ArticleeLife2021
Interrogating theoretical models of neural computation with emergent property inference.
Article in eLife, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- WhenAPL bioengineering · 2025Review
- Cell-mechanical parameter estimation from 1D cell trajectories using simulation-based inference.PloS one · 2025Article
- Automated customization of large-scale spiking network models to neuronal population activity.Nature computational science · 2024Article
- Constructing neural networks with pre-specified dynamics.Scientific reports · 2024Article
- Interactions between circuit architecture and plasticity in a closed-loop cerebellar system.eLife · 2024Article
- The Structure of Hippocampal CA1 Interactions Optimizes Spatial Coding across Experience.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- A unifying perspective on neural manifolds and circuits for cognition.Nature reviews. Neuroscience · 2023Review
- Connecting Connectomes to Physiology.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Review
- Neural learning rules for generating flexible predictions and computing the successor representation.eLife · 2023Article
- Neuroscience Cloud Analysis As a Service: An open-source platform for scalable, reproducible data analysis.Neuron · 2022Article
- Bringing Anatomical Information into Neuronal Network Models.Advances in experimental medicine and biology · 2022Article
- Multimodal parameter spaces of a complex multi-channel neuron model.Frontiers in systems neuroscience · 2022Article
- A small, computationally flexible network produces the phenotypic diversity of song recognition in crickets.eLife · 2021Article
- Collicular circuits for flexible sensorimotor routing.Nature neuroscience · 2021Article
- Theoretical principles for illuminating sensorimotor processing with brain-wide neuronal recordings.Current opinion in neurobiology · 2020Review
- Bayesian inference for biophysical neuron models enables stimulus optimization for retinal neuroprosthetics.eLife · 2020Article
- Training deep neural density estimators to identify mechanistic models of neural dynamics.eLife · 2020Article
- Maximum Entropy Framework for Predictive Inference of Cell Population Heterogeneity and Responses in Signaling Networks.Cell systems · 2020Article
- Geometric framework to predict structure from function in neural networks.Physical review researchArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
A cornerstone of theoretical neuroscience is the circuit model: a system of equations that captures a hypothesized neural mechanism. Such models are valuable when they give rise to an experimentally observed phenomenon -- whether behavioral or a pattern of neural activity -- and thus can offer insights into neural computation. The operation of these circuits, like all models, critically depends on the choice of model parameters. A key step is then to identify the model parameters consistent with observed phenomena: to solve the inverse problem. In this work, we present a novel technique, emergent property inference (EPI), that brings the modern probabilistic modeling toolkit to theoretical neuroscience. When theorizing circuit models, theoreticians predominantly focus on reproducing computational properties rather than a particular dataset. Our method uses deep neural networks to learn parameter distributions with these computational properties. This methodology is introduced through a motivational example of parameter inference in the stomatogastric ganglion. EPI is then shown to allow precise control over the behavior of inferred parameters and to scale in parameter dimension better than alternative techniques. In the remainder of this work, we present novel theoretical findings in models of primary visual cortex and superior colliculus, which were gained through the examination of complex parametric structure captured by EPI. Beyond its scientific contribution, this work illustrates the variety of analyses possible once deep learning is harnessed towards solving theoretical inverse problems.
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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.