Limits...
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.


Bump formation in the network without spike-frequency adaptation. (A) Local excitation and global inhibition give rise to a bump attractor, a region of clustered activity in the excitatory layer. Because of the competitive nature of the network activity, the bump can form in only one chart (here the third chart). In the other charts, the activity appears randomly scattered. The place field centers are represented by blue circles. Spiking cell are indicted by the red filled circles. (B) Standard deviation of the activity as a function of time. The horizontal red bar indicates the time period during which a biased external input was applied to one fifth of the excitatory neurons, selected randomly. The time at which network activity is shown in (A) is indicated by the vertical red bar. (C,D), Cell activity when the network stores 4 (blue bars), 6 (green bars) and 8 (red bars) charts. Shown are the distributions of the mean firing rate of the cells (C) and of the average adaptation input current, which was disconnected from the network dynamics (D).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3824291&req=5

Figure 2: Bump formation in the network without spike-frequency adaptation. (A) Local excitation and global inhibition give rise to a bump attractor, a region of clustered activity in the excitatory layer. Because of the competitive nature of the network activity, the bump can form in only one chart (here the third chart). In the other charts, the activity appears randomly scattered. The place field centers are represented by blue circles. Spiking cell are indicted by the red filled circles. (B) Standard deviation of the activity as a function of time. The horizontal red bar indicates the time period during which a biased external input was applied to one fifth of the excitatory neurons, selected randomly. The time at which network activity is shown in (A) is indicated by the vertical red bar. (C,D), Cell activity when the network stores 4 (blue bars), 6 (green bars) and 8 (red bars) charts. Shown are the distributions of the mean firing rate of the cells (C) and of the average adaptation input current, which was disconnected from the network dynamics (D).

Mentions: We first examined the properties of the bump attractor in a CANN that stores multiple charts with the adaptation currents Ji removed from the network dynamics in Equation 1. In this way, the adaptation current had no effect on the network dynamics but was allowed to accumulate. We return to this point in the next paragraph. The network activity was initialized by providing constant input currents for 1 s to one fifth of the population, which was selected randomly. After this period, the activity self-organized into a small region of the network (Figure 2A). This bump formed only in one chart. Since the charts were drawn independently of one another, the active cells were scattered seemingly randomly when arranged in a different chart (Figures 2A,B). Which chart the bump forms in, is determined by random heterogeneities in the network structure that break the symmetry. These heterogeneities are present in the distances between PFCs within one chart, in the cross-talk from other charts stored in the connectivity matrix, and the noise input.


A computational model for preplay in the hippocampus.

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

Bump formation in the network without spike-frequency adaptation. (A) Local excitation and global inhibition give rise to a bump attractor, a region of clustered activity in the excitatory layer. Because of the competitive nature of the network activity, the bump can form in only one chart (here the third chart). In the other charts, the activity appears randomly scattered. The place field centers are represented by blue circles. Spiking cell are indicted by the red filled circles. (B) Standard deviation of the activity as a function of time. The horizontal red bar indicates the time period during which a biased external input was applied to one fifth of the excitatory neurons, selected randomly. The time at which network activity is shown in (A) is indicated by the vertical red bar. (C,D), Cell activity when the network stores 4 (blue bars), 6 (green bars) and 8 (red bars) charts. Shown are the distributions of the mean firing rate of the cells (C) and of the average adaptation input current, which was disconnected from the network dynamics (D).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Bump formation in the network without spike-frequency adaptation. (A) Local excitation and global inhibition give rise to a bump attractor, a region of clustered activity in the excitatory layer. Because of the competitive nature of the network activity, the bump can form in only one chart (here the third chart). In the other charts, the activity appears randomly scattered. The place field centers are represented by blue circles. Spiking cell are indicted by the red filled circles. (B) Standard deviation of the activity as a function of time. The horizontal red bar indicates the time period during which a biased external input was applied to one fifth of the excitatory neurons, selected randomly. The time at which network activity is shown in (A) is indicated by the vertical red bar. (C,D), Cell activity when the network stores 4 (blue bars), 6 (green bars) and 8 (red bars) charts. Shown are the distributions of the mean firing rate of the cells (C) and of the average adaptation input current, which was disconnected from the network dynamics (D).
Mentions: We first examined the properties of the bump attractor in a CANN that stores multiple charts with the adaptation currents Ji removed from the network dynamics in Equation 1. In this way, the adaptation current had no effect on the network dynamics but was allowed to accumulate. We return to this point in the next paragraph. The network activity was initialized by providing constant input currents for 1 s to one fifth of the population, which was selected randomly. After this period, the activity self-organized into a small region of the network (Figure 2A). This bump formed only in one chart. Since the charts were drawn independently of one another, the active cells were scattered seemingly randomly when arranged in a different chart (Figures 2A,B). Which chart the bump forms in, is determined by random heterogeneities in the network structure that break the symmetry. These heterogeneities are present in the distances between PFCs within one chart, in the cross-talk from other charts stored in the connectivity matrix, and the noise input.

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.