Journal Article FZJ-2023-03213

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NMDA-driven dendritic modulation enables multitask representation learning in hierarchical sensory processing pathways

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2023
National Acad. of Sciences Washington, DC

Proceedings of the National Academy of Sciences of the United States of America 120(32), e2300558120 () [10.1073/pnas.2300558120]

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Abstract: While sensory representations in the brain depend on context, it remains unclearhow such modulations are implemented at the biophysical level, and how processinglayers further in the hierarchy can extract useful features for each possible contex-tual state. Here, we demonstrate that dendritic N-Methyl-D-Aspartate spikes can,within physiological constraints, implement contextual modulation of feedforwardprocessing. Such neuron-specific modulations exploit prior knowledge, encoded instable feedforward weights, to achieve transfer learning across contexts. In a network ofbiophysically realistic neuron models with context-independent feedforward weights,we show that modulatory inputs to dendritic branches can solve linearly nonseparablelearning problems with a Hebbian, error-modulated learning rule. We also demonstratethat local prediction of whether representations originate either from different inputs,or from different contextual modulations of the same input, results in representationlearning of hierarchical feedforward weights across processing layers that accommodatea multitude of contexts.

Classification:

Contributing Institute(s):
  1. Computational and Systems Neuroscience (INM-6)
  2. Computational and Systems Neuroscience (IAS-6)
  3. Jara-Institut Brain structure-function relationships (INM-10)
  4. Neuromorphic Software Eco System (PGI-15)
Research Program(s):
  1. 5232 - Computational Principles (POF4-523) (POF4-523)
  2. HBP SGA1 - Human Brain Project Specific Grant Agreement 1 (720270) (720270)
  3. HBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907) (785907)
  4. HBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539) (945539)
  5. SDS005 - Towards an integrated data science of complex natural systems (PF-JARA-SDS005) (PF-JARA-SDS005)
  6. neuroIC002 - Recurrence and stochasticity for neuro-inspired computation (EXS-SF-neuroIC002) (EXS-SF-neuroIC002)
  7. Functional Neural Architectures (jinm60_20190501) (jinm60_20190501)

Appears in the scientific report 2023
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Medline ; Creative Commons Attribution CC BY 4.0 ; OpenAccess ; BIOSIS Previews ; Biological Abstracts ; Clarivate Analytics Master Journal List ; Current Contents - Agriculture, Biology and Environmental Sciences ; Current Contents - Life Sciences ; Ebsco Academic Search ; Essential Science Indicators ; IF >= 10 ; JCR ; National-Konsortium ; PubMed Central ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection ; Zoological Record
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Dokumenttypen > Aufsätze > Zeitschriftenaufsätze
Institutssammlungen > INM > INM-10
Institutssammlungen > IAS > IAS-6
Institutssammlungen > INM > INM-6
Institutssammlungen > PGI > PGI-15
Workflowsammlungen > Öffentliche Einträge
Workflowsammlungen > Publikationsgebühren
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Open Access

 Datensatz erzeugt am 2023-08-28, letzte Änderung am 2025-01-23


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