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Internally organized mechanisms of the head direction sense.

Peyrache A, Lacroix MM, Petersen PC, Buzsáki G - Nat. Neurosci. (2015)

Bottom Line: The temporal correlation structure of HD neurons was preserved during sleep, characterized by a 60°-wide correlated neuronal firing (activity packet), both within and across these two brain structures.During rapid eye movement sleep, the spontaneous drift of the activity packet was similar to that observed during waking and accelerated tenfold during slow-wave sleep.These findings demonstrate that peripheral inputs impinge on an internally organized network, which provides amplification and enhanced precision of the HD signal.

View Article: PubMed Central - PubMed

Affiliation: The Neuroscience Institute, School of Medicine and Center for Neural Science, New York University, New York, New York, USA.

ABSTRACT
The head-direction (HD) system functions as a compass, with member neurons robustly increasing their firing rates when the animal's head points in a specific direction. HD neurons may be driven by peripheral sensors or, as computational models postulate, internally generated (attractor) mechanisms. We addressed the contributions of stimulus-driven and internally generated activity by recording ensembles of HD neurons in the antero-dorsal thalamic nucleus and the post-subiculum of mice by comparing their activity in various brain states. The temporal correlation structure of HD neurons was preserved during sleep, characterized by a 60°-wide correlated neuronal firing (activity packet), both within and across these two brain structures. During rapid eye movement sleep, the spontaneous drift of the activity packet was similar to that observed during waking and accelerated tenfold during slow-wave sleep. These findings demonstrate that peripheral inputs impinge on an internally organized network, which provides amplification and enhanced precision of the HD signal.

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Brain-state dependent dynamics of the HD signala: Firing rates during SWS (left) and REM (right) plotted against waking firing rates for ADn cells (red, n = 215) and PoS cells (blue, n = 62). Insets: Pearson correlation r values. b: Same as a for pairwise correlations (ADn pairs, n = 970; PoS pairs, n = 92). c: Examples of cross-correlations for three HD cell pairs during waking, SWS and REM. Normalized histograms with 1 representing chance. Polar plots display HD fields of the pairs. d: Same as a for all ADn HD cell pairs, sorted by the magnitude of difference between waking preferred directions (shown in right most panel). Each cross-correlogram was normalized between their minimum (dark blue) and maximum (dark red) values. Cell identity order is the same in each panel. e: Left: histogram of average angular velocity estimated from individual cross-correlograms during wake (black) and REM (yellow). Higher angular speed results in more “compressed” cross-correlograms. Their temporal profiles were thus used to quantify the average drifting speed (see Methods). Top: distribution of actual or Bayesian estimates of HD angular speed. Right: same for SWS. f: Distribution of ratios between sleep and wake-estimated angular velocities for ADN-ADN and PoS-PoS HD cell pairs. Boxplot in e and f show median and quartiles.
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Figure 2: Brain-state dependent dynamics of the HD signala: Firing rates during SWS (left) and REM (right) plotted against waking firing rates for ADn cells (red, n = 215) and PoS cells (blue, n = 62). Insets: Pearson correlation r values. b: Same as a for pairwise correlations (ADn pairs, n = 970; PoS pairs, n = 92). c: Examples of cross-correlations for three HD cell pairs during waking, SWS and REM. Normalized histograms with 1 representing chance. Polar plots display HD fields of the pairs. d: Same as a for all ADn HD cell pairs, sorted by the magnitude of difference between waking preferred directions (shown in right most panel). Each cross-correlogram was normalized between their minimum (dark blue) and maximum (dark red) values. Cell identity order is the same in each panel. e: Left: histogram of average angular velocity estimated from individual cross-correlograms during wake (black) and REM (yellow). Higher angular speed results in more “compressed” cross-correlograms. Their temporal profiles were thus used to quantify the average drifting speed (see Methods). Top: distribution of actual or Bayesian estimates of HD angular speed. Right: same for SWS. f: Distribution of ratios between sleep and wake-estimated angular velocities for ADN-ADN and PoS-PoS HD cell pairs. Boxplot in e and f show median and quartiles.

Mentions: Under the hypothesis of an internally-organized HD system, one expects that the temporal relationship of HD cells should persist in different brain states. To this end, neuronal ensembles were monitored during sleep sessions (5 hours ±1 s.d.), before and/or after active exploration in the open field. Sleep stages were classified as Slow-Wave Sleep (SWS) or Rapid Eye Movement Sleep (REM) based on the animal’s movement and time-resolved spectra of the local field potential recorded from the hippocampus or the PoS (Fig. 1c, see Methods). During sleep, individual neurons formed robustly similar sequential patterns as in the waking state. Using a Bayesian-based decoding of the HD signal from the population of HD cells (see Methods) and the animal’s actual head orientation (Fig. 1c), we could infer a ‘virtual gaze’ — i.e., which direction the mouse was ‘looking’ — during sleep (Fig. 1c,d; Supplementary Fig. 3; Supplementary Movie 1). Quantitative brain state comparisons of firing patterns revealed three important features of the ongoing network dynamics. First, the firing rates of HD neurons remained strongly correlated across brain states (Fig. 2a). Second, the pairwise correlations between HD neuron pairs both within and across structures were robustly similar across states (Fig. 2b), indicating the preservation of a coherent representation. Third, the rate of change of the virtual gaze differed between brain states. The angular velocity of the internal HD signal, either estimated by a Bayesian decoder or from the temporal profile of pairwise cross-correlograms (Fig. 2c–e, see Methods and Supplementary Fig. 4), was approximately tenfold faster during SWS than during waking (Fig. 2e,f), similar to the temporal “compression” of unit correlations observed in the hippocampus29–31 and neocortex32,33. Angular velocity of the internal HD signal was similar during waking and REM (Fig. 2e,f).


Internally organized mechanisms of the head direction sense.

Peyrache A, Lacroix MM, Petersen PC, Buzsáki G - Nat. Neurosci. (2015)

Brain-state dependent dynamics of the HD signala: Firing rates during SWS (left) and REM (right) plotted against waking firing rates for ADn cells (red, n = 215) and PoS cells (blue, n = 62). Insets: Pearson correlation r values. b: Same as a for pairwise correlations (ADn pairs, n = 970; PoS pairs, n = 92). c: Examples of cross-correlations for three HD cell pairs during waking, SWS and REM. Normalized histograms with 1 representing chance. Polar plots display HD fields of the pairs. d: Same as a for all ADn HD cell pairs, sorted by the magnitude of difference between waking preferred directions (shown in right most panel). Each cross-correlogram was normalized between their minimum (dark blue) and maximum (dark red) values. Cell identity order is the same in each panel. e: Left: histogram of average angular velocity estimated from individual cross-correlograms during wake (black) and REM (yellow). Higher angular speed results in more “compressed” cross-correlograms. Their temporal profiles were thus used to quantify the average drifting speed (see Methods). Top: distribution of actual or Bayesian estimates of HD angular speed. Right: same for SWS. f: Distribution of ratios between sleep and wake-estimated angular velocities for ADN-ADN and PoS-PoS HD cell pairs. Boxplot in e and f show median and quartiles.
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Figure 2: Brain-state dependent dynamics of the HD signala: Firing rates during SWS (left) and REM (right) plotted against waking firing rates for ADn cells (red, n = 215) and PoS cells (blue, n = 62). Insets: Pearson correlation r values. b: Same as a for pairwise correlations (ADn pairs, n = 970; PoS pairs, n = 92). c: Examples of cross-correlations for three HD cell pairs during waking, SWS and REM. Normalized histograms with 1 representing chance. Polar plots display HD fields of the pairs. d: Same as a for all ADn HD cell pairs, sorted by the magnitude of difference between waking preferred directions (shown in right most panel). Each cross-correlogram was normalized between their minimum (dark blue) and maximum (dark red) values. Cell identity order is the same in each panel. e: Left: histogram of average angular velocity estimated from individual cross-correlograms during wake (black) and REM (yellow). Higher angular speed results in more “compressed” cross-correlograms. Their temporal profiles were thus used to quantify the average drifting speed (see Methods). Top: distribution of actual or Bayesian estimates of HD angular speed. Right: same for SWS. f: Distribution of ratios between sleep and wake-estimated angular velocities for ADN-ADN and PoS-PoS HD cell pairs. Boxplot in e and f show median and quartiles.
Mentions: Under the hypothesis of an internally-organized HD system, one expects that the temporal relationship of HD cells should persist in different brain states. To this end, neuronal ensembles were monitored during sleep sessions (5 hours ±1 s.d.), before and/or after active exploration in the open field. Sleep stages were classified as Slow-Wave Sleep (SWS) or Rapid Eye Movement Sleep (REM) based on the animal’s movement and time-resolved spectra of the local field potential recorded from the hippocampus or the PoS (Fig. 1c, see Methods). During sleep, individual neurons formed robustly similar sequential patterns as in the waking state. Using a Bayesian-based decoding of the HD signal from the population of HD cells (see Methods) and the animal’s actual head orientation (Fig. 1c), we could infer a ‘virtual gaze’ — i.e., which direction the mouse was ‘looking’ — during sleep (Fig. 1c,d; Supplementary Fig. 3; Supplementary Movie 1). Quantitative brain state comparisons of firing patterns revealed three important features of the ongoing network dynamics. First, the firing rates of HD neurons remained strongly correlated across brain states (Fig. 2a). Second, the pairwise correlations between HD neuron pairs both within and across structures were robustly similar across states (Fig. 2b), indicating the preservation of a coherent representation. Third, the rate of change of the virtual gaze differed between brain states. The angular velocity of the internal HD signal, either estimated by a Bayesian decoder or from the temporal profile of pairwise cross-correlograms (Fig. 2c–e, see Methods and Supplementary Fig. 4), was approximately tenfold faster during SWS than during waking (Fig. 2e,f), similar to the temporal “compression” of unit correlations observed in the hippocampus29–31 and neocortex32,33. Angular velocity of the internal HD signal was similar during waking and REM (Fig. 2e,f).

Bottom Line: The temporal correlation structure of HD neurons was preserved during sleep, characterized by a 60°-wide correlated neuronal firing (activity packet), both within and across these two brain structures.During rapid eye movement sleep, the spontaneous drift of the activity packet was similar to that observed during waking and accelerated tenfold during slow-wave sleep.These findings demonstrate that peripheral inputs impinge on an internally organized network, which provides amplification and enhanced precision of the HD signal.

View Article: PubMed Central - PubMed

Affiliation: The Neuroscience Institute, School of Medicine and Center for Neural Science, New York University, New York, New York, USA.

ABSTRACT
The head-direction (HD) system functions as a compass, with member neurons robustly increasing their firing rates when the animal's head points in a specific direction. HD neurons may be driven by peripheral sensors or, as computational models postulate, internally generated (attractor) mechanisms. We addressed the contributions of stimulus-driven and internally generated activity by recording ensembles of HD neurons in the antero-dorsal thalamic nucleus and the post-subiculum of mice by comparing their activity in various brain states. The temporal correlation structure of HD neurons was preserved during sleep, characterized by a 60°-wide correlated neuronal firing (activity packet), both within and across these two brain structures. During rapid eye movement sleep, the spontaneous drift of the activity packet was similar to that observed during waking and accelerated tenfold during slow-wave sleep. These findings demonstrate that peripheral inputs impinge on an internally organized network, which provides amplification and enhanced precision of the HD signal.

Show MeSH