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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.


Spatio-temporal correlations caused by continuous movement of the bump across the network in a given chart. (A) Illustration of how a linear track (white rectangle) can be represented by a subset of neurons in a given chart (displayed as in Figure 2A). For our analysis, we randomly selected 20 neurons with place fields on the linear track to form a template (filled circles). (B) Spikes fired by the template neurons during selected spiking events. The place cells are sorted according to the x-coordinate of the place field centers. For this plot, we selected spiking events that show a clear positive or negative correlation between spike timing and place field orderings. (C) Distributions of rank-order correlations between the time of the first spike in spiking events and the spatial templates (red bars). Each of the six panels corresponds to a template drawn from one of the six charts stored in the network. Spiking events were limited to the times when the bump was located in the template's chart. Large positive and negative correlation values indicate events of forward and reverse preplay, respectively. The correlations obtained in the simulations are significantly different from the shuffled distributions (green bars; Kolmogrov–Smirnov test, p = 0.02, p < 10−35, p < 10−20, p < 10−95, p < 10−7, p < 10−17).
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Figure 5: Spatio-temporal correlations caused by continuous movement of the bump across the network in a given chart. (A) Illustration of how a linear track (white rectangle) can be represented by a subset of neurons in a given chart (displayed as in Figure 2A). For our analysis, we randomly selected 20 neurons with place fields on the linear track to form a template (filled circles). (B) Spikes fired by the template neurons during selected spiking events. The place cells are sorted according to the x-coordinate of the place field centers. For this plot, we selected spiking events that show a clear positive or negative correlation between spike timing and place field orderings. (C) Distributions of rank-order correlations between the time of the first spike in spiking events and the spatial templates (red bars). Each of the six panels corresponds to a template drawn from one of the six charts stored in the network. Spiking events were limited to the times when the bump was located in the template's chart. Large positive and negative correlation values indicate events of forward and reverse preplay, respectively. The correlations obtained in the simulations are significantly different from the shuffled distributions (green bars; Kolmogrov–Smirnov test, p = 0.02, p < 10−35, p < 10−20, p < 10−95, p < 10−7, p < 10−17).

Mentions: We hypothesized that preplay could be generated by the following mechanism. First, continuous movement of the bump in a chart generates OSA that is significantly correlated with the order of PFCs in that chart. Second, the network stores a small number of charts and one of the charts is reused to represent a novel environment. As a result the PFCs in the novel environment would be correlated with the OSA recorded before the first exposure to the novel environment. To examine this hypothesis, we randomly selected a set of 20 template neurons with place fields within a simulated linear track, and then identified spiking events during which a large number of the template neurons fired spikes (see Materials and Methods and Figure 5A). Selected examples of spiking events show large correlations between spike times during the spiking event and PFCs (Figure 5B). We next calculated the rank-order correlation (Equation 9) between the PFCs and those spiking events that occurred when the bump was located in the template chart (Figure 5C). A large fraction of the correlation values had large positive or negative values, suggesting that forward and reverse preplay could be found in the model. The correlations were much larger than expected by chance as indicated by a comparison of the network-generated distribution of correlations to the shuffled distribution (see Materials and Methods).


A computational model for preplay in the hippocampus.

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

Spatio-temporal correlations caused by continuous movement of the bump across the network in a given chart. (A) Illustration of how a linear track (white rectangle) can be represented by a subset of neurons in a given chart (displayed as in Figure 2A). For our analysis, we randomly selected 20 neurons with place fields on the linear track to form a template (filled circles). (B) Spikes fired by the template neurons during selected spiking events. The place cells are sorted according to the x-coordinate of the place field centers. For this plot, we selected spiking events that show a clear positive or negative correlation between spike timing and place field orderings. (C) Distributions of rank-order correlations between the time of the first spike in spiking events and the spatial templates (red bars). Each of the six panels corresponds to a template drawn from one of the six charts stored in the network. Spiking events were limited to the times when the bump was located in the template's chart. Large positive and negative correlation values indicate events of forward and reverse preplay, respectively. The correlations obtained in the simulations are significantly different from the shuffled distributions (green bars; Kolmogrov–Smirnov test, p = 0.02, p < 10−35, p < 10−20, p < 10−95, p < 10−7, p < 10−17).
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Figure 5: Spatio-temporal correlations caused by continuous movement of the bump across the network in a given chart. (A) Illustration of how a linear track (white rectangle) can be represented by a subset of neurons in a given chart (displayed as in Figure 2A). For our analysis, we randomly selected 20 neurons with place fields on the linear track to form a template (filled circles). (B) Spikes fired by the template neurons during selected spiking events. The place cells are sorted according to the x-coordinate of the place field centers. For this plot, we selected spiking events that show a clear positive or negative correlation between spike timing and place field orderings. (C) Distributions of rank-order correlations between the time of the first spike in spiking events and the spatial templates (red bars). Each of the six panels corresponds to a template drawn from one of the six charts stored in the network. Spiking events were limited to the times when the bump was located in the template's chart. Large positive and negative correlation values indicate events of forward and reverse preplay, respectively. The correlations obtained in the simulations are significantly different from the shuffled distributions (green bars; Kolmogrov–Smirnov test, p = 0.02, p < 10−35, p < 10−20, p < 10−95, p < 10−7, p < 10−17).
Mentions: We hypothesized that preplay could be generated by the following mechanism. First, continuous movement of the bump in a chart generates OSA that is significantly correlated with the order of PFCs in that chart. Second, the network stores a small number of charts and one of the charts is reused to represent a novel environment. As a result the PFCs in the novel environment would be correlated with the OSA recorded before the first exposure to the novel environment. To examine this hypothesis, we randomly selected a set of 20 template neurons with place fields within a simulated linear track, and then identified spiking events during which a large number of the template neurons fired spikes (see Materials and Methods and Figure 5A). Selected examples of spiking events show large correlations between spike times during the spiking event and PFCs (Figure 5B). We next calculated the rank-order correlation (Equation 9) between the PFCs and those spiking events that occurred when the bump was located in the template chart (Figure 5C). A large fraction of the correlation values had large positive or negative values, suggesting that forward and reverse preplay could be found in the model. The correlations were much larger than expected by chance as indicated by a comparison of the network-generated distribution of correlations to the shuffled distribution (see Materials and Methods).

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.