ArticleThe Journal of neuroscience : the official journal of the Society for Neuroscience2013
Optimizing working memory with heterogeneity of recurrent cortical excitation.
Article in The Journal of neuroscience : the official journal of the Society for Neuroscience, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed.
- Homeostatic Plasticity Enables Stable yet Tunable Neuronal Assemblies.bioRxiv : the preprint server for biology · 2026Article
- Neural circuit models for evidence accumulation through choice-selective sequences.Nature communications · 2026Article
- The effects of the post-delay epochs on working memory error reduction.PLoS computational biology · 2025Article
- Neural heterogeneity enhances reliable neural information processing: Local sensitivity and globally input-slaved transient dynamics.Science advances · 2025Article
- Decoding auditory working memory content from EEG aftereffects of auditory-cortical TMS.bioRxiv : the preprint server for biology · 2025Article
- Biologically inspired heterogeneous learning for accurate, efficient and low-latency neural network.National science review · 2025Article
- Robustly encoding certainty in a metastable neural circuit model.Physical review. E · 2024Article
- Linking Cognitive Integrity to Working Memory Dynamics in the Aging Human Brain.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- Cholinergic Neuromodulation of Prefrontal Attractor Dynamics Controls Performance in Spatial Working Memory.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- Adenosinergic Modulation of Layer 6 Microcircuitry in the Medial Prefrontal Cortex Is Specific to Presynaptic Cell Type.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- Cholinergic neuromodulation of prefrontal attractor dynamics controls performance in spatial working memory.bioRxiv : the preprint server for biology · 2024Article
- Neural heterogeneity controls computations in spiking neural networks.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Decentralized Neural Circuits of Multisensory Information Integration in the Brain.Advances in experimental medicine and biology · 2024Article
- Learning efficient representations of environmental priors in working memory.PLoS computational biology · 2023Article
- Descending pathways increase sensory neural response heterogeneity to facilitate decoding and behavior.iScience · 2023Article
- A unifying perspective on neural manifolds and circuits for cognition.Nature reviews. Neuroscience · 2023Review
- Attractor dynamics with activity-dependent plasticity capture human working memory across time scales.Communications psychology · 2023Article
- Learning accurate path integration in ring attractor models of the head direction system.eLife · 2022Article
- Fixational drift is driven by diffusive dynamics in central neural circuitry.Nature communications · 2022Article
- Strategy-dependent effects of working-memory limitations on human perceptual decision-making.eLife · 2022Article
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
A neural correlate of parametric working memory is a stimulus-specific rise in neuron firing rate that persists long after the stimulus is removed. Network models with local excitation and broad inhibition support persistent neural activity, linking network architecture and parametric working memory. Cortical neurons receive noisy input fluctuations that cause persistent activity to diffusively wander about the network, degrading memory over time. We explore how cortical architecture that supports parametric working memory affects the diffusion of persistent neural activity. Studying both a spiking network and a simplified potential well model, we show that spatially heterogeneous excitatory coupling stabilizes a discrete number of persistent states, reducing the diffusion of persistent activity over the network. However, heterogeneous coupling also coarse-grains the stimulus representation space, limiting the storage capacity of parametric working memory. The storage errors due to coarse-graining and diffusion trade off so that information transfer between the initial and recalled stimulus is optimized at a fixed network heterogeneity. For sufficiently long delay times, the optimal number of attractors is less than the number of possible stimuli, suggesting that memory networks can under-represent stimulus space to optimize performance. Our results clearly demonstrate the combined effects of network architecture and stochastic fluctuations on parametric memory storage.
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