ArticleHippocampus2015
A simple biophysically plausible model for long time constants in single neurons.
Article in Hippocampus, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed, 61 citations in OpenAlex.
- Article
- Intrinsic persistent firing in CA1 encodes elapsed time across behaviorally relevant scales.bioRxiv : the preprint server for biology · 2025Article
- Continuous Attractor Networks for Laplace Neural Manifolds.Computational brain & behavior · 2025Article
- Self-supervised learning of scale-invariant neural representations of space and time.Journal of computational neuroscience · 2025Article
- The influence of emotion on temporal context models.Cognition & emotion · 2025Review
- 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
- Modeling Time Cell Neuron-Level Dynamics.Computational brain & behavior · 2024Article
- Neurocomputational Models of Interval Timing: Seeing the Forest for the Trees.Advances in experimental medicine and biology · 2024Review
- Article
- Episodic boundary cells in human medial temporal lobe during the free recall task.Hippocampus · 2022Article
- Article
- Potential and efficiency of statistical learning closely intertwined with individuals' executive functions: a mathematical modeling study.Scientific reports · 2020Article
- 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
- Navigating Through Time: A Spatial Navigation Perspective on How the Brain May Encode Time.Annual review of neuroscience · 2020Review
- The Role of Hierarchical Dynamical Functions in Coding for Episodic Memory and Cognition.Journal of cognitive neuroscience · 2019Article
- Estimating Scale-Invariant Future in Continuous Time.Neural computation · 2019Article
- A neural microcircuit model for a scalable scale-invariant representation of time.Hippocampus · 2019Article
- Evidence accumulation in a Laplace domain decision space.Computational brain & behavior · 2018Article
- Is working memory stored along a logarithmic timeline? Converging evidence from neuroscience, behavior and models.Neurobiology of learning and memory · 2018Review
- Compressed Timeline of Recent Experience in Monkey Lateral Prefrontal Cortex.Journal of cognitive neuroscience · 2018Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Recent work in computational neuroscience and cognitive psychology suggests that a set of cells that decay exponentially could be used to support memory for the time at which events took place. Analytically and through simulations on a biophysical model of an individual neuron, we demonstrate that exponentially decaying firing with a range of time constants up to minutes could be implemented using a simple combination of well-known neural mechanisms. In particular, we consider firing supported by calcium-controlled cation current. When the amount of calcium leaving the cell during an interspike interval is larger than the calcium influx during a spike, the overall decay in calcium concentration can be exponential, resulting in exponential decay of the firing rate. The time constant of the decay can be several orders of magnitude larger than the time constant of calcium clearance, and it could be controlled externally via a variety of biologically plausible ways. The ability to flexibly and rapidly control time constants could enable working memory of temporal history to be generalized to other variables in computing spatial and ordinal representations.
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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.