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Predictive Place-Cell Sequences for Goal-Finding Emerge from Goal Memory and the Cognitive Map: A Computational Model
Lorenz Gönner1, Julien Vitay1, Fred H Hamker1,2
1Artificial Intelligence, Department of Computer Science, Technische Universität Chemnitz, Chemnitz, Germany.
Frontiers in Computational Neuroscience
|October 28, 2017
Summary
This study models how the hippocampus generates sequences for planning. The model explains goal-directed planning and sequence generation, offering testable predictions for future research.
Area of Science:
- Neuroscience
- Computational Neuroscience
Background:
- Hippocampal place-cell sequences during immobility are linked to memory, but also to planning.
- The mechanisms generating these sequences and their role in planning remain unclear.
- Specifically, how sequences are biased towards goals adaptively is unknown.
Purpose of the Study:
- To propose and validate a computational model for spatial learning and hippocampal sequence generation.
- To investigate the mechanisms underlying goal-biased sequential activity for planning.
- To explore the functional role of these sequences in flexible navigation.
Main Methods:
- Developed a map-based computational model integrating cortical inputs, synaptic plasticity, and neuromodulation.
- Simulated hippocampal place-cell activity using continuous attractor network dynamics.
- Applied Bayesian decoding to simulated spike trains for comparison with experimental data.
Main Results:
- The model generates novel spatial sequences without self-motion cues.
- It successfully replicates the observed bias of place-cell sequences towards goal locations.
- Demonstrated the utility of these sequences for adaptive route planning.
Conclusions:
- Spatial learning and sequence generation are interdependent processes in the hippocampus.
- The model provides a mechanistic explanation for goal-directed planning via hippocampal sequences.
- The findings offer testable predictions for future experimental validation.
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