Discriminating neural ensemble patterns through dendritic computations in randomly connected feedforward networks
Co-active or temporally ordered neural ensembles are a signature of salient sensory, motor, and cognitive events. Local convergence of such patterned activity as synaptic clusters on dendrites could help single neurons harness the potential of dendritic nonlinearities to decode neural activity patte...
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| Format: | Article |
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eLife Sciences Publications Ltd
2025-01-01
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| Online Access: | https://elifesciences.org/articles/100664 |
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| author | Bhanu Priya Somashekar Upinder Singh Bhalla |
| author_facet | Bhanu Priya Somashekar Upinder Singh Bhalla |
| author_sort | Bhanu Priya Somashekar |
| collection | DOAJ |
| description | Co-active or temporally ordered neural ensembles are a signature of salient sensory, motor, and cognitive events. Local convergence of such patterned activity as synaptic clusters on dendrites could help single neurons harness the potential of dendritic nonlinearities to decode neural activity patterns. We combined theory and simulations to assess the likelihood of whether projections from neural ensembles could converge onto synaptic clusters even in networks with random connectivity. Using rat hippocampal and cortical network statistics, we show that clustered convergence of axons from three to four different co-active ensembles is likely even in randomly connected networks, leading to representation of arbitrary input combinations in at least 10 target neurons in a 100,000 population. In the presence of larger ensembles, spatiotemporally ordered convergence of three to five axons from temporally ordered ensembles is also likely. These active clusters result in higher neuronal activation in the presence of strong dendritic nonlinearities and low background activity. We mathematically and computationally demonstrate a tight interplay between network connectivity, spatiotemporal scales of subcellular electrical and chemical mechanisms, dendritic nonlinearities, and uncorrelated background activity. We suggest that dendritic clustered and sequence computation is pervasive, but its expression as somatic selectivity requires confluence of physiology, background activity, and connectomics. |
| format | Article |
| id | doaj-art-11df2e3083864875ba4cee07864599e7 |
| 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-11df2e3083864875ba4cee07864599e72025-01-24T17:02:25ZengeLife Sciences Publications LtdeLife2050-084X2025-01-011310.7554/eLife.100664Discriminating neural ensemble patterns through dendritic computations in randomly connected feedforward networksBhanu Priya Somashekar0https://orcid.org/0000-0003-4873-767XUpinder Singh Bhalla1https://orcid.org/0000-0003-1722-5188National Centre for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, IndiaNational Centre for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, IndiaCo-active or temporally ordered neural ensembles are a signature of salient sensory, motor, and cognitive events. Local convergence of such patterned activity as synaptic clusters on dendrites could help single neurons harness the potential of dendritic nonlinearities to decode neural activity patterns. We combined theory and simulations to assess the likelihood of whether projections from neural ensembles could converge onto synaptic clusters even in networks with random connectivity. Using rat hippocampal and cortical network statistics, we show that clustered convergence of axons from three to four different co-active ensembles is likely even in randomly connected networks, leading to representation of arbitrary input combinations in at least 10 target neurons in a 100,000 population. In the presence of larger ensembles, spatiotemporally ordered convergence of three to five axons from temporally ordered ensembles is also likely. These active clusters result in higher neuronal activation in the presence of strong dendritic nonlinearities and low background activity. We mathematically and computationally demonstrate a tight interplay between network connectivity, spatiotemporal scales of subcellular electrical and chemical mechanisms, dendritic nonlinearities, and uncorrelated background activity. We suggest that dendritic clustered and sequence computation is pervasive, but its expression as somatic selectivity requires confluence of physiology, background activity, and connectomics.https://elifesciences.org/articles/100664synaptic clustersdendritic sequencesdendritic computationnoiseneural ensemblesdendritic nonlinearities |
| spellingShingle | Bhanu Priya Somashekar Upinder Singh Bhalla Discriminating neural ensemble patterns through dendritic computations in randomly connected feedforward networks eLife synaptic clusters dendritic sequences dendritic computation noise neural ensembles dendritic nonlinearities |
| title | Discriminating neural ensemble patterns through dendritic computations in randomly connected feedforward networks |
| title_full | Discriminating neural ensemble patterns through dendritic computations in randomly connected feedforward networks |
| title_fullStr | Discriminating neural ensemble patterns through dendritic computations in randomly connected feedforward networks |
| title_full_unstemmed | Discriminating neural ensemble patterns through dendritic computations in randomly connected feedforward networks |
| title_short | Discriminating neural ensemble patterns through dendritic computations in randomly connected feedforward networks |
| title_sort | discriminating neural ensemble patterns through dendritic computations in randomly connected feedforward networks |
| topic | synaptic clusters dendritic sequences dendritic computation noise neural ensembles dendritic nonlinearities |
| url | https://elifesciences.org/articles/100664 |
| work_keys_str_mv | AT bhanupriyasomashekar discriminatingneuralensemblepatternsthroughdendriticcomputationsinrandomlyconnectedfeedforwardnetworks AT upindersinghbhalla discriminatingneuralensemblepatternsthroughdendriticcomputationsinrandomlyconnectedfeedforwardnetworks |