ArticleComputational brain & behavior2025
Learning Temporal Relationships Between Symbols with Laplace Neural Manifolds.
Article in Computational brain & behavior, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Duration between rewards controls the rate of behavioral and dopaminergic learning.Nature neuroscience · 2026Article
- Reconciling time and prediction error theories of associative learning.Nature communications · 2025Article
- Covariant spatio-temporal receptive fields for spiking neural networks.Nature communications · 2025Article
- Continuous Attractor Networks for Laplace Neural Manifolds.Computational brain & behavior · 2025Article
- Ramping dynamics in the frontal cortex unfold over multiple timescales during motor planning.Journal of neurophysiology · 2025Article
- Ramping cells in the rodent medial prefrontal cortex encode time to past and future events via real Laplace transform.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
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Abstract
Firing across populations of neurons in many regions of the mammalian brain maintains a temporal memory, a neural timeline of the recent past. Behavioral results demonstrate that people can both remember the past and anticipate the future over an analogous internal timeline. This paper presents a mathematical framework for building this timeline of the future. We assume that the input to the system is a time series of symbols-sparse tokenized representations of the present-in continuous time. The goal is to record pairwise temporal relationships between symbols over a wide range of time scales. We assume that the brain has access to a temporal memory in the form of the real Laplace transform. Hebbian associations with a diversity of synaptic time scales are formed between the past timeline and the present symbol. The associative memory stores the convolution between the past and the present. Knowing the temporal relationship between the past and the present allows one to infer relationships between the present and the future. With appropriate normalization, this Hebbian associative matrix can store a Laplace successor representation and a Laplace predecessor representation from which measures of temporal contingency can be evaluated. The diversity of synaptic time constants allows for the learning of non-stationary statistics as well as joint statistics between triplets of symbols. This framework synthesizes a number of recent neuroscientific findings including results from dopamine neurons in the mesolimbic forebrain.
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