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A computational model for preplay in the hippocampus.

Azizi AH, Wiskott L, Cheng S - Front Comput Neurosci (2013)

Bottom Line: Recent experiments found this correlation even between offline sequential activity (OSA) recorded before the animal ran in a novel environment and the place fields in that environment.Our results suggest two different accounts for preplay.Either an existing chart is re-used to represent a novel environment or a new chart is formed.

View Article: PubMed Central - PubMed

Affiliation: Mercator Research Group "Structure of Memory," Department of Psychology, Ruhr-University Bochum Bochum, Germany.

ABSTRACT
The hippocampal network produces sequences of neural activity even when there is no time-varying external drive. In offline states, the temporal sequence in which place cells fire spikes correlates with the sequence of their place fields. Recent experiments found this correlation even between offline sequential activity (OSA) recorded before the animal ran in a novel environment and the place fields in that environment. This preplay phenomenon suggests that OSA is generated intrinsically in the hippocampal network, and not established by external sensory inputs. Previous studies showed that continuous attractor networks with asymmetric patterns of connectivity, or with slow, local negative feedback, can generate sequential activity. This mechanism could account for preplay if the network only represented a single spatial map, or chart. However, global remapping in the hippocampus implies that multiple charts are represented simultaneously in the hippocampal network and it remains unknown whether the network with multiple charts can account for preplay. Here we show that it can. Driven with random inputs, the model generates sequences in every chart. Place fields in a given chart and OSA generated by the network are highly correlated. We also find significant correlations, albeit less frequently, even when the OSA is correlated with a new chart in which place fields are randomly scattered. These correlations arise from random correlations between the orderings of place fields in the new chart and those in a pre-existing chart. Our results suggest two different accounts for preplay. Either an existing chart is re-used to represent a novel environment or a new chart is formed.

No MeSH data available.


Random correlations between charts gives rise to preplay of off-charts. (A) Relationship between three correlation values tied to spiking events. Rbump: rank order correlation between time of first spike and the positions of the corresponding cells in a template from the chart that hosts the bump; Rother: rank order correlation between time of first spike and the positions of the corresponding cells in a template from an off-chart; and Rchart: rank order correlation between the two templates. The color of the points change from blue (Rchart = −1) to red (Rchart = 1). The cloud of points indicates that large off-chart correlations occur only when Rbump and Rchart are large, suggesting that it is the movement of the bump and the random correlation between charts that generates significant preplay in the off-chart. (B) To make this even more visible we plot Rother vs. Rbump for 100 spiking events for which the corresponding Rchart is closest to 1, (C) for 100 events with Rchart closest to −1, and (D) for 100 events with Rchart values closest to zero.
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Figure 8: Random correlations between charts gives rise to preplay of off-charts. (A) Relationship between three correlation values tied to spiking events. Rbump: rank order correlation between time of first spike and the positions of the corresponding cells in a template from the chart that hosts the bump; Rother: rank order correlation between time of first spike and the positions of the corresponding cells in a template from an off-chart; and Rchart: rank order correlation between the two templates. The color of the points change from blue (Rchart = −1) to red (Rchart = 1). The cloud of points indicates that large off-chart correlations occur only when Rbump and Rchart are large, suggesting that it is the movement of the bump and the random correlation between charts that generates significant preplay in the off-chart. (B) To make this even more visible we plot Rother vs. Rbump for 100 spiking events for which the corresponding Rchart is closest to 1, (C) for 100 events with Rchart closest to −1, and (D) for 100 events with Rchart values closest to zero.

Mentions: We next investigated whether reusing a chart was the only way to obtain preplay in our network. We have obtained a first hint that other mechanisms are possible when we studied the correlations between PFCs and only those spiking events that occurred when the bump was located in an off-chart. As we mentioned in the preceding section, we expected these correlations to be small since the charts are generated independently of one another. This was indeed the case, but to our surprise these correlations were sometimes significant (Figure 7). To find the source of the unexpected correlations, we investigated the relationship between (A) the correlations between spiking events and the PFCs in the chart that hosts the bump Rbump, (B) the correlations between spiking events and the PFCs in an off-chart Rother, and (C) the correlations between the PFCs of the active cells in the template in the two charts Rchart. Since we generated each chart independently of the others, PFCs in two charts quite frequently have random non-zero correlation values. Large random correlations are quite likely since they are calculated between PFCs of cells that are active in the spiking events, and by our definition, this number can be as low as five. Indeed, we found a consistent relationship between these three correlations (Figure 8). When Rchart is large and positive, Rbump and Rother are similar (Figure 8B); when Rchart is large and negative, Rbump and Rother are inversely related (Figure 8C). On the other hand, when the Rchart correlations are near zero, the relationship between Rbump and Rother vanishes (Figure 8D). We therefore conclude that correlations indicative of preplay can be introduced by random correlations between the PFCs in two charts.


A computational model for preplay in the hippocampus.

Azizi AH, Wiskott L, Cheng S - Front Comput Neurosci (2013)

Random correlations between charts gives rise to preplay of off-charts. (A) Relationship between three correlation values tied to spiking events. Rbump: rank order correlation between time of first spike and the positions of the corresponding cells in a template from the chart that hosts the bump; Rother: rank order correlation between time of first spike and the positions of the corresponding cells in a template from an off-chart; and Rchart: rank order correlation between the two templates. The color of the points change from blue (Rchart = −1) to red (Rchart = 1). The cloud of points indicates that large off-chart correlations occur only when Rbump and Rchart are large, suggesting that it is the movement of the bump and the random correlation between charts that generates significant preplay in the off-chart. (B) To make this even more visible we plot Rother vs. Rbump for 100 spiking events for which the corresponding Rchart is closest to 1, (C) for 100 events with Rchart closest to −1, and (D) for 100 events with Rchart values closest to zero.
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Related In: Results  -  Collection

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Figure 8: Random correlations between charts gives rise to preplay of off-charts. (A) Relationship between three correlation values tied to spiking events. Rbump: rank order correlation between time of first spike and the positions of the corresponding cells in a template from the chart that hosts the bump; Rother: rank order correlation between time of first spike and the positions of the corresponding cells in a template from an off-chart; and Rchart: rank order correlation between the two templates. The color of the points change from blue (Rchart = −1) to red (Rchart = 1). The cloud of points indicates that large off-chart correlations occur only when Rbump and Rchart are large, suggesting that it is the movement of the bump and the random correlation between charts that generates significant preplay in the off-chart. (B) To make this even more visible we plot Rother vs. Rbump for 100 spiking events for which the corresponding Rchart is closest to 1, (C) for 100 events with Rchart closest to −1, and (D) for 100 events with Rchart values closest to zero.
Mentions: We next investigated whether reusing a chart was the only way to obtain preplay in our network. We have obtained a first hint that other mechanisms are possible when we studied the correlations between PFCs and only those spiking events that occurred when the bump was located in an off-chart. As we mentioned in the preceding section, we expected these correlations to be small since the charts are generated independently of one another. This was indeed the case, but to our surprise these correlations were sometimes significant (Figure 7). To find the source of the unexpected correlations, we investigated the relationship between (A) the correlations between spiking events and the PFCs in the chart that hosts the bump Rbump, (B) the correlations between spiking events and the PFCs in an off-chart Rother, and (C) the correlations between the PFCs of the active cells in the template in the two charts Rchart. Since we generated each chart independently of the others, PFCs in two charts quite frequently have random non-zero correlation values. Large random correlations are quite likely since they are calculated between PFCs of cells that are active in the spiking events, and by our definition, this number can be as low as five. Indeed, we found a consistent relationship between these three correlations (Figure 8). When Rchart is large and positive, Rbump and Rother are similar (Figure 8B); when Rchart is large and negative, Rbump and Rother are inversely related (Figure 8C). On the other hand, when the Rchart correlations are near zero, the relationship between Rbump and Rother vanishes (Figure 8D). We therefore conclude that correlations indicative of preplay can be introduced by random correlations between the PFCs in two charts.

Bottom Line: Recent experiments found this correlation even between offline sequential activity (OSA) recorded before the animal ran in a novel environment and the place fields in that environment.Our results suggest two different accounts for preplay.Either an existing chart is re-used to represent a novel environment or a new chart is formed.

View Article: PubMed Central - PubMed

Affiliation: Mercator Research Group "Structure of Memory," Department of Psychology, Ruhr-University Bochum Bochum, Germany.

ABSTRACT
The hippocampal network produces sequences of neural activity even when there is no time-varying external drive. In offline states, the temporal sequence in which place cells fire spikes correlates with the sequence of their place fields. Recent experiments found this correlation even between offline sequential activity (OSA) recorded before the animal ran in a novel environment and the place fields in that environment. This preplay phenomenon suggests that OSA is generated intrinsically in the hippocampal network, and not established by external sensory inputs. Previous studies showed that continuous attractor networks with asymmetric patterns of connectivity, or with slow, local negative feedback, can generate sequential activity. This mechanism could account for preplay if the network only represented a single spatial map, or chart. However, global remapping in the hippocampus implies that multiple charts are represented simultaneously in the hippocampal network and it remains unknown whether the network with multiple charts can account for preplay. Here we show that it can. Driven with random inputs, the model generates sequences in every chart. Place fields in a given chart and OSA generated by the network are highly correlated. We also find significant correlations, albeit less frequently, even when the OSA is correlated with a new chart in which place fields are randomly scattered. These correlations arise from random correlations between the orderings of place fields in the new chart and those in a pre-existing chart. Our results suggest two different accounts for preplay. Either an existing chart is re-used to represent a novel environment or a new chart is formed.

No MeSH data available.