ArticlePLoS computational biology2018
Robust spatial memory maps encoded by networks with transient connections.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Spaces and sequences in the hippocampus: a homological perspective.Journal of computational neuroscience · 2026Article
- Topological data analysis of the firings of a network of stochastic spiking neurons.Frontiers in neural circuits · 2023Article
- Spatial representability of neuronal activity.Scientific reports · 2021Article
- Dynamical self-organization and efficient representation of space by grid cells.Current opinion in neurobiology · 2021Review
- Evaluating State Space Discovery by Persistent Cohomology in the Spatial Representation System.Frontiers in computational neuroscience · 2021Article
- From Topological Analyses to Functional Modeling: The Case of Hippocampus.Frontiers in computational neuroscience · 2020Article
- Replays of spatial memories suppress topological fluctuations in cognitive map.Network neuroscience (Cambridge, Mass.) · 2019Article
- The importance of forgetting: Limiting memory improves recovery of topological characteristics from neural data.PloS one · 2018Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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Abstract
The spiking activity of principal cells in mammalian hippocampus encodes an internalized neuronal representation of the ambient space-a cognitive map. Once learned, such a map enables the animal to navigate a given environment for a long period. However, the neuronal substrate that produces this map is transient: the synaptic connections in the hippocampus and in the downstream neuronal networks never cease to form and to deteriorate at a rapid rate. How can the brain maintain a robust, reliable representation of space using a network that constantly changes its architecture? We address this question using a computational framework that allows evaluating the effect produced by the decaying connections between simulated hippocampal neurons on the properties of the cognitive map. Using novel Algebraic Topology techniques, we demonstrate that emergence of stable cognitive maps produced by networks with transient architectures is a generic phenomenon. The model also points out that deterioration of the cognitive map caused by weakening or lost connections between neurons may be compensated by simulating the neuronal activity. Lastly, the model explicates the importance of the complementary learning systems for processing spatial information at different levels of spatiotemporal granularity.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.