ArticleJournal of computational neuroscience2010
Pattern orthogonalization via channel decorrelation by adaptive networks.
Article in Journal of computational neuroscience, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- How the olfactory bulb maintains stable odor manifolds amid adaptation and representational drift.bioRxiv : the preprint server for biology · 2026Article
- Article
- Normative and mechanistic model of an adaptive circuit for efficient encoding and feature extraction.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Structural spine plasticity: Learning and forgetting of odor-specific subnetworks in the olfactory bulb.PLoS computational biology · 2022Article
- A Model of Pattern Separation by Single Neurons.Frontiers in computational neuroscience · 2022Article
- Local Homeostatic Regulation of the Spectral Radius of Echo-State Networks.Frontiers in computational neuroscience · 2021Article
- Impact of Perineuronal Nets on Electrophysiology of Parvalbumin Interneurons, Principal Neurons, and Brain Oscillations: A Review.Frontiers in synaptic neuroscience · 2021Article
- Effect of Interglomerular Inhibitory Networks on Olfactory Bulb Odor Representations.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2020Article
- Quantitative properties of a feedback circuit predict frequency-dependent pattern separation.eLife · 2020Article
- Pattern separation of spiketrains in hippocampal neurons.Scientific reports · 2019Article
- Top-down inputs drive neuronal network rewiring and context-enhanced sensory processing in olfaction.PLoS computational biology · 2019Article
- Persistent Structural Plasticity Optimizes Sensory Information Processing in the Olfactory Bulb.Neuron · 2016Article
- Population imaging at subcellular resolution supports specific and local inhibition by granule cells in the olfactory bulb.Scientific reports · 2016Article
- A neuromorphic network for generic multivariate data classification.Proceedings of the National Academy of Sciences of the United States of America · 2014Article
- Mitral cell spike synchrony modulated by dendrodendritic synapse location.Frontiers in computational neuroscience · 2012Article
- Neurogenesis drives stimulus decorrelation in a model of the olfactory bulb.PLoS computational biology · 2012Article
- On-Center/Inhibitory-Surround Decorrelation via Intraglomerular Inhibition in the Olfactory Bulb Glomerular Layer.Frontiers in integrative neuroscience · 2012Article
- Article
- Parallel representation of stimulus identity and intensity in a dual pathway model inspired by the olfactory system of the honeybee.Frontiers in neuroengineering · 2011Article
- Early transformations in odor representation.Trends in neurosciences · 2010Review
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Authors and funding
4 authors.
Funding
Abstract
The early processing of sensory information by neuronal circuits often includes a reshaping of activity patterns that may facilitate further processing in the brain. For instance, in the olfactory system the activity patterns that related odors evoke at the input of the olfactory bulb can be highly similar. Nevertheless, the corresponding activity patterns of the mitral cells, which represent the output of the olfactory bulb, can differ significantly from each other due to strong inhibition by granule cells and peri-glomerular cells. Motivated by these results we study simple adaptive inhibitory networks that aim to separate or even orthogonalize activity patterns representing similar stimuli. Since the animal experiences the different stimuli at different times it is difficult for the network to learn the connectivity based on their similarity; biologically it is more plausible that learning is driven by simultaneous correlations between the input channels. We investigate the connection between pattern orthogonalization and channel decorrelation and demonstrate that networks can achieve effective pattern orthogonalization through channel decorrelation if they simultaneously equalize their output levels. In feedforward networks biophysically plausible learning mechanisms fail, however, for even moderately similar input patterns. Recurrent networks do not have that limitation; they can orthogonalize the representations of highly similar input patterns. Even when they are optimized for linear neuronal dynamics they perform very well when the dynamics are nonlinear. These results provide insights into fundamental features of simplified inhibitory networks that may be relevant for pattern orthogonalization by neuronal circuits in general.
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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.