ArticleNeural computation2019
Estimating Scale-Invariant Future in Continuous Time.
Article in Neural computation, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed.
- Time-Local Transformer.Computational brain & behavior · 2026Article
- Intrinsic persistent firing in CA1 encodes elapsed time across behaviorally relevant scales.bioRxiv : the preprint server for biology · 2025Article
- Reconciling time and prediction error theories of associative learning.Nature communications · 2025Article
- Article
- Learning Temporal Relationships Between Symbols with Laplace Neural Manifolds.Computational brain & behavior · 2025Article
- Event boundaries drive norepinephrine release and distinctive neural representations of space in the rodent hippocampus.bioRxiv : the preprint server for biology · 2024Article
- Neural temporal context reinstatement of event structure during memory recall.Journal of experimental psychology. General · 2023Article
- Scanning a compressed ordered representation of the future.Journal of experimental psychology. General · 2022Article
- Predicting the Future With a Scale-Invariant Temporal Memory for the Past.Neural computation · 2022Article
- Time Is of the Essence: Neural Codes, Synchronies, Oscillations, Architectures.Frontiers in computational neuroscience · 2022Article
- Neural circuits and symbolic processing.Neurobiology of learning and memory · 2021Review
- A temporal record of the past with a spectrum of time constants in the monkey entorhinal cortex.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- Time-conjunctive representations of future events.Memory & cognition · 2020Article
- Bayesian nonparametric models characterize instantaneous strategies in a competitive dynamic game.Nature communications · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Natural learners must compute an estimate of future outcomes that follow from a stimulus in continuous time. Widely used reinforcement learning algorithms discretize continuous time and estimate either transition functions from one step to the next (model-based algorithms) or a scalar value of exponentially discounted future reward using the Bellman equation (model-free algorithms). An important drawback of model-based algorithms is that computational cost grows linearly with the amount of time to be simulated. An important drawback of model-free algorithms is the need to select a timescale required for exponential discounting. We present a computational mechanism, developed based on work in psychology and neuroscience, for computing a scale-invariant timeline of future outcomes. This mechanism efficiently computes an estimate of inputs as a function of future time on a logarithmically compressed scale and can be used to generate a scale-invariant power-law-discounted estimate of expected future reward. The representation of future time retains information about what will happen when. The entire timeline can be constructed in a single parallel operation that generates concrete behavioral and neural predictions. This computational mechanism could be incorporated into future reinforcement learning algorithms.
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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.