Associative memory networks for graph-based abstraction

Abstract

Our cognition relies on the ability of the brain to segment hierarchically structured events on multiple scales. Recent evidence suggests that the brain performs this event segmentation based on the structure of state-transition graphs behind sequential experiences. However, the underlying circuit mechanisms are only poorly understood. In this paper, we propose an extended attractor network model for the graph-based hierarchical computation, called as Laplacian associative memory. This model generates multiscale representations for communities (clusters) of associative links between memory items, and the scale is regulated by heterogenous modulation of inhibitory circuits. We analytically and numerically show that these representations correspond to graph Laplacian eigenvectors, a popular method for graph segmentation and dimensionality reduction. Finally, we demonstrate that our model with asymmetricity exhibits chunking resembling to hippocampal theta sequences. Our model connects graph theory and the attractor dynamics to provide a biologically plausible mechanism for abstraction in the brain.

Intrinsic dynamics of randomly clustered networks generate place fields and preplay of novel environments

“…In prior models of preplay and replay, a preexisting map of the environment is typically assumed to be contained within the recurrent connections of CA3 cells, such that cells with nearby place fields are more strongly connected (Figure 1a ). While this type of model successfully produces replay (Haga and Fukai, 2018 ;Pang and Fairhall, 2019 ), such a map would only be expected to exist in a familiar environment, after experience-dependent synaptic plasticity has had time to shape the network (Theodoni et al, 2018 ). It remains unclear how, in the absence of such a preexisting map of the environment, the hippocampus can generate both preplay and immediate replay of a novel environment.…”

“…Most replay models rely on a recurrent network structure in which a map of the environment is encoded in the recurrent connections of CA3 cells, such that cells with nearby place fields are more strongly connected. Some models assume this structure is pre-existing (Haga and Fukai, 2018 ;Pang and Fairhall, 2019 ), and some show how it could develop over time through synaptic plasticity (Theodoni et al, 2018 ;Jahnke et al, 2015 ). However, in novel environments place cells remap immediately in a seemingly random fashion (Leutgeb et al, 2005 ;Muller and Kubie, 1987 ).…”

Intrinsic dynamics of randomly clustered networks generate place fields and preplay of novel environments

“…In prior models of preplay and replay, a preexisting map of the environment is typically assumed to be contained within the recurrent connections of CA3 cells, such that cells with nearby place fields are more strongly connected (Figure 1a ). While this type of model successfully produces replay (Haga and Fukai, 2018 ;Pang and Fairhall, 2019 ), such a map would only be expected to exist in a familiar environment, after experience-dependent synaptic plasticity has had time to shape the network (Theodoni et al, 2018 ). It remains unclear how, in the absence of such a preexisting map of the environment, the hippocampus can generate both preplay and immediate replay of a novel environment.…”

“…Most replay models rely on a recurrent network structure in which a map of the environment is encoded in the recurrent connections of CA3 cells, such that cells with nearby place fields are more strongly connected. Some models assume this structure is pre-existing (Haga and Fukai, 2018 ;Pang and Fairhall, 2019 ), and some show how it could develop over time through synaptic plasticity (Theodoni et al, 2018 ;Jahnke et al, 2015 ). However, in novel environments place cells remap immediately in a seemingly random fashion (Leutgeb et al, 2005 ;Muller and Kubie, 1987 ).…”