Volume 14 Supplement 1
Homeostasis in large networks of neurons through the Ising model - do higher order interactions matter?
© Panas et al; licensee BioMed Central Ltd. 2013
Published: 8 July 2013
Homeostatic activity in large networks of neurons is a relatively scantly explored area of neuroscience, both on experimental and computational level . With recent advance in recording techniques, the lack of experimental data is gradually ceasing to be the limitation. New multielectrode arrays (MEA) allow for monitoring cultures of thousands of neurons over many days with high spatial resolution . However, the interpretation of multi-neuron recordings is not straightforward and requires methods going beyond the simplest descriptive statistics.
Here we explore a novel approach to analyzing multi-unit neuronal activity recorded over a five day homeostatic experiment by employing the Ising model [3, 4]. This statistical model explains the probability of multi-neuron spike patterns solely on the basis of firing rates and correlations, assuming an otherwise minimally structured distribution. Its application to a variety of recordings has helped re-evaluate the importance of neural interactions in shaping the global activity [3, 4]. In addition, due to the models minimal structure, the quality of the fits can be treated as an indicator of higher-order interactions in the activity .
- Turrigiano G: Too Many Cooks? Intrinsic and Synaptic Homeostatic Mechanisms in Cortical Circuit Refinement. Annu Rev Neuroscience. 2011, 34: 89-103. 10.1146/annurev-neuro-060909-153238.View ArticleGoogle Scholar
- Berdondini L, Imfeld K, Maccione A, Tedesco M, Neukom S, Koudelka-Hep M, Martinoia S: Active pixel sensor array for high spatio-temporal resolution electrophysiological recordings from single cell to large scale neuronal networks. Lab Chip. 2009, 9: 2644-2651. 10.1039/b907394a.View ArticlePubMedGoogle Scholar
- Schneidman E, Bialek W: Ising models for networks of real neurons. Nature. 2006, 440: 1007-1012. 10.1038/nature04701.PubMed CentralView ArticlePubMedGoogle Scholar
- Ohiorhenuan IE, Mechler F, Purpura KP, Schmid AM, Hu Q, Victor JD: Sparse coding and high-order correlations in fine-scale cortical networks. Nature. 2010, 617-621. 466Google Scholar
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