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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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Dependence of decoded trajectories on the number of cells in asequence.(A–C) Distributions of the number ofcells which spike in a theta cycle, for simulations of the independentcoding model with different densities of place fields on the track (i.e.,different numbers of place fields on a track of fixed length).(A) The cell density used to reproduce the results ofGupta et al. (2012).(B and C) Simulations with higher placefield densities in which more active cells are recorded in each thetacycle on average. (D–F) Relationshipbetween decoded ahead and behind length, calculated as in Gupta et al. (2012), shown forsimulations with different place field densities and for differentthresholds of the minimum number of cells required for a sequence to beincluded for analysis. (D) Simulations with 12 cells on thetrack and a threshold of three cells generate results similar to Gupta et al. (2012).(E–F) The density of place fields onthe track and the threshold for sequence selection affect the decodedtrajectories, with higher values for either resulting in a smaller changein behind length as a function of ahead length.(G–H) Spearman's rankcorrelation between ahead length and behind length for different placefield densities plotted as a function of the threshold for the minimumnumber active of cells. Although the magnitude of the effect shown in(D–F) is diminished as thesequantities increase, the correlation between ahead and behind lengthstays constant. Moreover, this correlation remains significant despitethe decreasing effect size. Only when the number of selected sequencesbecomes too low to maintain a reliable measure does the effect becomeinsignificant.DOI:http://dx.doi.org/10.7554/eLife.03542.017
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fig6s1: Dependence of decoded trajectories on the number of cells in asequence.(A–C) Distributions of the number ofcells which spike in a theta cycle, for simulations of the independentcoding model with different densities of place fields on the track (i.e.,different numbers of place fields on a track of fixed length).(A) The cell density used to reproduce the results ofGupta et al. (2012).(B and C) Simulations with higher placefield densities in which more active cells are recorded in each thetacycle on average. (D–F) Relationshipbetween decoded ahead and behind length, calculated as in Gupta et al. (2012), shown forsimulations with different place field densities and for differentthresholds of the minimum number of cells required for a sequence to beincluded for analysis. (D) Simulations with 12 cells on thetrack and a threshold of three cells generate results similar to Gupta et al. (2012).(E–F) The density of place fields onthe track and the threshold for sequence selection affect the decodedtrajectories, with higher values for either resulting in a smaller changein behind length as a function of ahead length.(G–H) Spearman's rankcorrelation between ahead length and behind length for different placefield densities plotted as a function of the threshold for the minimumnumber active of cells. Although the magnitude of the effect shown in(D–F) is diminished as thesequantities increase, the correlation between ahead and behind lengthstays constant. Moreover, this correlation remains significant despitethe decreasing effect size. Only when the number of selected sequencesbecomes too low to maintain a reliable measure does the effect becomeinsignificant.DOI:http://dx.doi.org/10.7554/eLife.03542.017

Mentions: Finally, precise coordination of theta sequences has been suggested on the basis thattheta sequence properties vary according to environmental features such as landmarksand behavioral factors such as acceleration, with sequences sometimes representinglocations further ahead or behind the animal (Guptaet al., 2012). To establish whether independent coding could also accountfor these results, we generated data from our model and applied the sequenceidentification and decoding analysis reported by Gupta et al. (2012). We found that, even for simulated data based on purerate coding with no theta modulation (k = 0), large numbersof significant sequences were detected at high running speeds (Figure 6A). Therefore, to test the performance of the fullsequence detection and Bayesian decoding protocol used by (Gupta et al., 2012), we analyzed two simulateddatasets—one with a realistic value of phase locking (k= 0.5, Figure 6B–D, solid lines)and another with zero phase locking (i.e., no theta related activity, Figure 6B–D, dashed lines). In both cases,applying the reported Bayesian decoding analysis yielded similar decoded path lengthsto those found experimentally (Figure 6C,D).Importantly, there was an inverse relationship between the ahead and behind lengthsdecoded from the simulated data, which reproduces the apparent shift in sequencesahead or behind the animal observed in experimental data (cf. Figure 4c of Gupta et al. (2012)). This effect was dependenton the density of recorded place fields on the track and the threshold for theminimum number of cells in a theta cycle required for sequence selection (Figure 6—figure supplement 1). As theseresults were obtained both in the case with realistic phase coding and in the casewith only rate coding (and therefore no theta sequences), the properties of thedecoded trajectories are not related to theta activity within the population. Hence,these data do not constrain models of theta activity in CA1.10.7554/eLife.03542.016Figure 6.Analysis of individual sequence statistics.


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

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

Dependence of decoded trajectories on the number of cells in asequence.(A–C) Distributions of the number ofcells which spike in a theta cycle, for simulations of the independentcoding model with different densities of place fields on the track (i.e.,different numbers of place fields on a track of fixed length).(A) The cell density used to reproduce the results ofGupta et al. (2012).(B and C) Simulations with higher placefield densities in which more active cells are recorded in each thetacycle on average. (D–F) Relationshipbetween decoded ahead and behind length, calculated as in Gupta et al. (2012), shown forsimulations with different place field densities and for differentthresholds of the minimum number of cells required for a sequence to beincluded for analysis. (D) Simulations with 12 cells on thetrack and a threshold of three cells generate results similar to Gupta et al. (2012).(E–F) The density of place fields onthe track and the threshold for sequence selection affect the decodedtrajectories, with higher values for either resulting in a smaller changein behind length as a function of ahead length.(G–H) Spearman's rankcorrelation between ahead length and behind length for different placefield densities plotted as a function of the threshold for the minimumnumber active of cells. Although the magnitude of the effect shown in(D–F) is diminished as thesequantities increase, the correlation between ahead and behind lengthstays constant. Moreover, this correlation remains significant despitethe decreasing effect size. Only when the number of selected sequencesbecomes too low to maintain a reliable measure does the effect becomeinsignificant.DOI:http://dx.doi.org/10.7554/eLife.03542.017
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Related In: Results  -  Collection

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fig6s1: Dependence of decoded trajectories on the number of cells in asequence.(A–C) Distributions of the number ofcells which spike in a theta cycle, for simulations of the independentcoding model with different densities of place fields on the track (i.e.,different numbers of place fields on a track of fixed length).(A) The cell density used to reproduce the results ofGupta et al. (2012).(B and C) Simulations with higher placefield densities in which more active cells are recorded in each thetacycle on average. (D–F) Relationshipbetween decoded ahead and behind length, calculated as in Gupta et al. (2012), shown forsimulations with different place field densities and for differentthresholds of the minimum number of cells required for a sequence to beincluded for analysis. (D) Simulations with 12 cells on thetrack and a threshold of three cells generate results similar to Gupta et al. (2012).(E–F) The density of place fields onthe track and the threshold for sequence selection affect the decodedtrajectories, with higher values for either resulting in a smaller changein behind length as a function of ahead length.(G–H) Spearman's rankcorrelation between ahead length and behind length for different placefield densities plotted as a function of the threshold for the minimumnumber active of cells. Although the magnitude of the effect shown in(D–F) is diminished as thesequantities increase, the correlation between ahead and behind lengthstays constant. Moreover, this correlation remains significant despitethe decreasing effect size. Only when the number of selected sequencesbecomes too low to maintain a reliable measure does the effect becomeinsignificant.DOI:http://dx.doi.org/10.7554/eLife.03542.017
Mentions: Finally, precise coordination of theta sequences has been suggested on the basis thattheta sequence properties vary according to environmental features such as landmarksand behavioral factors such as acceleration, with sequences sometimes representinglocations further ahead or behind the animal (Guptaet al., 2012). To establish whether independent coding could also accountfor these results, we generated data from our model and applied the sequenceidentification and decoding analysis reported by Gupta et al. (2012). We found that, even for simulated data based on purerate coding with no theta modulation (k = 0), large numbersof significant sequences were detected at high running speeds (Figure 6A). Therefore, to test the performance of the fullsequence detection and Bayesian decoding protocol used by (Gupta et al., 2012), we analyzed two simulateddatasets—one with a realistic value of phase locking (k= 0.5, Figure 6B–D, solid lines)and another with zero phase locking (i.e., no theta related activity, Figure 6B–D, dashed lines). In both cases,applying the reported Bayesian decoding analysis yielded similar decoded path lengthsto those found experimentally (Figure 6C,D).Importantly, there was an inverse relationship between the ahead and behind lengthsdecoded from the simulated data, which reproduces the apparent shift in sequencesahead or behind the animal observed in experimental data (cf. Figure 4c of Gupta et al. (2012)). This effect was dependenton the density of recorded place fields on the track and the threshold for theminimum number of cells in a theta cycle required for sequence selection (Figure 6—figure supplement 1). As theseresults were obtained both in the case with realistic phase coding and in the casewith only rate coding (and therefore no theta sequences), the properties of thedecoded trajectories are not related to theta activity within the population. Hence,these data do not constrain models of theta activity in CA1.10.7554/eLife.03542.016Figure 6.Analysis of individual sequence statistics.

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
Related in: MedlinePlus