publication . Article . Other literature type . 2016

How attention can create synaptic tags for the learning of working memories in sequential tasks.

Rombouts, Jaldert O.; Bohte, Sander M.; Roelfsema, Pieter R.;
Open Access
  • Published: 09 Dec 2016 Journal: PLOS Computational Biology, volume 11, page e1004060 (eissn: 1553-7358, Copyright policy)
  • Publisher: Public Library of Science (PLoS)
Abstract
Author Summary Working memory is a cornerstone of intelligence. Most, if not all, tasks that one can imagine require some form of working memory. The optimal solution of a working memory task depends on information that was presented in the past, for example choosing the right direction at an intersection based on a road-sign some hundreds of meters before. Interestingly, animals like monkeys readily learn difficult working memory tasks, just by receiving rewards such as fruit juice when they perform the desired behavior. Neurons in association areas in the brain play an important role in this process; these areas integrate perceptual and memory information to s...
Subjects
free text keywords: Neuroscience, Deep learning, Associative cortex, Python, Ecology, Modelling and Simulation, Computational Theory and Mathematics, Genetics, Ecology, Evolution, Behavior and Systematics, Molecular Biology, Cellular and Molecular Neuroscience, Artificial intelligence, business.industry, business, Artificial neural network, Cognitive science, Stimulus (physiology), Reinforcement learning, Bioinformatics, Learning rule, Neuronal tuning, Working memory, Biology, Perception, media_common.quotation_subject, media_common, Probabilistic logic, Biology (General), QH301-705.5, Research Article, [ INFO.INFO-NE ] Computer Science [cs]/Neural and Evolutionary Computing [cs.NE]
Funded by
NWO| Teaching and Learning in Multi Agent Systems
Project
  • Funder: Netherlands Organisation for Scientific Research (NWO) (NWO)
  • Project Code: 2300154681
,
EC| ABC
Project
ABC
Adaptive Brain Computations
  • Funder: European Commission (EC)
  • Project Code: 290011
  • Funding stream: FP7 | SP3 | PEOPLE
,
NWO| How do neurons in the visual cortex code motivation? A multidisciplinary approach: from mice to humans
Project
  • Funder: Netherlands Organisation for Scientific Research (NWO) (NWO)
  • Project Code: 2300158483
,
EC| BRAINSCALES
Project
BRAINSCALES
Brain-inspired multiscale computation in neuromorphic hybrid systems
  • Funder: European Commission (EC)
  • Project Code: 269921
  • Funding stream: FP7 | SP1 | ICT
,
EC| CORTIC_AL_GORITHMS
Project
CORTIC_AL_GORITHMS
Cortical algorithms for perceptual grouping
  • Funder: European Commission (EC)
  • Project Code: 339490
  • Funding stream: FP7 | SP2 | ERC
Communities
FET FP7FET Proactive: FET proactive 8: Brain Inspired ICT
FET FP7FET Proactive: Brain-inspired multiscale computation in neuromorphic hybrid systems

[1] J. Rombouts. Implementation of simple AuGMEnT network example. url: https://github. com/JRombouts/augment.

[2] J. Rombouts, S. Bohte, and P. Roelfsema. “How Attention Can Create Synaptic Tags for the Learning of Working Memories in Sequential Tasks”. In: PLoS Computational Biology 11.3 (2015), e1004060. doi: 10.1371/journal.pcbi.1004060.

[3] J. Rombouts, P. Roelfsema, and S. Bohte. “Learning Resets of Neural Working Memory”. In: 22nd European Symposium on Artificial Neural Networks, Computational Intelligence And Machine Learning. 2014. url: http://www.i6doc.com/en/livre/?GCOI=28001100432440. [OpenAIRE]

[4] J. Rombouts, P. Roelfsema, and S. Bohte. “Neurally Plausible Reinforcement Learning of Working Memory Tasks”. In: Advances in Neural Information Processing Systems 25. 2012. url: http : / / papers . nips . cc / paper / 4813 - neurally - plausible - reinforcement - learning - of - working-memory-tasks. [OpenAIRE]

[5] J. Vitay, H. Dinklebach, and F. Hamker. “ANNarchy: a code generation approach to neural simulations on parallel hardware”. In: Frontiers Neuroinformatics 9.19 (2015). doi: 10.3389/ fninf.2015.00019. [OpenAIRE]

[6] T. Yang and M. Shalden. “Probabilistic reasoning by neurons”. In: Nature 447.7148 (2007), pp. 1075-80. doi: 10.1038/nature05852.

[7] D. Zambrano, P. Roelfsema, and S. Bohte. “Continuous-time on-policy neural Reinforcement Learning of working memory tasks”. In: International Joint Conference on Neural Networks (2015). doi: 10.1109/IJCNN.2015.7280636. [OpenAIRE]

Abstract
Author Summary Working memory is a cornerstone of intelligence. Most, if not all, tasks that one can imagine require some form of working memory. The optimal solution of a working memory task depends on information that was presented in the past, for example choosing the right direction at an intersection based on a road-sign some hundreds of meters before. Interestingly, animals like monkeys readily learn difficult working memory tasks, just by receiving rewards such as fruit juice when they perform the desired behavior. Neurons in association areas in the brain play an important role in this process; these areas integrate perceptual and memory information to s...
Subjects
free text keywords: Neuroscience, Deep learning, Associative cortex, Python, Ecology, Modelling and Simulation, Computational Theory and Mathematics, Genetics, Ecology, Evolution, Behavior and Systematics, Molecular Biology, Cellular and Molecular Neuroscience, Artificial intelligence, business.industry, business, Artificial neural network, Cognitive science, Stimulus (physiology), Reinforcement learning, Bioinformatics, Learning rule, Neuronal tuning, Working memory, Biology, Perception, media_common.quotation_subject, media_common, Probabilistic logic, Biology (General), QH301-705.5, Research Article, [ INFO.INFO-NE ] Computer Science [cs]/Neural and Evolutionary Computing [cs.NE]
Funded by
NWO| Teaching and Learning in Multi Agent Systems
Project
  • Funder: Netherlands Organisation for Scientific Research (NWO) (NWO)
  • Project Code: 2300154681
,
EC| ABC
Project
ABC
Adaptive Brain Computations
  • Funder: European Commission (EC)
  • Project Code: 290011
  • Funding stream: FP7 | SP3 | PEOPLE
,
NWO| How do neurons in the visual cortex code motivation? A multidisciplinary approach: from mice to humans
Project
  • Funder: Netherlands Organisation for Scientific Research (NWO) (NWO)
  • Project Code: 2300158483
,
EC| BRAINSCALES
Project
BRAINSCALES
Brain-inspired multiscale computation in neuromorphic hybrid systems
  • Funder: European Commission (EC)
  • Project Code: 269921
  • Funding stream: FP7 | SP1 | ICT
,
EC| CORTIC_AL_GORITHMS
Project
CORTIC_AL_GORITHMS
Cortical algorithms for perceptual grouping
  • Funder: European Commission (EC)
  • Project Code: 339490
  • Funding stream: FP7 | SP2 | ERC
Communities
FET FP7FET Proactive: FET proactive 8: Brain Inspired ICT
FET FP7FET Proactive: Brain-inspired multiscale computation in neuromorphic hybrid systems

[1] J. Rombouts. Implementation of simple AuGMEnT network example. url: https://github. com/JRombouts/augment.

[2] J. Rombouts, S. Bohte, and P. Roelfsema. “How Attention Can Create Synaptic Tags for the Learning of Working Memories in Sequential Tasks”. In: PLoS Computational Biology 11.3 (2015), e1004060. doi: 10.1371/journal.pcbi.1004060.

[3] J. Rombouts, P. Roelfsema, and S. Bohte. “Learning Resets of Neural Working Memory”. In: 22nd European Symposium on Artificial Neural Networks, Computational Intelligence And Machine Learning. 2014. url: http://www.i6doc.com/en/livre/?GCOI=28001100432440. [OpenAIRE]

[4] J. Rombouts, P. Roelfsema, and S. Bohte. “Neurally Plausible Reinforcement Learning of Working Memory Tasks”. In: Advances in Neural Information Processing Systems 25. 2012. url: http : / / papers . nips . cc / paper / 4813 - neurally - plausible - reinforcement - learning - of - working-memory-tasks. [OpenAIRE]

[5] J. Vitay, H. Dinklebach, and F. Hamker. “ANNarchy: a code generation approach to neural simulations on parallel hardware”. In: Frontiers Neuroinformatics 9.19 (2015). doi: 10.3389/ fninf.2015.00019. [OpenAIRE]

[6] T. Yang and M. Shalden. “Probabilistic reasoning by neurons”. In: Nature 447.7148 (2007), pp. 1075-80. doi: 10.1038/nature05852.

[7] D. Zambrano, P. Roelfsema, and S. Bohte. “Continuous-time on-policy neural Reinforcement Learning of working memory tasks”. In: International Joint Conference on Neural Networks (2015). doi: 10.1109/IJCNN.2015.7280636. [OpenAIRE]

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publication . Article . Other literature type . 2016

How attention can create synaptic tags for the learning of working memories in sequential tasks.

Rombouts, Jaldert O.; Bohte, Sander M.; Roelfsema, Pieter R.;