ArticlePLoS computational biology2023
Geometry of population activity in spiking networks with low-rank structure.
Article in PLoS computational biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed.
- Representational learning by optimization of neural manifolds in an olfactory memory network.Nature neuroscience · 2026Article
- Exploring neural manifolds across a wide range of intrinsic dimensions.PLoS computational biology · 2026Article
- Design principles for cost functions in optimal sensory coding.Frontiers in computational neuroscience · 2026Review
- Topological analysis of neuronal assemblies reveals low-rank structure modulated by cholinergic activity.bioRxiv : the preprint server for biology · 2025Article
- Balanced state of networks of winner-take-all units.PLoS computational biology · 2025Article
- TiDHy: Timescale Demixing via Hypernetworks to learn simultaneous dynamics from mixed observations.bioRxiv : the preprint server for biology · 2025Article
- Linking neural population formatting to function.bioRxiv : the preprint server for biology · 2025Article
- Neural oscillation in low-rank SNNs: bridging network dynamics and cognitive function.Frontiers in computational neuroscience · 2025Article
- Firing rate distributions in plastic networks of spiking neurons.Network neuroscience (Cambridge, Mass.) · 2025Article
- Topological features of spike trains in recurrent spiking neural networks that are trained to generate spatiotemporal patterns.Frontiers in computational neuroscience · 2024Article
- Relating local connectivity and global dynamics in recurrent excitatory-inhibitory networks.PLoS computational biology · 2023Article
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3 authors.
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Abstract
Recurrent network models are instrumental in investigating how behaviorally-relevant computations emerge from collective neural dynamics. A recently developed class of models based on low-rank connectivity provides an analytically tractable framework for understanding of how connectivity structure determines the geometry of low-dimensional dynamics and the ensuing computations. Such models however lack some fundamental biological constraints, and in particular represent individual neurons in terms of abstract units that communicate through continuous firing rates rather than discrete action potentials. Here we examine how far the theoretical insights obtained from low-rank rate networks transfer to more biologically plausible networks of spiking neurons. Adding a low-rank structure on top of random excitatory-inhibitory connectivity, we systematically compare the geometry of activity in networks of integrate-and-fire neurons to rate networks with statistically equivalent low-rank connectivity. We show that the mean-field predictions of rate networks allow us to identify low-dimensional dynamics at constant population-average activity in spiking networks, as well as novel non-linear regimes of activity such as out-of-phase oscillations and slow manifolds. We finally exploit these results to directly build spiking networks that perform nonlinear computations.
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