ArticlePLoS computational biology2021
Efficient and robust coding in heterogeneous recurrent networks.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Article
- Spiking neurons as predictive controllers of linear systems.PLoS computational biology · 2026Article
- Design principles for cost functions in optimal sensory coding.Frontiers in computational neuroscience · 2026Review
- Diverse and flexible behavioral strategies arise in recurrent neural networks trained on multisensory decision making.PLoS computational biology · 2025Article
- Heterogeneous synaptic homeostasis: A novel mechanism boosting information propagation in the cortex.PLoS computational biology · 2025Article
- Neural heterogeneity enhances reliable neural information processing: Local sensitivity and globally input-slaved transient dynamics.Science advances · 2025Article
- Article
- The functional role of oscillatory dynamics in neocortical circuits: A computational perspective.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Efficient coding in biophysically realistic excitatory-inhibitory spiking networks.bioRxiv : the preprint server for biology · 2025Article
- Biologically inspired heterogeneous learning for accurate, efficient and low-latency neural network.National science review · 2025Article
- The impact of spike timing precision and spike emission reliability on decoding accuracy.Scientific reports · 2024Article
- The tuning of tuning: How adaptation influences single cell information transfer.PLoS computational biology · 2024Article
- Neural populations within macaque early vestibular pathways are adapted to encode natural self-motion.PLoS biology · 2024Article
- Neural heterogeneity controls computations in spiking neural networks.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Brain-inspired modular echo state network for EEG-based emotion recognition.Frontiers in neuroscience · 2024Article
- Adult-born granule cells improve stimulus encoding and discrimination in the dentate gyrus.eLife · 2023Article
- Descending pathways increase sensory neural response heterogeneity to facilitate decoding and behavior.iScience · 2023Article
- Intrinsic neural diversity quenches the dynamic volatility of neural networks.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Neural learning rules for generating flexible predictions and computing the successor representation.eLife · 2023Article
- Coding of object location by heterogeneous neural populations with spatially dependent correlations in weakly electric fish.PLoS computational biology · 2023Article
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3 authors.
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Abstract
Cortical networks show a large heterogeneity of neuronal properties. However, traditional coding models have focused on homogeneous populations of excitatory and inhibitory neurons. Here, we analytically derive a class of recurrent networks of spiking neurons that close to optimally track a continuously varying input online, based on two assumptions: 1) every spike is decoded linearly and 2) the network aims to reduce the mean-squared error between the input and the estimate. From this we derive a class of predictive coding networks, that unifies encoding and decoding and in which we can investigate the difference between homogeneous networks and heterogeneous networks, in which each neurons represents different features and has different spike-generating properties. We find that in this framework, 'type 1' and 'type 2' neurons arise naturally and networks consisting of a heterogeneous population of different neuron types are both more efficient and more robust against correlated noise. We make two experimental predictions: 1) we predict that integrators show strong correlations with other integrators and resonators are correlated with resonators, whereas the correlations are much weaker between neurons with different coding properties and 2) that 'type 2' neurons are more coherent with the overall network activity than 'type 1' neurons.
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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.