Geometry and dynamics of representations in a precisely balanced memory network related to olfactory cortex
Biological memory networks are thought to store information by experience-dependent changes in the synaptic connectivity between assemblies of neurons. Recent models suggest that these assemblies contain both excitatory and inhibitory neurons (E/I assemblies), resulting in co-tuning and precise bala...
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| Format: | Article |
| Language: | English |
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eLife Sciences Publications Ltd
2025-01-01
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| Series: | eLife |
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| Online Access: | https://elifesciences.org/articles/96303 |
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| author | Claire Meissner-Bernard Friedemann Zenke Rainer W Friedrich |
| author_facet | Claire Meissner-Bernard Friedemann Zenke Rainer W Friedrich |
| author_sort | Claire Meissner-Bernard |
| collection | DOAJ |
| description | Biological memory networks are thought to store information by experience-dependent changes in the synaptic connectivity between assemblies of neurons. Recent models suggest that these assemblies contain both excitatory and inhibitory neurons (E/I assemblies), resulting in co-tuning and precise balance of excitation and inhibition. To understand computational consequences of E/I assemblies under biologically realistic constraints we built a spiking network model based on experimental data from telencephalic area Dp of adult zebrafish, a precisely balanced recurrent network homologous to piriform cortex. We found that E/I assemblies stabilized firing rate distributions compared to networks with excitatory assemblies and global inhibition. Unlike classical memory models, networks with E/I assemblies did not show discrete attractor dynamics. Rather, responses to learned inputs were locally constrained onto manifolds that ‘focused’ activity into neuronal subspaces. The covariance structure of these manifolds supported pattern classification when information was retrieved from selected neuronal subsets. Networks with E/I assemblies therefore transformed the geometry of neuronal coding space, resulting in continuous representations that reflected both relatedness of inputs and an individual’s experience. Such continuous representations enable fast pattern classification, can support continual learning, and may provide a basis for higher-order learning and cognitive computations. |
| format | Article |
| id | doaj-art-d78a0f32cce847908e36563351aaa934 |
| institution | Kabale University |
| issn | 2050-084X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj-art-d78a0f32cce847908e36563351aaa9342025-01-13T17:51:42ZengeLife Sciences Publications LtdeLife2050-084X2025-01-011310.7554/eLife.96303Geometry and dynamics of representations in a precisely balanced memory network related to olfactory cortexClaire Meissner-Bernard0https://orcid.org/0009-0007-2038-8398Friedemann Zenke1https://orcid.org/0000-0003-1883-644XRainer W Friedrich2https://orcid.org/0000-0001-9107-0482Friedrich Miescher Institute for Biomedical Research, Basel, SwitzerlandFriedrich Miescher Institute for Biomedical Research, Basel, Switzerland; University of Basel, Basel, SwitzerlandFriedrich Miescher Institute for Biomedical Research, Basel, Switzerland; University of Basel, Basel, SwitzerlandBiological memory networks are thought to store information by experience-dependent changes in the synaptic connectivity between assemblies of neurons. Recent models suggest that these assemblies contain both excitatory and inhibitory neurons (E/I assemblies), resulting in co-tuning and precise balance of excitation and inhibition. To understand computational consequences of E/I assemblies under biologically realistic constraints we built a spiking network model based on experimental data from telencephalic area Dp of adult zebrafish, a precisely balanced recurrent network homologous to piriform cortex. We found that E/I assemblies stabilized firing rate distributions compared to networks with excitatory assemblies and global inhibition. Unlike classical memory models, networks with E/I assemblies did not show discrete attractor dynamics. Rather, responses to learned inputs were locally constrained onto manifolds that ‘focused’ activity into neuronal subspaces. The covariance structure of these manifolds supported pattern classification when information was retrieved from selected neuronal subsets. Networks with E/I assemblies therefore transformed the geometry of neuronal coding space, resulting in continuous representations that reflected both relatedness of inputs and an individual’s experience. Such continuous representations enable fast pattern classification, can support continual learning, and may provide a basis for higher-order learning and cognitive computations.https://elifesciences.org/articles/96303autoassociative memoryassemblyolfactory cortexcomputational modelzebrafishneural manifold |
| spellingShingle | Claire Meissner-Bernard Friedemann Zenke Rainer W Friedrich Geometry and dynamics of representations in a precisely balanced memory network related to olfactory cortex eLife autoassociative memory assembly olfactory cortex computational model zebrafish neural manifold |
| title | Geometry and dynamics of representations in a precisely balanced memory network related to olfactory cortex |
| title_full | Geometry and dynamics of representations in a precisely balanced memory network related to olfactory cortex |
| title_fullStr | Geometry and dynamics of representations in a precisely balanced memory network related to olfactory cortex |
| title_full_unstemmed | Geometry and dynamics of representations in a precisely balanced memory network related to olfactory cortex |
| title_short | Geometry and dynamics of representations in a precisely balanced memory network related to olfactory cortex |
| title_sort | geometry and dynamics of representations in a precisely balanced memory network related to olfactory cortex |
| topic | autoassociative memory assembly olfactory cortex computational model zebrafish neural manifold |
| url | https://elifesciences.org/articles/96303 |
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