Neurodynamics of mental exploration
Open accessAbstract
Thinking allows an animal to take an effective action in a novel situation based on a mental exploration of possibilities and previous knowledge. We describe a model animal, with a neural system based loosely on the rodent hippocampus, which performs mental exploration to find a useful route in a spatial world it has previously learned. It then mentally recapitulates the chosen route, and this intent is converted to motor acts that move the animal physically along the route. The modeling is based on spiking neurons with spike-frequency adaptation. Adaptation causes the continuing evolution in the pattern of neural activity that is essential to mental exploration. A successful mental exploration is remembered through spike-timing-dependent synaptic plasticity. The system is also an episodic memory for an animal chiefly concerned with locations.
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In some simple models, targeted navigation in the hippocampus is achieved via the dynamics of bump attractors or propagating waves in a place cell network with Hebbian plasticity and adaptation.
“In some simple models, targeted navigation in the hippocampus is achieved via the dynamics of “bump attractors” or propagating waves in a place cell network with Hebbian plasticity and adaptation (Hopfield, 2009; Buzsáki and Moser, 2013; Ponulak and Hopfield, 2013)”
From Toward an integration of deep learning and neuroscience (2016) · cites this paper as Hopfield, 2009
The role of the hippocampus in imagination, concept generation, scene construction, mental exploration and goal-directed path planning suggests that it could help to create generative models to underpin more complex inference such as program induction or common-sense world simulation.
“The role of the hippocampus in imagination, concept generation (Kumaran et al., 2009), scene construction (Hassabis and Maguire, 2007), mental exploration and goal-directed path planning (Hopfield, 2009; Ólafsdóttir et al., 2015; Brown et al., 2016) suggests that it could help to create generative models to underpin more complex inference such as program induction (Lake et al., 2015) or common-s”
From Toward an integration of deep learning and neuroscience (2016) · cites this paper as Hopfield, 2009
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- Authors
- Hopfield, J. J.
- Affiliations
- Princeton University (US)
- Corresponding author
- Hopfield, John J.
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2009-12-30
- Volume
- 107
- Issue
- 4
- Pages
- 1648-1653
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC2824418
- OpenAlex
- W1971145390
- Cited by
- 96
- References
- 24
- Citations by year
- 2026: 3 · 2025: 12 · 2024: 11 · 2023: 10 · 2022: 5 · 2021: 3 · 2020: 1 · 2019: 4 · 2018: 6 · 2017: 9 · 2016: 8 · 2015: 3 · 2014: 8 · 2013: 4 · 2012: 2
- FWCI
- 1.45
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Physical Sciences › Engineering › Automotive Engineering › Spatial Cognition and Navigation
- Keywords
- hippocampus, spiking neurons, spike frequency adaptation, spike-timing-dependent plasticity, episodic memory, spatial navigation, neural models, memory-guided behavior
- MeSH terms
- Animals, Behavior, Animal, Hippocampus, Learning, Models, Neurological, Neuronal Plasticity
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