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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.


Model definition. (A) The two dimensional arrangement of excitatory cells represents a map of the environment, so that neighboring cells have neighbouring place field centers (PFC). By randomly rearranging the PFC's of the cells, the same neurons can represent different environments (multiple charts). (B) One dimensional representation of the network architecture. 80% of the units in the network are excitatory (lower layer) and the remaining 20% are inhibitory neurons (top layer). There are all-to-all connections between excitatory and inhibitory neurons and among inhibitory neurons, denoted by WIE, WEI and WII. As a result, each excitatory neuron receives global inhibition that is a function of the total network activity. Each excitatory neuron also receives a constant bias input, Ibias. The connectivity pattern between excitatory neurons in each chart, WEE, is a Gaussian function of the distance between their PFC's. The total excitatory weight matrix of the network is the summation of the weights in each chart.
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Figure 1: Model definition. (A) The two dimensional arrangement of excitatory cells represents a map of the environment, so that neighboring cells have neighbouring place field centers (PFC). By randomly rearranging the PFC's of the cells, the same neurons can represent different environments (multiple charts). (B) One dimensional representation of the network architecture. 80% of the units in the network are excitatory (lower layer) and the remaining 20% are inhibitory neurons (top layer). There are all-to-all connections between excitatory and inhibitory neurons and among inhibitory neurons, denoted by WIE, WEI and WII. As a result, each excitatory neuron receives global inhibition that is a function of the total network activity. Each excitatory neuron also receives a constant bias input, Ibias. The connectivity pattern between excitatory neurons in each chart, WEE, is a Gaussian function of the distance between their PFC's. The total excitatory weight matrix of the network is the summation of the weights in each chart.

Mentions: We base our model of CA3 on two models proposed by Hopfield (2010) and Samsonovich and McNaughton (1997). The model consists of integrate- and fire neurons arranged in two sub-layers of excitatory and inhibitory units (Figure 1). The membrane potential of excitatory (s = E) and inhibitory (s = I) neurons is defined as follows:(1)duisdt=−uisτcells+Ibias+IsE−IsI−Jis+Inoise,where usi is the membrane potential of the i-th neuron, and Ibias is a constant input current which determines the level of excitability of each neuron. In the absence of synaptic and noise inputs, the membrane potential relaxes to τscellIbias. τscell = 20 ms is the integration time constant for both cell types. Inoise is a Gaussian noise input current with zero mean and standard deviation of 0.2.


A computational model for preplay in the hippocampus.

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

Model definition. (A) The two dimensional arrangement of excitatory cells represents a map of the environment, so that neighboring cells have neighbouring place field centers (PFC). By randomly rearranging the PFC's of the cells, the same neurons can represent different environments (multiple charts). (B) One dimensional representation of the network architecture. 80% of the units in the network are excitatory (lower layer) and the remaining 20% are inhibitory neurons (top layer). There are all-to-all connections between excitatory and inhibitory neurons and among inhibitory neurons, denoted by WIE, WEI and WII. As a result, each excitatory neuron receives global inhibition that is a function of the total network activity. Each excitatory neuron also receives a constant bias input, Ibias. The connectivity pattern between excitatory neurons in each chart, WEE, is a Gaussian function of the distance between their PFC's. The total excitatory weight matrix of the network is the summation of the weights in each chart.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: Model definition. (A) The two dimensional arrangement of excitatory cells represents a map of the environment, so that neighboring cells have neighbouring place field centers (PFC). By randomly rearranging the PFC's of the cells, the same neurons can represent different environments (multiple charts). (B) One dimensional representation of the network architecture. 80% of the units in the network are excitatory (lower layer) and the remaining 20% are inhibitory neurons (top layer). There are all-to-all connections between excitatory and inhibitory neurons and among inhibitory neurons, denoted by WIE, WEI and WII. As a result, each excitatory neuron receives global inhibition that is a function of the total network activity. Each excitatory neuron also receives a constant bias input, Ibias. The connectivity pattern between excitatory neurons in each chart, WEE, is a Gaussian function of the distance between their PFC's. The total excitatory weight matrix of the network is the summation of the weights in each chart.
Mentions: We base our model of CA3 on two models proposed by Hopfield (2010) and Samsonovich and McNaughton (1997). The model consists of integrate- and fire neurons arranged in two sub-layers of excitatory and inhibitory units (Figure 1). The membrane potential of excitatory (s = E) and inhibitory (s = I) neurons is defined as follows:(1)duisdt=−uisτcells+Ibias+IsE−IsI−Jis+Inoise,where usi is the membrane potential of the i-th neuron, and Ibias is a constant input current which determines the level of excitability of each neuron. In the absence of synaptic and noise inputs, the membrane potential relaxes to τscellIbias. τscell = 20 ms is the integration time constant for both cell types. Inoise is a Gaussian noise input current with zero mean and standard deviation of 0.2.

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.