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Parameter estimation in spiking neural networks: a reverse-engineering approach.

H Rostro-Gonzalez¹, B Cessac, T Vieville

¹KIOS Research Centre and Holistic Electronics Research Lab., Department of Electrical and Computer Engineering, University of Cyprus, 75 Kallipoleos Avenue, PO Box 20537, 1678 Nicosia, Cyprus. hrosgonz@ucy.ac.cy

Journal of Neural Engineering

|March 16, 2012

Summary

This study introduces a novel linear programming method for reverse engineering spiking neural networks (SNNs). This approach efficiently estimates parameters in SNNs with synaptic delays, overcoming computational complexity.

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Area of Science:

Computational Neuroscience
Machine Learning
Artificial Intelligence

Background:

Spiking Neural Networks (SNNs) are biologically inspired computational models.
Parameter estimation in SNNs with synaptic delays is computationally challenging (NP-hard).
Existing methods struggle with the complexity introduced by time delays in neural signaling.

Purpose of the Study:

To develop an efficient reverse engineering approach for parameter estimation in SNNs.
To reformulate the parameter estimation problem as a solvable linear programming problem.
To enable precise 'programming' of SNNs for specific input-output transformations.

Main Methods:

Modeled SNNs using generalized integrate-and-fire neurons with time-discretized, deterministic evolution.
Transformed the parameter estimation problem into a linear programming (LP) problem.
Leveraged spike time observations for network reverse engineering.

Main Results:

Successfully reformulated SNN parameter estimation with delays as an LP problem, solvable in polynomial time.
Demonstrated that the LP adjustment mechanism is local and resembles Hebbian learning.
Presented a generalized method for designing input-output transformations to program SNNs.

Conclusions:

The proposed LP-based reverse engineering approach offers an efficient solution for parameter estimation in SNNs with delays.
This method provides a practical way to program SNNs by defining desired input-output behaviors.
The approach has implications for designing and controlling complex neural network architectures.