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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.


Observing significant preplay in session data. (A) Distributions of spatio-temporal correlations when all spiking events that involved the template neurons are included, irrespective of which chart the bump was located in during the spiking event (red bars). For each panel, the template was drawn from a different one of the six charts. The data set is the same as in Figure 5. The network-generated correlations are significantly different from the shuffled distributions (green bars; Kolmogrov–Smirnov test, p < 10−5, p < 10−11, p < 10−9, p < 10−33, p < 10−13 and p < 10−21). [(B), left] Fraction of simulation runs that yield significant preplay as a function of the session length and number of charts stored in the network. [(B) right] The recording time required to obtain significant preplay in 50% of runs.
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Figure 6: Observing significant preplay in session data. (A) Distributions of spatio-temporal correlations when all spiking events that involved the template neurons are included, irrespective of which chart the bump was located in during the spiking event (red bars). For each panel, the template was drawn from a different one of the six charts. The data set is the same as in Figure 5. The network-generated correlations are significantly different from the shuffled distributions (green bars; Kolmogrov–Smirnov test, p < 10−5, p < 10−11, p < 10−9, p < 10−33, p < 10−13 and p < 10−21). [(B), left] Fraction of simulation runs that yield significant preplay as a function of the session length and number of charts stored in the network. [(B) right] The recording time required to obtain significant preplay in 50% of runs.

Mentions: An analysis such as the one above is impossible in experimental data, since it is unknown which chart the bump, if it exists, is located in. The analysis has to include all spiking events that the template cells are involved in, including those when the bump is located in one of the other charts (off-chart). We therefore analyzed all spiking events generated by the network regardless of which chart the bump was in at the time of the event. The off-chart spiking events contributed mostly low correlation values (Figure 6A). This result was expected since the movement of the bump in an off-chart does not usually result in sequential activation of the neurons in the template chart. Yet, the network-generated distribution was significantly different from the shuffled distributions. This result raises the possibility that the experimentally observed preplay might be the result of the intrinsic network structure, but it is only one example.


A computational model for preplay in the hippocampus.

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

Observing significant preplay in session data. (A) Distributions of spatio-temporal correlations when all spiking events that involved the template neurons are included, irrespective of which chart the bump was located in during the spiking event (red bars). For each panel, the template was drawn from a different one of the six charts. The data set is the same as in Figure 5. The network-generated correlations are significantly different from the shuffled distributions (green bars; Kolmogrov–Smirnov test, p < 10−5, p < 10−11, p < 10−9, p < 10−33, p < 10−13 and p < 10−21). [(B), left] Fraction of simulation runs that yield significant preplay as a function of the session length and number of charts stored in the network. [(B) right] The recording time required to obtain significant preplay in 50% of runs.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 6: Observing significant preplay in session data. (A) Distributions of spatio-temporal correlations when all spiking events that involved the template neurons are included, irrespective of which chart the bump was located in during the spiking event (red bars). For each panel, the template was drawn from a different one of the six charts. The data set is the same as in Figure 5. The network-generated correlations are significantly different from the shuffled distributions (green bars; Kolmogrov–Smirnov test, p < 10−5, p < 10−11, p < 10−9, p < 10−33, p < 10−13 and p < 10−21). [(B), left] Fraction of simulation runs that yield significant preplay as a function of the session length and number of charts stored in the network. [(B) right] The recording time required to obtain significant preplay in 50% of runs.
Mentions: An analysis such as the one above is impossible in experimental data, since it is unknown which chart the bump, if it exists, is located in. The analysis has to include all spiking events that the template cells are involved in, including those when the bump is located in one of the other charts (off-chart). We therefore analyzed all spiking events generated by the network regardless of which chart the bump was in at the time of the event. The off-chart spiking events contributed mostly low correlation values (Figure 6A). This result was expected since the movement of the bump in an off-chart does not usually result in sequential activation of the neurons in the template chart. Yet, the network-generated distribution was significantly different from the shuffled distributions. This result raises the possibility that the experimentally observed preplay might be the result of the intrinsic network structure, but it is only one example.

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.