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Related Experiment Video

Updated: May 9, 2026

Setting Limits on Supersymmetry Using Simplified Models

Setting Limits on Supersymmetry Using Simplified Models

Published on: November 15, 2013

Missing mass approximations for the partition function of stimulus driven Ising models.

Robert Haslinger¹, Demba Ba, Ralf Galuske

¹Martinos Center for Biomedical Imaging, Massachusetts General Hospital Charlestown, MA, USA ; Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology Cambridge, MA, USA.

Frontiers in Computational Neuroscience

|July 31, 2013

Summary

This summary is machine-generated.

This study introduces a fast method to approximate the stimulus-dependent partition function for Ising models, enabling efficient analysis of neural population dynamics. The new approach significantly reduces computation time for incorporating time-varying stimuli in neural network models.

Keywords:

Ising model multiple unit recordings network function partition function population codes stimulus coding

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Area of Science:

Computational neuroscience
Statistical physics

Background:

Ising models are crucial for quantifying neural population functional structure.
Traditional Ising models often exclude time-varying stimulus drive, limiting understanding of neural dynamics.
Incorporating stimulus drive traditionally imposes a significant computational burden due to stimulus-dependent partition function calculations.

Purpose of the Study:

To develop a computationally efficient method for approximating the stimulus-dependent partition function in Ising models.
To enable the inclusion of time-varying stimuli in Ising models for studying neural population encoding.
To overcome the computational limitations of existing methods for analyzing stimulus-driven neural activity.

Main Methods:

Approximation of the stimulus-dependent partition function by explicitly summing probable spike patterns.
Estimation of the remaining improbable patterns using stimulus-modulated missing mass.
Utilizing a product of conditioned logistic regression models to approximate the stimulus-modulated missing mass.
Achieving a computational complexity of approximately O(LNNpat).

Main Results:

The proposed method approximates the stimulus-dependent partition function in seconds or minutes, a significant speed improvement.
Demonstrated orders of magnitude reduction in computation time compared to Monte Carlo methods.
Showed more accurate approximation of the stimulus-driven partition function than existing Monte Carlo or deterministic methods.
Validated using multi-unit recordings from rat hippocampus, macaque DLPFC, and cat Area 18.

Conclusions:

The developed method offers a fast and accurate way to incorporate stimulus drive into Ising models.
This advance facilitates the study of population-based stimulus encoding in neural systems.
Makes Ising models more suitable for analyzing the dynamics of neural network function under time-varying stimuli.