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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.


Significant preplay of novel charts. (A) Spatio-temporal correlations for a chart that was not present in the network when the network activity was recorded (red bars) compared to the shuffled distribution (green bars). One of the comparisons showed a significant difference (Kolomogrov–Smirnov test, p = 0.20, p = 0.01, and p = 0.13). (B) Fraction of simulation runs that show significant preplay as a function of the session length.
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Figure 9: Significant preplay of novel charts. (A) Spatio-temporal correlations for a chart that was not present in the network when the network activity was recorded (red bars) compared to the shuffled distribution (green bars). One of the comparisons showed a significant difference (Kolomogrov–Smirnov test, p = 0.20, p = 0.01, and p = 0.13). (B) Fraction of simulation runs that show significant preplay as a function of the session length.

Mentions: Continuing this line of argument, it is possible that the chart, which is preplayed, was not present in the network when the moving bump of activity generated the OSA. Any new chart, even if generated later and randomly, will have some large correlations with a pre-existing chart. We therefore simulated network activity with three charts stored in the network, then constructed a new chart, selected a template from this chart and calculated the correlations between PFCs and spiking events. Sometimes we found significant preplay, but sometimes we did not (Figure 9A). To examine this effect more closely, we repeated this analysis for 100 different selections of cells and 20 different network structures, as in Figure 6A. We found that it is possible that significant preplay of a novel chart is seen (Figure 9B), but, as expected, the likelihood is lower than that for preplay of pre-existing charts. However, the likelihoods are still high enough that the possibility cannot be excluded that preplay can be found experimentally, even when a novel environment is represented by a newly constructed chart.


A computational model for preplay in the hippocampus.

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

Significant preplay of novel charts. (A) Spatio-temporal correlations for a chart that was not present in the network when the network activity was recorded (red bars) compared to the shuffled distribution (green bars). One of the comparisons showed a significant difference (Kolomogrov–Smirnov test, p = 0.20, p = 0.01, and p = 0.13). (B) Fraction of simulation runs that show significant preplay as a function of the session length.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3824291&req=5

Figure 9: Significant preplay of novel charts. (A) Spatio-temporal correlations for a chart that was not present in the network when the network activity was recorded (red bars) compared to the shuffled distribution (green bars). One of the comparisons showed a significant difference (Kolomogrov–Smirnov test, p = 0.20, p = 0.01, and p = 0.13). (B) Fraction of simulation runs that show significant preplay as a function of the session length.
Mentions: Continuing this line of argument, it is possible that the chart, which is preplayed, was not present in the network when the moving bump of activity generated the OSA. Any new chart, even if generated later and randomly, will have some large correlations with a pre-existing chart. We therefore simulated network activity with three charts stored in the network, then constructed a new chart, selected a template from this chart and calculated the correlations between PFCs and spiking events. Sometimes we found significant preplay, but sometimes we did not (Figure 9A). To examine this effect more closely, we repeated this analysis for 100 different selections of cells and 20 different network structures, as in Figure 6A. We found that it is possible that significant preplay of a novel chart is seen (Figure 9B), but, as expected, the likelihood is lower than that for preplay of pre-existing charts. However, the likelihoods are still high enough that the possibility cannot be excluded that preplay can be found experimentally, even when a novel environment is represented by a newly constructed chart.

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.