ReviewFrontiers in neural circuits2013
Adaptive stimulus optimization for sensory systems neuroscience.
Review in Frontiers in neural circuits, 2013. 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.
- Article
- A software platform for real-time and adaptive neuroscience experiments.Nature communications · 2025Article
- Active Learning for Discrete Latent Variable Models.Neural computation · 2024Article
- Compact deep neural network models of visual cortex.bioRxiv : the preprint server for biology · 2023Article
- Driving and suppressing the human language network using large language models.bioRxiv : the preprint server for biology · 2023Article
- On the synthesis of visual illusions using deep generative models.Journal of vision · 2022Article
- Bubblewrap: Online tiling and real-time flow prediction on neural manifolds.Advances in neural information processing systems · 2021Article
- Estimating the Parameters of Fitzhugh-Nagumo Neurons from Neural Spiking Data.Brain sciences · 2019Article
- Optimizing optogenetic stimulation protocols in auditory corticofugal neurons based on closed-loop spike feedback.Journal of neural engineering · 2019Article
- Conjoint psychometric field estimation for bilateral audiometry.Behavior research methods · 2019Article
- Review
- Fitting of dynamic recurrent neural network models to sensory stimulus-response data.Journal of biological physics · 2018Article
- Dethroning the Fano Factor: A Flexible, Model-Based Approach to Partitioning Neural Variability.Neural computation · 2018Article
- Adaptive Stimulus Design for Dynamic Recurrent Neural Network Models.Frontiers in neural circuits · 2018Article
- Article
- Temporal Code-Driven Stimulation: Definition and Application to Electric Fish Signaling.Frontiers in neuroinformatics · 2016Article
- Online stimulus optimization rapidly reveals multidimensional selectivity in auditory cortical neurons.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2014Article
- Closed-loop neuroscience and neuroengineering.Frontiers in neural circuits · 2014Article
- Article
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Authors and funding
2 authors.
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Abstract
In this paper, we review several lines of recent work aimed at developing practical methods for adaptive on-line stimulus generation for sensory neurophysiology. We consider various experimental paradigms where on-line stimulus optimization is utilized, including the classical optimal stimulus paradigm where the goal of experiments is to identify a stimulus which maximizes neural responses, the iso-response paradigm which finds sets of stimuli giving rise to constant responses, and the system identification paradigm where the experimental goal is to estimate and possibly compare sensory processing models. We discuss various theoretical and practical aspects of adaptive firing rate optimization, including optimization with stimulus space constraints, firing rate adaptation, and possible network constraints on the optimal stimulus. We consider the problem of system identification, and show how accurate estimation of non-linear models can be highly dependent on the stimulus set used to probe the network. We suggest that optimizing stimuli for accurate model estimation may make it possible to successfully identify non-linear models which are otherwise intractable, and summarize several recent studies of this type. Finally, we present a two-stage stimulus design procedure which combines the dual goals of model estimation and model comparison and may be especially useful for system identification experiments where the appropriate model is unknown beforehand. We propose that fast, on-line stimulus optimization enabled by increasing computer power can make it practical to move sensory neuroscience away from a descriptive paradigm and toward a new paradigm of real-time model estimation and comparison.
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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.