ArticlePLoS computational biology2018
Brain-state invariant thalamo-cortical coordination revealed by non-linear encoders.
Article in PLoS computational biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Anti-neuraminidase and anti-HA stalk antibodies reduce the susceptibility to and infectivity of influenza A/H3N2 virus.Nature communications · 2025Article
- Continuous remote monitoring of neurophysiologic Immersion accurately predicts mood.Frontiers in digital health · 2024Article
- Anatomically distributed neural representations of instincts in the hypothalamus.bioRxiv : the preprint server for biology · 2023Article
- Precise coupling of the thalamic head-direction system to hippocampal ripples.Nature communications · 2020Article
- Machine-learning classification of neurocognitive performance in children with perinatal HIV initiating de novo antiretroviral therapy.AIDS (London, England) · 2020Article
- The roles of supervised machine learning in systems neuroscience.Progress in neurobiology · 2019Review
- A Comparison of Neural Decoding Methods and Population Coding Across Thalamo-Cortical Head Direction Cells.Frontiers in neural circuits · 2019Article
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Authors and funding
3 authors.
Funding
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Abstract
Understanding how neurons cooperate to integrate sensory inputs and guide behavior is a fundamental problem in neuroscience. A large body of methods have been developed to study neuronal firing at the single cell and population levels, generally seeking interpretability as well as predictivity. However, these methods are usually confronted with the lack of ground-truth necessary to validate the approach. Here, using neuronal data from the head-direction (HD) system, we present evidence demonstrating how gradient boosted trees, a non-linear and supervised Machine Learning tool, can learn the relationship between behavioral parameters and neuronal responses with high accuracy by optimizing the information rate. Interestingly, and unlike other classes of Machine Learning methods, the intrinsic structure of the trees can be interpreted in relation to behavior (e.g. to recover the tuning curves) or to study how neurons cooperate with their peers in the network. We show how the method, unlike linear analysis, reveals that the coordination in thalamo-cortical circuits is qualitatively the same during wakefulness and sleep, indicating a brain-state independent feed-forward circuit. Machine Learning tools thus open new avenues for benchmarking model-based characterization of spike trains.
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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.