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Independent theta phase coding accounts for CA1 population sequences and enables flexible remapping.

Chadwick A, van Rossum MC, Nolan MF - Elife (2015)

Bottom Line: These sequential representations have been suggested to result from temporally coordinated synaptic interactions within and between cell assemblies.We identify measures of global remapping and intracellular theta dynamics as critical for distinguishing mechanisms for pacemaking and coordination of sequential population activity.Our analysis suggests that, unlike synaptically coupled assemblies, independent neurons flexibly generate sequential population activity within the duration of a single theta cycle.

View Article: PubMed Central - PubMed

Affiliation: Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Edinburgh, United Kingdom.

ABSTRACT
Hippocampal place cells encode an animal's past, current, and future location through sequences of action potentials generated within each cycle of the network theta rhythm. These sequential representations have been suggested to result from temporally coordinated synaptic interactions within and between cell assemblies. Instead, we find through simulations and analysis of experimental data that rate and phase coding in independent neurons is sufficient to explain the organization of CA1 population activity during theta states. We show that CA1 population activity can be described as an evolving traveling wave that exhibits phase coding, rate coding, spike sequences and that generates an emergent population theta rhythm. We identify measures of global remapping and intracellular theta dynamics as critical for distinguishing mechanisms for pacemaking and coordination of sequential population activity. Our analysis suggests that, unlike synaptically coupled assemblies, independent neurons flexibly generate sequential population activity within the duration of a single theta cycle.

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Results of shuffling analysis.(A–D) The analysis of Foster and Wilson (2007) and(E–F) a corrected analysis.(A) Spike phases were initially calculated byinterpolation between theta peaks, shown as dotted lines.(B) After shuffling the phases of spikes, a new spike timeis calculated by interpolation between the nearest two theta troughs(dotted lines) to the original spike, which often generates erroneousspike times. The shuffled spike in this case acquires a small phasejitter, but a large temporal jitter. (C) The unshuffledsequence correlations between cell rank order and spike times. The redline shows the mean correlation. (D) Shuffled sequencecorrelations remained greater than zero, but were significantly reducedrelative to the unshuffled case as in experimental data (Foster and Wilson, 2007).(E) Results of a corrected shuffling procedure applied tosimulated independent coding datasets and an experimental dataset (heightmagnified for comparison). Displayed are the average changes in sequencecorrelations caused by shuffling for each simulated dataset. In 74% ofsimulated datasets, there was no significant difference between theoriginal and shuffled distributions. (F) Results of thecorrected shuffling procedure when applied to datasets simulated withcoordinated assemblies. In 81% of simulated coordinated coding datasets,shuffling significantly changed the distribution of sequencecorrelations. The experimental dataset was not significantly affected byshuffling (p = 0.28, t-test, 2436 putative sequences).DOI:http://dx.doi.org/10.7554/eLife.03542.015
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fig5s2: Results of shuffling analysis.(A–D) The analysis of Foster and Wilson (2007) and(E–F) a corrected analysis.(A) Spike phases were initially calculated byinterpolation between theta peaks, shown as dotted lines.(B) After shuffling the phases of spikes, a new spike timeis calculated by interpolation between the nearest two theta troughs(dotted lines) to the original spike, which often generates erroneousspike times. The shuffled spike in this case acquires a small phasejitter, but a large temporal jitter. (C) The unshuffledsequence correlations between cell rank order and spike times. The redline shows the mean correlation. (D) Shuffled sequencecorrelations remained greater than zero, but were significantly reducedrelative to the unshuffled case as in experimental data (Foster and Wilson, 2007).(E) Results of a corrected shuffling procedure applied tosimulated independent coding datasets and an experimental dataset (heightmagnified for comparison). Displayed are the average changes in sequencecorrelations caused by shuffling for each simulated dataset. In 74% ofsimulated datasets, there was no significant difference between theoriginal and shuffled distributions. (F) Results of thecorrected shuffling procedure when applied to datasets simulated withcoordinated assemblies. In 81% of simulated coordinated coding datasets,shuffling significantly changed the distribution of sequencecorrelations. The experimental dataset was not significantly affected byshuffling (p = 0.28, t-test, 2436 putative sequences).DOI:http://dx.doi.org/10.7554/eLife.03542.015

Mentions: Further experimental support for the notion of inter-assembly coordination has comefrom an analysis suggesting that single cell phase precession is less precise thanobserved theta sequences (Foster and Wilson,2007). This conclusion relies on a shuffling procedure which preserves thestatistics of single cell phase precession yet reduces intra-sequence correlations.However, performing the same shuffling analysis on data generated by our independentcoding model also reduced sequence correlations (t-test, p <10−46) (Figure 5—figuresupplement 2). The effect arises because the shuffling procedure does notpreserve the temporal structure of single cell phase precession, despite preservingthe phasic structure (Figure 5—figuresupplement 2A,B). Hence, the phase–position correlations areunaffected, while the time–position correlations and hence sequencecorrelations are disrupted (Figure 5—figuresupplement 2C,D). Thus, inter-assembly coordination is not required toaccount for these findings.


Independent theta phase coding accounts for CA1 population sequences and enables flexible remapping.

Chadwick A, van Rossum MC, Nolan MF - Elife (2015)

Results of shuffling analysis.(A–D) The analysis of Foster and Wilson (2007) and(E–F) a corrected analysis.(A) Spike phases were initially calculated byinterpolation between theta peaks, shown as dotted lines.(B) After shuffling the phases of spikes, a new spike timeis calculated by interpolation between the nearest two theta troughs(dotted lines) to the original spike, which often generates erroneousspike times. The shuffled spike in this case acquires a small phasejitter, but a large temporal jitter. (C) The unshuffledsequence correlations between cell rank order and spike times. The redline shows the mean correlation. (D) Shuffled sequencecorrelations remained greater than zero, but were significantly reducedrelative to the unshuffled case as in experimental data (Foster and Wilson, 2007).(E) Results of a corrected shuffling procedure applied tosimulated independent coding datasets and an experimental dataset (heightmagnified for comparison). Displayed are the average changes in sequencecorrelations caused by shuffling for each simulated dataset. In 74% ofsimulated datasets, there was no significant difference between theoriginal and shuffled distributions. (F) Results of thecorrected shuffling procedure when applied to datasets simulated withcoordinated assemblies. In 81% of simulated coordinated coding datasets,shuffling significantly changed the distribution of sequencecorrelations. The experimental dataset was not significantly affected byshuffling (p = 0.28, t-test, 2436 putative sequences).DOI:http://dx.doi.org/10.7554/eLife.03542.015
© Copyright Policy
Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC4383210&req=5

fig5s2: Results of shuffling analysis.(A–D) The analysis of Foster and Wilson (2007) and(E–F) a corrected analysis.(A) Spike phases were initially calculated byinterpolation between theta peaks, shown as dotted lines.(B) After shuffling the phases of spikes, a new spike timeis calculated by interpolation between the nearest two theta troughs(dotted lines) to the original spike, which often generates erroneousspike times. The shuffled spike in this case acquires a small phasejitter, but a large temporal jitter. (C) The unshuffledsequence correlations between cell rank order and spike times. The redline shows the mean correlation. (D) Shuffled sequencecorrelations remained greater than zero, but were significantly reducedrelative to the unshuffled case as in experimental data (Foster and Wilson, 2007).(E) Results of a corrected shuffling procedure applied tosimulated independent coding datasets and an experimental dataset (heightmagnified for comparison). Displayed are the average changes in sequencecorrelations caused by shuffling for each simulated dataset. In 74% ofsimulated datasets, there was no significant difference between theoriginal and shuffled distributions. (F) Results of thecorrected shuffling procedure when applied to datasets simulated withcoordinated assemblies. In 81% of simulated coordinated coding datasets,shuffling significantly changed the distribution of sequencecorrelations. The experimental dataset was not significantly affected byshuffling (p = 0.28, t-test, 2436 putative sequences).DOI:http://dx.doi.org/10.7554/eLife.03542.015
Mentions: Further experimental support for the notion of inter-assembly coordination has comefrom an analysis suggesting that single cell phase precession is less precise thanobserved theta sequences (Foster and Wilson,2007). This conclusion relies on a shuffling procedure which preserves thestatistics of single cell phase precession yet reduces intra-sequence correlations.However, performing the same shuffling analysis on data generated by our independentcoding model also reduced sequence correlations (t-test, p <10−46) (Figure 5—figuresupplement 2). The effect arises because the shuffling procedure does notpreserve the temporal structure of single cell phase precession, despite preservingthe phasic structure (Figure 5—figuresupplement 2A,B). Hence, the phase–position correlations areunaffected, while the time–position correlations and hence sequencecorrelations are disrupted (Figure 5—figuresupplement 2C,D). Thus, inter-assembly coordination is not required toaccount for these findings.

Bottom Line: These sequential representations have been suggested to result from temporally coordinated synaptic interactions within and between cell assemblies.We identify measures of global remapping and intracellular theta dynamics as critical for distinguishing mechanisms for pacemaking and coordination of sequential population activity.Our analysis suggests that, unlike synaptically coupled assemblies, independent neurons flexibly generate sequential population activity within the duration of a single theta cycle.

View Article: PubMed Central - PubMed

Affiliation: Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Edinburgh, United Kingdom.

ABSTRACT
Hippocampal place cells encode an animal's past, current, and future location through sequences of action potentials generated within each cycle of the network theta rhythm. These sequential representations have been suggested to result from temporally coordinated synaptic interactions within and between cell assemblies. Instead, we find through simulations and analysis of experimental data that rate and phase coding in independent neurons is sufficient to explain the organization of CA1 population activity during theta states. We show that CA1 population activity can be described as an evolving traveling wave that exhibits phase coding, rate coding, spike sequences and that generates an emergent population theta rhythm. We identify measures of global remapping and intracellular theta dynamics as critical for distinguishing mechanisms for pacemaking and coordination of sequential population activity. Our analysis suggests that, unlike synaptically coupled assemblies, independent neurons flexibly generate sequential population activity within the duration of a single theta cycle.

Show MeSH