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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.


Kinematics of the bump. (A) The speed of the bump increases as a function of the adaptation increment. We identified continuous bump trajectories in time periods during which the standard deviation of activity was below threshold continuously in the same chart. The unadaptation time constant was τunadapt = 5 s. (B) The speed of the bump increases as a function of the unadaptation time constant. The adaptation increment was set at α = 0.02.
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Figure 4: Kinematics of the bump. (A) The speed of the bump increases as a function of the adaptation increment. We identified continuous bump trajectories in time periods during which the standard deviation of activity was below threshold continuously in the same chart. The unadaptation time constant was τunadapt = 5 s. (B) The speed of the bump increases as a function of the unadaptation time constant. The adaptation increment was set at α = 0.02.

Mentions: Without the adaptation current, the bump stays stationary until the end of the simulation. Even though the center of the bump jitters due to the noise in the system, the jitter amplitude is very small and hence the network cannot generate long temporal sequences across the network. That is why we need a mechanism that moves the bump of activity around. We employ an adaptation current, which models SFA. SFA moves the bump continuously in short trajectories in the neuronal sheet, making this mechanism a good candidate for a model of OSA (Figure 3A). We analyzed the kinetics of the bump movement by identifying time windows during which a stable bump remained in one chart and recorded the length of the bump's trajectory in this time window. When the trajectory lengths are plotted vs. the durations of the time windows, for a given adaptation increment α (Equation 3), the points fall on a straight line (Figure 4A). This result indicates that the bump moves predominantly at a constant speed. We further found that the mean velocity of bump propagation increases linearly with the adaptation increment for α < 0.03. For larger adaptation increments, the relationship with bump speed becomes more variable. The other parameter that governs SFA, the time constant τunadapt (Equation 2), also affects the speed of the bump movement linearly (Figure 4B). This result is in agreement with an earlier study that reported that the speed of the moving bump is modulated by the time scale of adaptation (Itskov et al., 2011a).


A computational model for preplay in the hippocampus.

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

Kinematics of the bump. (A) The speed of the bump increases as a function of the adaptation increment. We identified continuous bump trajectories in time periods during which the standard deviation of activity was below threshold continuously in the same chart. The unadaptation time constant was τunadapt = 5 s. (B) The speed of the bump increases as a function of the unadaptation time constant. The adaptation increment was set at α = 0.02.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: Kinematics of the bump. (A) The speed of the bump increases as a function of the adaptation increment. We identified continuous bump trajectories in time periods during which the standard deviation of activity was below threshold continuously in the same chart. The unadaptation time constant was τunadapt = 5 s. (B) The speed of the bump increases as a function of the unadaptation time constant. The adaptation increment was set at α = 0.02.
Mentions: Without the adaptation current, the bump stays stationary until the end of the simulation. Even though the center of the bump jitters due to the noise in the system, the jitter amplitude is very small and hence the network cannot generate long temporal sequences across the network. That is why we need a mechanism that moves the bump of activity around. We employ an adaptation current, which models SFA. SFA moves the bump continuously in short trajectories in the neuronal sheet, making this mechanism a good candidate for a model of OSA (Figure 3A). We analyzed the kinetics of the bump movement by identifying time windows during which a stable bump remained in one chart and recorded the length of the bump's trajectory in this time window. When the trajectory lengths are plotted vs. the durations of the time windows, for a given adaptation increment α (Equation 3), the points fall on a straight line (Figure 4A). This result indicates that the bump moves predominantly at a constant speed. We further found that the mean velocity of bump propagation increases linearly with the adaptation increment for α < 0.03. For larger adaptation increments, the relationship with bump speed becomes more variable. The other parameter that governs SFA, the time constant τunadapt (Equation 2), also affects the speed of the bump movement linearly (Figure 4B). This result is in agreement with an earlier study that reported that the speed of the moving bump is modulated by the time scale of adaptation (Itskov et al., 2011a).

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.