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Learning probabilistic models of connectivity from multiple spike train data

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In this work we present a new class of dynamic Bayesian networks to infer polysynaptic excitatory connectivity between spiking cortical neurons... We formally establish a connection between efficient frequent episode mining algorithms (used to indentify frequently repeating patterns of spiking activity ) and learning probabilistic models for excitatory connections... This framework is depicted in Figure 1... We demonstrate the effectiveness of our method in discovering connectivity information on synthetic and real datasets... We also demonstrate the application of our method on multi-electrode arrays recordings from dissociated cortical cultures gathered by Steve Potter's laboratory at Georgia Tech... Existing data analysis tools like cross-correlograms, JPSTH and PCA do not scale well as we look at several neurons at a time... Our approach provides an efficient and formal basis for learning probabilistic models from observed spike train data... Several types of network dynamics like syn-fire chains, polychrony etc. that neuronal networks are known to exhibit can be modeled as excitatory networks and hence their putative structure can be learnt using our method (as illustrated in Figure 2)... Our proposed approach also scales very well to large data sizes as it marries fast data mining style algorithms with formal model learning.

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A multi-electrode array (MEA; left) produces a stream of action potentials (middle). Mining frequent episodes of firing in simultaneously recorded multiple-spike train data uncovers excitatory circuits (right).
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Figure 1: A multi-electrode array (MEA; left) produces a stream of action potentials (middle). Mining frequent episodes of firing in simultaneously recorded multiple-spike train data uncovers excitatory circuits (right).

Mentions: We model the spike train data as binary random variables and learn high mutual information parent sets of neurons that excite the spiking of down-stream neurons at variable delays. Thus we can express the probability of spiking of each neuron conditioned on the activity of a subset of relevant neurons in recent past (or history window). We formally establish a connection between efficient frequent episode mining algorithms (used to indentify frequently repeating patterns of spiking activity [3]) and learning probabilistic models for excitatory connections. This framework is depicted in Figure 1.


Learning probabilistic models of connectivity from multiple spike train data
A multi-electrode array (MEA; left) produces a stream of action potentials (middle). Mining frequent episodes of firing in simultaneously recorded multiple-spike train data uncovers excitatory circuits (right).
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3090878&req=5

Figure 1: A multi-electrode array (MEA; left) produces a stream of action potentials (middle). Mining frequent episodes of firing in simultaneously recorded multiple-spike train data uncovers excitatory circuits (right).
Mentions: We model the spike train data as binary random variables and learn high mutual information parent sets of neurons that excite the spiking of down-stream neurons at variable delays. Thus we can express the probability of spiking of each neuron conditioned on the activity of a subset of relevant neurons in recent past (or history window). We formally establish a connection between efficient frequent episode mining algorithms (used to indentify frequently repeating patterns of spiking activity [3]) and learning probabilistic models for excitatory connections. This framework is depicted in Figure 1.

View Article: PubMed Central - HTML

AUTOMATICALLY GENERATED EXCERPT
Please rate it.

In this work we present a new class of dynamic Bayesian networks to infer polysynaptic excitatory connectivity between spiking cortical neurons... We formally establish a connection between efficient frequent episode mining algorithms (used to indentify frequently repeating patterns of spiking activity ) and learning probabilistic models for excitatory connections... This framework is depicted in Figure 1... We demonstrate the effectiveness of our method in discovering connectivity information on synthetic and real datasets... We also demonstrate the application of our method on multi-electrode arrays recordings from dissociated cortical cultures gathered by Steve Potter's laboratory at Georgia Tech... Existing data analysis tools like cross-correlograms, JPSTH and PCA do not scale well as we look at several neurons at a time... Our approach provides an efficient and formal basis for learning probabilistic models from observed spike train data... Several types of network dynamics like syn-fire chains, polychrony etc. that neuronal networks are known to exhibit can be modeled as excitatory networks and hence their putative structure can be learnt using our method (as illustrated in Figure 2)... Our proposed approach also scales very well to large data sizes as it marries fast data mining style algorithms with formal model learning.

No MeSH data available.