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Place field repetition and purely local remapping in a multicompartment environment.

Spiers HJ, Hayman RM, Jovalekic A, Marozzi E, Jeffery KJ - Cereb. Cortex (2013)

Bottom Line: Some studies report that place cells can disambiguate different compartments, while others report that they do not.Second, this repetition does not diminish with extended experience.Third, remapping was found to be purely local for both geometric change and contextual change.

View Article: PubMed Central - PubMed

Affiliation: Department of Cognitive, Perceptual and Brain Sciences, Division of Psychology and Language Sciences, Institute of Behavioural Neuroscience, University College London, UK.

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Rate coding analysis, to see whether firing rates might contain information that could be used to disambiguate compartments. (A) Change in peak firing rate of place cells as a function of distance from the compartment having the peak rate, for naive rats (“First day”; open circles) or rats with extensive experience of the environment (“Last day”; closed circles). Below is shown a plan view of the apparatus, filled circles represent place fields from a single hypothetical place cell, illustrating the result shown above for comparison to the predictions in Figure 1. (B) Reconstruction error analysis, based on an ensemble of 15 simultaneously recorded cells, in which the firing rate population vector was used to reconstruct the position of the rat, and this position compared against the rat's actual position in the X- and Y-dimensions. The data for the whole 4-compartment environment were compared against the same dataset collapsed in the X-dimension (see inset) onto a single, composite compartment (thus losing compartment-specific information). For the whole-environment dataset, the X-error was much great than the Y-error, which reflects the ambiguity in the firing rate information about which environment the rat was in. When the data were collapsed onto a single-compartment reference frame, the X-error was slightly less, probably because the X-dimension was slightly but significantly less than the Y-dimension and there was thus less room for error. (C) The raw data were compared against the same data in which homologous bins in the 4 compartments were exchanged, so that compartment-specific rate information would be dispersed while spatial information common to all 4 compartments would be preserved. Although in both datasets the errors in the X-dimension were much larger than those in the Y-dimension, this error was even greater for the scrambled data than the raw data, suggesting a slight degree of compartment-specific information contained in the firing rate maps.
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BHT198F6: Rate coding analysis, to see whether firing rates might contain information that could be used to disambiguate compartments. (A) Change in peak firing rate of place cells as a function of distance from the compartment having the peak rate, for naive rats (“First day”; open circles) or rats with extensive experience of the environment (“Last day”; closed circles). Below is shown a plan view of the apparatus, filled circles represent place fields from a single hypothetical place cell, illustrating the result shown above for comparison to the predictions in Figure 1. (B) Reconstruction error analysis, based on an ensemble of 15 simultaneously recorded cells, in which the firing rate population vector was used to reconstruct the position of the rat, and this position compared against the rat's actual position in the X- and Y-dimensions. The data for the whole 4-compartment environment were compared against the same dataset collapsed in the X-dimension (see inset) onto a single, composite compartment (thus losing compartment-specific information). For the whole-environment dataset, the X-error was much great than the Y-error, which reflects the ambiguity in the firing rate information about which environment the rat was in. When the data were collapsed onto a single-compartment reference frame, the X-error was slightly less, probably because the X-dimension was slightly but significantly less than the Y-dimension and there was thus less room for error. (C) The raw data were compared against the same data in which homologous bins in the 4 compartments were exchanged, so that compartment-specific rate information would be dispersed while spatial information common to all 4 compartments would be preserved. Although in both datasets the errors in the X-dimension were much larger than those in the Y-dimension, this error was even greater for the scrambled data than the raw data, suggesting a slight degree of compartment-specific information contained in the firing rate maps.

Mentions: In the first—neighborhood—analysis, we assigned neighborhood relations to the compartments as follows: any compartments adjacent to the peak were labeled “1,” any that were next-but-one were labeled “2” and any that were 2 compartments away (for the subset of cells with peak fields in an end compartment) were labeled “3.” For cells in which there were 2 compartments occupying the “1” position, these values were averaged. We then analyzed firing rates as a function of this ordinal distance from the peak-rate compartment for both first day and last day of recording (see Fig. 6A). A 2-factor (day and compartment distance) repeated-measures ANOVA revealed a main effect of compartment distance [F2,64 = 15.1, P < 0.001, but no main effect of day or significant interaction. Unsurprisingly, for both first and last day, post hoc t-tests revealed significant differences in rate when the peak compartment was compared with compartments at a distance of 1 (P < 0.05, Cohen's d for first day = 0.64, Cohen's d for last day = 0.55), 2 (P < 0.05, Cohen's d for first day = 0.77, Cohen's d for last day = 0.76), or 3 compartments from the peak (P < 0.05, Cohen's d for first day = 0.79, Cohen's d for last day = 0.63). However, no other post hoc tests were significant (maximum Cohen's d = 0.22, 1 vs. 2 on last day). Thus, our neighborhood analysis provided no evidence of any modulation of peak rates by distance beyond the peak compartment.Figure 6.


Place field repetition and purely local remapping in a multicompartment environment.

Spiers HJ, Hayman RM, Jovalekic A, Marozzi E, Jeffery KJ - Cereb. Cortex (2013)

Rate coding analysis, to see whether firing rates might contain information that could be used to disambiguate compartments. (A) Change in peak firing rate of place cells as a function of distance from the compartment having the peak rate, for naive rats (“First day”; open circles) or rats with extensive experience of the environment (“Last day”; closed circles). Below is shown a plan view of the apparatus, filled circles represent place fields from a single hypothetical place cell, illustrating the result shown above for comparison to the predictions in Figure 1. (B) Reconstruction error analysis, based on an ensemble of 15 simultaneously recorded cells, in which the firing rate population vector was used to reconstruct the position of the rat, and this position compared against the rat's actual position in the X- and Y-dimensions. The data for the whole 4-compartment environment were compared against the same dataset collapsed in the X-dimension (see inset) onto a single, composite compartment (thus losing compartment-specific information). For the whole-environment dataset, the X-error was much great than the Y-error, which reflects the ambiguity in the firing rate information about which environment the rat was in. When the data were collapsed onto a single-compartment reference frame, the X-error was slightly less, probably because the X-dimension was slightly but significantly less than the Y-dimension and there was thus less room for error. (C) The raw data were compared against the same data in which homologous bins in the 4 compartments were exchanged, so that compartment-specific rate information would be dispersed while spatial information common to all 4 compartments would be preserved. Although in both datasets the errors in the X-dimension were much larger than those in the Y-dimension, this error was even greater for the scrambled data than the raw data, suggesting a slight degree of compartment-specific information contained in the firing rate maps.
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Related In: Results  -  Collection

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BHT198F6: Rate coding analysis, to see whether firing rates might contain information that could be used to disambiguate compartments. (A) Change in peak firing rate of place cells as a function of distance from the compartment having the peak rate, for naive rats (“First day”; open circles) or rats with extensive experience of the environment (“Last day”; closed circles). Below is shown a plan view of the apparatus, filled circles represent place fields from a single hypothetical place cell, illustrating the result shown above for comparison to the predictions in Figure 1. (B) Reconstruction error analysis, based on an ensemble of 15 simultaneously recorded cells, in which the firing rate population vector was used to reconstruct the position of the rat, and this position compared against the rat's actual position in the X- and Y-dimensions. The data for the whole 4-compartment environment were compared against the same dataset collapsed in the X-dimension (see inset) onto a single, composite compartment (thus losing compartment-specific information). For the whole-environment dataset, the X-error was much great than the Y-error, which reflects the ambiguity in the firing rate information about which environment the rat was in. When the data were collapsed onto a single-compartment reference frame, the X-error was slightly less, probably because the X-dimension was slightly but significantly less than the Y-dimension and there was thus less room for error. (C) The raw data were compared against the same data in which homologous bins in the 4 compartments were exchanged, so that compartment-specific rate information would be dispersed while spatial information common to all 4 compartments would be preserved. Although in both datasets the errors in the X-dimension were much larger than those in the Y-dimension, this error was even greater for the scrambled data than the raw data, suggesting a slight degree of compartment-specific information contained in the firing rate maps.
Mentions: In the first—neighborhood—analysis, we assigned neighborhood relations to the compartments as follows: any compartments adjacent to the peak were labeled “1,” any that were next-but-one were labeled “2” and any that were 2 compartments away (for the subset of cells with peak fields in an end compartment) were labeled “3.” For cells in which there were 2 compartments occupying the “1” position, these values were averaged. We then analyzed firing rates as a function of this ordinal distance from the peak-rate compartment for both first day and last day of recording (see Fig. 6A). A 2-factor (day and compartment distance) repeated-measures ANOVA revealed a main effect of compartment distance [F2,64 = 15.1, P < 0.001, but no main effect of day or significant interaction. Unsurprisingly, for both first and last day, post hoc t-tests revealed significant differences in rate when the peak compartment was compared with compartments at a distance of 1 (P < 0.05, Cohen's d for first day = 0.64, Cohen's d for last day = 0.55), 2 (P < 0.05, Cohen's d for first day = 0.77, Cohen's d for last day = 0.76), or 3 compartments from the peak (P < 0.05, Cohen's d for first day = 0.79, Cohen's d for last day = 0.63). However, no other post hoc tests were significant (maximum Cohen's d = 0.22, 1 vs. 2 on last day). Thus, our neighborhood analysis provided no evidence of any modulation of peak rates by distance beyond the peak compartment.Figure 6.

Bottom Line: Some studies report that place cells can disambiguate different compartments, while others report that they do not.Second, this repetition does not diminish with extended experience.Third, remapping was found to be purely local for both geometric change and contextual change.

View Article: PubMed Central - PubMed

Affiliation: Department of Cognitive, Perceptual and Brain Sciences, Division of Psychology and Language Sciences, Institute of Behavioural Neuroscience, University College London, UK.

Show MeSH
Related in: MedlinePlus