitude reduction in computational demands.
- Approximate spike rasters for specific neurons can be derived from surrogate model simulations.
- Simulations can utilize significantly larger step sizes (e.g., 20 ms) due to the absence of individual spike modeling.
Conclusions:
- The proposed surrogate modeling approach offers a computationally efficient alternative for large-scale neural simulations.
- This method provides a clear relationship between the surrogate and detailed neural models.
- Potential extensions include application to non-NEF networks and more complex neuron models.
