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Understanding short-timescale neuronal firing sequences via bias matrices

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The brain generates persistent neuronal firing sequences across varying timescales... While sequential firing during SWRs is known to be biased by the previous experiences of the animal, the exact relationship of the short-timescale sequences during SWRs and longer-timescale sequences during spatial and non-spatial behaviors is still poorly understood... To distinguish these and other possibilities, one needs mathematical tools beyond the center-of-mass sequences and Spearman's rank-correlation coefficient that are currently used... We introduce a new mathematical tool that captures the intrinsic properties of neuronal firing sequences... The bias matrix of a given sequence (Figure 1) contains more detailed information than the center-of-mass average and captures more complex relationships among different neuronal sequences... This tool enabled us to directly investigate the relationships among firing sequences across different conditions: short-timescale sequences (during SWRs) and long-timescale behavioral sequences (during spatial navigation and wheel running)... We also performed a pharmacological manipulation that resulted in elimination of theta oscillation (as previously reported in ) and increased the frequency of SWRs... We have found that the pairwise biases of sequences during SWRs are highly correlated with sequences during most of the conditions... Moreover, while sequences of neuronal activations are uncorrelated across different behaviors, the bias matrices of SWR sequences are highly correlated with those of various behavior sequences... Our findings provide a new tool for understanding the structure of short-timescale neuronal sequences and suggest that intrinsic pairwise biases are likely the underlying mechanism for the "replay/preplay" of longer-timescale sequences observed in the hippocampus.

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Neuronal spike trains (top) are converted to bias matrices (middle) by computing the probability of pairs of neurons spiking in a particular order. The correlation between bias matrices (bottom) is then computed via the angle between the bias matrices.
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Figure 1: Neuronal spike trains (top) are converted to bias matrices (middle) by computing the probability of pairs of neurons spiking in a particular order. The correlation between bias matrices (bottom) is then computed via the angle between the bias matrices.

Mentions: We introduce a new mathematical tool that captures the intrinsic properties of neuronal firing sequences. The bias matrix of a given sequence (Figure 1) contains more detailed information than the center-of-mass average and captures more complex relationships among different neuronal sequences. This tool enabled us to directly investigate the relationships among firing sequences across different conditions: short-timescale sequences (during SWRs) and long-timescale behavioral sequences (during spatial navigation and wheel running). We also performed a pharmacological manipulation that resulted in elimination of theta oscillation (as previously reported in [4]) and increased the frequency of SWRs. We have found that the pairwise biases of sequences during SWRs are highly correlated with sequences during most of the conditions. Moreover, while sequences of neuronal activations are uncorrelated across different behaviors, the bias matrices of SWR sequences are highly correlated with those of various behavior sequences. Our findings provide a new tool for understanding the structure of short-timescale neuronal sequences and suggest that intrinsic pairwise biases are likely the underlying mechanism for the "replay/preplay" of longer-timescale sequences observed in the hippocampus [2,3].


Understanding short-timescale neuronal firing sequences via bias matrices
Neuronal spike trains (top) are converted to bias matrices (middle) by computing the probability of pairs of neurons spiking in a particular order. The correlation between bias matrices (bottom) is then computed via the angle between the bias matrices.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4697544&req=5

Figure 1: Neuronal spike trains (top) are converted to bias matrices (middle) by computing the probability of pairs of neurons spiking in a particular order. The correlation between bias matrices (bottom) is then computed via the angle between the bias matrices.
Mentions: We introduce a new mathematical tool that captures the intrinsic properties of neuronal firing sequences. The bias matrix of a given sequence (Figure 1) contains more detailed information than the center-of-mass average and captures more complex relationships among different neuronal sequences. This tool enabled us to directly investigate the relationships among firing sequences across different conditions: short-timescale sequences (during SWRs) and long-timescale behavioral sequences (during spatial navigation and wheel running). We also performed a pharmacological manipulation that resulted in elimination of theta oscillation (as previously reported in [4]) and increased the frequency of SWRs. We have found that the pairwise biases of sequences during SWRs are highly correlated with sequences during most of the conditions. Moreover, while sequences of neuronal activations are uncorrelated across different behaviors, the bias matrices of SWR sequences are highly correlated with those of various behavior sequences. Our findings provide a new tool for understanding the structure of short-timescale neuronal sequences and suggest that intrinsic pairwise biases are likely the underlying mechanism for the "replay/preplay" of longer-timescale sequences observed in the hippocampus [2,3].

View Article: PubMed Central - HTML

AUTOMATICALLY GENERATED EXCERPT
Please rate it.

The brain generates persistent neuronal firing sequences across varying timescales... While sequential firing during SWRs is known to be biased by the previous experiences of the animal, the exact relationship of the short-timescale sequences during SWRs and longer-timescale sequences during spatial and non-spatial behaviors is still poorly understood... To distinguish these and other possibilities, one needs mathematical tools beyond the center-of-mass sequences and Spearman's rank-correlation coefficient that are currently used... We introduce a new mathematical tool that captures the intrinsic properties of neuronal firing sequences... The bias matrix of a given sequence (Figure 1) contains more detailed information than the center-of-mass average and captures more complex relationships among different neuronal sequences... This tool enabled us to directly investigate the relationships among firing sequences across different conditions: short-timescale sequences (during SWRs) and long-timescale behavioral sequences (during spatial navigation and wheel running)... We also performed a pharmacological manipulation that resulted in elimination of theta oscillation (as previously reported in ) and increased the frequency of SWRs... We have found that the pairwise biases of sequences during SWRs are highly correlated with sequences during most of the conditions... Moreover, while sequences of neuronal activations are uncorrelated across different behaviors, the bias matrices of SWR sequences are highly correlated with those of various behavior sequences... Our findings provide a new tool for understanding the structure of short-timescale neuronal sequences and suggest that intrinsic pairwise biases are likely the underlying mechanism for the "replay/preplay" of longer-timescale sequences observed in the hippocampus.

No MeSH data available.