Reinforcement learning of linking and tracing contours in recurrent neural networks
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The processing of a visual stimulus can be subdivided into a number of stages. Upon stimulus presentation there is an early phase of feedforward processing where the visual information is propagated from lower to higher visual areas for the extraction of basic and complex stimulus features. This is followed by a later phase where horizontal connections within areas and feedback connections from higher areas back to lower areas come into play. In this later phase, image elements that are behaviorally relevant are grouped by Gestalt grouping rules and are labeled in the cortex with enhanced neuronal activity (object-based attention in psychology). Recent neurophysiological studies revealed that reward-based learning influences these recurrent grouping processes, but it is not well understood how rewards train recurrent circuits for perceptual organization. This paper examines the mechanisms for reward-based learning of new grouping rules. We derive a learning rule that can explain how rewards influence the information flow through feedforward, horizontal and feedback connections. We illustrate the efficiency with two tasks that have been used to study the neuronal correlates of perceptual organization in early visual cortex. The first task is called contour-integration and demands the integration of collinear contour elements into an elongated curve. We show how reward-based learning causes an enhancement of the representation of the to-be-grouped elements at early levels of a recurrent neural network, just as is observed in the visual cortex of monkeys. The second task is curve-tracing where the aim is to determine the endpoint of an elongated curve composed of connected image elements. If trained with the new learning rule, neural networks learn to propagate enhanced activity over the curve, in accordance with neurophysiological data. We close the paper with a number of model predictions that can be tested in future neurophysiological and computational studies.
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For networks with more than three layers, this gives rise to a model based on columns containing parallel feedforward and feedback pathways, and for recurrent networks that settle into attractor states it gives a reinforcement-trained version of the Almeida/Pineda recurrent backpropagation algorithm.
“For networks with more than three layers, this gives rise to a model based on columns containing parallel feedforward and feedback pathways (Roelfsema and van Ooyen, 2005), and for recurrent networks that settle into attractor states it gives a reinforcement-trained version (Brosch et al., 2015) of the Almeida/Pineda recurrent backpropagation algorithm (Pineda, 1987).”
From Toward an integration of deep learning and neuroscience (2016) · cites this paper as Brosch et al., 2015
The AGREL networks and variants like KickBack give a way to compute an approximation to the full gradient in a reinforcement learning context using a feedback-based attention mechanism for credit assignment within the multi-layer network.
“The “attention-gated reinforcement learning” (AGREL) networks of Stanisor et al. (2013), Brosch et al. (2015), and Roelfsema and van Ooyen (2005), and variants like KickBack (Balduzzi, 2014), give a way to compute an approximation to the full gradient in a reinforcement learning context using a feedback-based attention mechanism for credit assignment within the multi-layer network.”
From Toward an integration of deep learning and neuroscience (2016) · cites this paper as Brosch et al., 2015
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Brosch, T., & Heiko Neumann, B. (2014) · Neural Computation · doi:10.1162/neco_a_00675 · Cited by 64 · see citing works
Self-citationHow Attention Can Create Synaptic Tags for the Learning of Working Memories in Sequential Tasks
Rombouts, J. O., Bohté, S. M., & Roelfsema, P. R. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004060 · Cited by 60 · see citing works
Self-citationComputational neurobiology is a useful tool in translational neurology: the example of ataxia
Brown, S.-A., McCullough, L., & Loew, L. (2014) · Frontiers in Neuroscience · doi:10.3389/fnins.2015.00001 · Cited by 59 · see citing works
Visual curve tracing properties.
Jolicoeur, P., Ullman, S., & Mackay, M. (1991) · Journal of Experimental Psychology Human Perception & Performance · doi:10.1037//0096-1523.17.4.997 · Cited by 59 · see citing works
Object-based attention in Chinese readers of Chinese words: Beyond Gestalt principles
Li, X., & Logan, G. D. (2008) · Psychonomic Bulletin & Review · doi:10.3758/pbr.15.5.945 · Cited by 53 · see citing works
The Representation of Erroneously Perceived Stimuli in the Primary Visual Cortex
Roelfsema, P. R., & Spekreijse, H. (2001) · Neuron · doi:10.1016/s0896-6273(01)00408-1 · Cited by 51 · see citing works
Self-citationA Growth-Cone Model for the Spread of Object-Based Attention during Contour Grouping
Pooresmaeili, A., & Roelfsema, P. R. (2014) · Current Biology · doi:10.1016/j.cub.2014.10.007 · Cited by 49 · see citing works
Self-citationRoelfsema, P. R., & Singer, W. (1998) · Cerebral Cortex · doi:10.1093/cercor/8.5.385 · Cited by 49 · see citing works
Self-citationElemental operations in vision
ROELFSEMA, P. (2005) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2005.03.012 · Cited by 48 · see citing works
Self-citationBackpropagation in perceptrons with feedback
Lmeida, L. B. (1988) · Cited by 46 · see citing works
Brosch, T., & Heiko Neumann, B. (2014) · Neural Networks · doi:10.1016/j.neunet.2014.02.005 · Cited by 45 · see citing works
Self-citationModulatory Effects of Metabotropic Glutamate Receptors on Local Cortical Circuits
Pasquale, R. D., & Sherman, S. M. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.0090-12.2012 · Cited by 40 · see citing works
A neural model of the temporal dynamics of figure–ground segregation in motion perception
Raudies, F., & Heiko Neumann, B. (2009) · Neural Networks · doi:10.1016/j.neunet.2009.10.005 · Cited by 39 · see citing works
Temporal constraints on the grouping of contour segments into spatially extended objects
Roelfsema, P. R., Scholte, H. S., & Spekreijse, H. (1999) · Vision Research · doi:10.1016/s0042-6989(98)00222-3 · Cited by 39 · see citing works
Self-citationSize invariance in curve tracing
Jolicœur, P., & Ingleton, M. (1991) · Memory & Cognition · doi:10.3758/bf03198493 · Cited by 37 · see citing works
Statistical Models of Natural Images and Cortical Visual Representation
Hyvärinen, A. (2009) · Topics in Cognitive Science · doi:10.1111/j.1756-8765.2009.01057.x · Cited by 36 · see citing works
Neuronal activity in the visual cortex reveals the temporal order of cognitive operations
Moro, S. I., Tolboom, M., Khayat, P. S., & Roelfsema, P. R. (2010) · The Journal of Neuroscience · doi:10.1523/jneurosci.1256-10.2010 · Cited by 35 · see citing works
Self-citationA Model of Motion Transparency Processing with Local Center-Surround Interactions and Feedback
Raudies, F., Mingolla, E., & Heiko Neumann, B. (2011) · Neural Computation · doi:10.1162/neco_a_00193 · Cited by 29 · see citing works
Belief states as a framework to explain extra-retinal influences in visual cortex
Nienborg, H., & Roelfsema, P. R. (2014) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2014.10.013 · Cited by 28 · see citing works
Self-citationThe Time Course of Perceptual Grouping in Natural Scenes
Korjoukov, I., Jeurissen, D., Kloosterman, N. A., Verhoeven, J. E., Scholte, H. S., & Roelfsema, P. R. (2012) · Psychological Science · doi:10.1177/0956797612443832 · Cited by 28 · see citing works
Self-citationMISHRA, A. K., & Aloimonos, Y. (2009) · International Journal of Humanoid Robotics · doi:10.1142/s0219843609001784 · Cited by 27 · see citing works
Extraction of Surface-Related Features in a Recurrent Model of V1-V2 Interactions
Weidenbacher, U., & Heiko Neumann, B. (2009) · PLoS ONE · doi:10.1371/journal.pone.0005909 · Cited by 26 · see citing works
Seeing surfaces: The brain's vision of the world
Neumann, H., Yazdanbakhsh, A., & Mingolla, E. (2007) · Physics of Life Reviews · doi:10.1016/j.plrev.2007.09.001 · Cited by 25 · see citing works
Self-citationBouecke, J. D., Tlapale, É., Kornprobst, P., & Heiko Neumann, B. (2010) · EURASIP Journal on Advances in Signal Processing · doi:10.1155/2011/781561 · Cited by 24 · see citing works
A single functional model of drivers and modulators in cortex
Spratling, M. (2013) · Journal of Computational Neuroscience · doi:10.1007/s10827-013-0471-7 · Cited by 22 · see citing works
Cousins, J. (2012) · Cited by 21 · see citing works
Weidenbacher, U., Bayerl, P., Neumann, H., et al. (2006) · ACM Transactions on Applied Perception · doi:10.1145/1166087.1166094 · Cited by 18 · see citing works
Self-citationVisual statistical learning can drive object-based attentional selection
Zhao, L., Cosman, J. D., Vatterott, D. B., Gupta, P., & Vecera, S. P. (2014) · Attention Perception & Psychophysics · doi:10.3758/s13414-014-0708-1 · Cited by 16 · see citing works
The Selective Tuning Model for Visual Attention
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Gintautas, V., Ham, M., Kunsberg, B., Barr, S., Brumby, S. P., Rasmussen, C. E., George, J. S., Nemenman, I. M., Bettencourt, L. M. A., & Kenyon, G. T. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002162 · Cited by 12 · see citing works
The Brain’s Sequential Parallelism: Perceptual Decision-Making and Early Sensory Responses
Brosch, T., & Neumann, H. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-34481-7_6 · Cited by 8 · see citing works
Self-citationALGORITHMS FOR THE DETECTION OF CONNECTEDNESS AND THEIR NEURAL IMPLEMENTATION
Roelfsema, P. R., Bohté, S. M., & Spekreijse, H. (1999) · Series in Mathematical Biology and Medicine · doi:10.1142/9789812818041_0004 · Cited by 7 · see citing works
Self-citationNeuronal Information Processing
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Attention-Gated Reinforcement Learning in Neural Networks—A Unified View
Brosch, T., Schwenker, F., & Neumann, H. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40728-4_34 · Cited by 4 · see citing works
Self-citationNeurally plausible reinforcement learning of working memory tasks (abstract)
Rombouts, J. O., Bohté, S. M., & Roelfsema, P. R. (2013) · Research Repository (Delft University of Technology) · Cited by 3 · see citing works
Self-citationSearch for the Neural Correlates of Learning to Discriminate Orientations: Figure 1.
Hegdé, J. (2006) · Journal of Neuroscience · doi:10.1523/jneurosci.3012-06.2006 · Cited by 3 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- [115]O’Reilly RC, Munakata Y (2000) Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain. Cambridge, Mass, USA: MIT Press.
- [121]Steil JJ (2004) Backpropagation–Decorrelation: Online Recurrent Learning with O(N) Complexity In: IJCNN. IEEE, volume 2, pp. 843– 8.
Cited by
Sorted by citations
Toward an integration of deep learning and neuroscience
Marblestone, A. H., Wayne, G., & Kording, K. P. (2016) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2016.00094 · Cited by 119 · see citing works
- Authors
- Brosch, T., Neumann, H., & Roelfsema, P. R.
- Affiliations
- Universität Ulm (DE)
- Netherlands Institute for Neuroscience (NL)
- Corresponding author
- Brosch, Tobias
- Roelfsema, Pieter R.
- ORCID
- Roelfsema, Pieter R.
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2015-10-23
- Volume
- 11
- Issue
- 10
- Pages
- e1004489
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC4619762
- OpenAlex
- W2138332780
- Cited by
- 33
- References
- 150
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 4 · 2023: 1 · 2022: 2 · 2020: 2 · 2019: 5 · 2018: 4 · 2017: 2 · 2016: 7
- FWCI
- 2.61
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- KNAW research portal (Royal Academy of Art and Sciences (KNAW)) (publishedVersion, other-oa)
- VU Research Portal (publishedVersion, other-oa)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Pure Amsterdam UMC (submittedVersion, other-oa)
- Funders
- European Commission (604102, 339490, 2007–2013, FP7 2007-2013, FP7/2007, 290011)
- Deutsche Forschungsgemeinschaft (FP7/2007–2013)
- Universität Ulm
- FP7 Ideas: European Research Council (339490)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- reward learning, perceptual organization, Gestalt grouping, contour integration, curve tracing, recurrent neural networks, feedback connections, object-based attention, early visual cortex
- MeSH terms
- Animals, Computer Simulation, Macaca, Memory, Models, Neurological, Nerve Net, Reinforcement, Psychology, Visual Cortex, Visual Perception, Feedback, Physiological
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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