ArticleFrontiers in computational neuroscience2018
Modern Machine Learning as a Benchmark for Fitting Neural Responses.
Article in Frontiers in computational neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
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- Distributed implantable microchips as wireless networks for neural sensing and stimulation.Frontiers in neuroscience · 2026Review
- A common computational and neural anomaly across mouse models of autism.Nature neuroscience · 2025Article
- Computations that sustain neural feature selectivity across processing stages.PLoS computational biology · 2025Article
- Distributed representations of temporally accumulated reward prediction errors in the mouse cortex.Science advances · 2025Article
- An asynchronous wireless network for capturing event-driven data from large populations of autonomous sensors.Nature electronics · 2024Article
- Targeted V1 comodulation supports task-adaptive sensory decisions.Nature communications · 2023Article
- Emergence of cortical network motifs for short-term memory during learning.Nature communications · 2023Article
- Coherent mapping of position and head direction across auditory and visual cortex.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- Article
- Low-dimensional encoding of decisions in parietal cortex reflects long-term training history.Nature communications · 2023Article
- Interpretable machine learning for prediction of clinical outcomes in acute ischemic stroke.Frontiers in neurology · 2023Article
- Spikebench: An open benchmark for spike train time-series classification.PLoS computational biology · 2023Article
- Multiregion neuronal activity: the forest and the trees.Nature reviews. Neuroscience · 2022Review
- The promise of a model-based psychiatry: building computational models of mental ill health.The Lancet. Digital health · 2022Review
- Coding of latent variables in sensory, parietal, and frontal cortices during closed-loop virtual navigation.eLife · 2022Article
- Look-up and look-down neurons in the mouse visual thalamus during freely moving exploration.Current biology : CB · 2022Article
- Representation learning in the artificial and biological neural networks underlying sensorimotor integration.Science advances · 2022Article
- Integrating Statistical and Machine Learning Approaches for Neural Classification.IEEE access : practical innovations, open solutions · 2022Article
- A deep convolutional visual encoding model of neuronal responses in the LGN.Brain informatics · 2021Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Neuroscience has long focused on finding encoding models that effectively ask "what predicts neural spiking?" and generalized linear models (GLMs) are a typical approach. It is often unknown how much of explainable neural activity is captured, or missed, when fitting a model. Here we compared the predictive performance of simple models to three leading machine learning methods: feedforward neural networks, gradient boosted trees (using XGBoost), and stacked ensembles that combine the predictions of several methods. We predicted spike counts in macaque motor (M1) and somatosensory (S1) cortices from standard representations of reaching kinematics, and in rat hippocampal cells from open field location and orientation. Of these methods, XGBoost and the ensemble consistently produced more accurate spike rate predictions and were less sensitive to the preprocessing of features. These methods can thus be applied quickly to detect if feature sets relate to neural activity in a manner not captured by simpler methods. Encoding models built with a machine learning approach accurately predict spike rates and can offer meaningful benchmarks for simpler models.
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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.