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Dendritic cortical microcircuits approximate the backpropagation algorithm [article]

João Sacramento, Rui Ponte Costa, Yoshua Bengio, Walter Senn
2018 arXiv   pre-print
We demonstrate the learning capabilities of the model in regression and classification tasks, and show analytically that it approximates the error backpropagation algorithm.  ...  Moreover, our framework is consistent with recent observations of learning between brain areas and the architecture of cortical microcircuits.  ...  Acknowledgements The authors would like to thank Timothy P. Lillicrap  ... 
arXiv:1810.11393v1 fatcat:jetulqdayrdrtnbh4ejzfqtusq

Dendritic error backpropagation in deep cortical microcircuits [article]

João Sacramento and Rui Ponte Costa and Yoshua Bengio and Walter Senn
2017 arXiv   pre-print
We demonstrate the learning capabilities of the model in a number of tasks and show that it approximates the classical error backpropagation algorithm.  ...  Our framework makes several experimental predictions on the function of dendritic integration and cortical microcircuits, is consistent with recent observations of cross-area learning, and suggests a biological  ...  WS thanks Matthew Larkum for many discussions on dendritic processing and the option of dendritic error representation. In addition, JS thanks Elena Kreutzer, Pascal Leimer and Martin T.  ... 
arXiv:1801.00062v1 fatcat:uruwi5mq3bgxdl7fgeokof55dm

Current State and Future Directions for Learning in Biological Recurrent Neural Networks: A Perspective Piece

Roy Henha Eyono, Ellen Boven, Arna Ghosh, Joseph Pemberton, Franz Scherr, Claudia Clopath, Rui Ponte Costa, Wolfgang Maass, Blake A. Richards, Cristina Savin, Katharina Wilmes, Luke Y. Prince
2022 Neurons, Behavior, Data analysis, and Theory  
We provide a brief review of the common assumptions about biological learning with findings from experimental neuroscience and contrast them with the efficiency of gradientbased learning in recurrent neural  ...  We conclude with recommendations for both theoretical and experimental neuroscientists when designing new studies that could help bring clarity to these issues. a recurrent neural networks, backpropagation  ...  Backpropagation through time and the brain. Current opinion in neurobiology 2019;55:82-89. [2] Jozefowicz R, Zaremba W, Sutskever I. An empirical exploration of recurrent network architectures.  ... 
doi:10.51628/001c.35302 fatcat:ahqqn4rpw5dw3mu3n5tzb2auqq

Dendritic predictive coding: A theory of cortical computation with spiking neurons [article]

Fabian A. Mikulasch, Lucas Rudelt, Michael Wibral, Viola Priesemann
2022 arXiv   pre-print
The implied model shows a remarkable correspondence to experimentally observed cortical connectivity patterns, plasticity and dynamics, and at the same time can explain hallmarks of predictive processing  ...  We thus propose dendritic predictive coding as one of the main organizational principles of cortex.  ...  comments on the manuscript.  ... 
arXiv:2205.05303v1 fatcat:y6std5r4yrbwhlzsvmn46vvii4

A simple normative network approximates local non-Hebbian learning in the cortex [article]

Siavash Golkar, David Lipshutz, Yanis Bahroun, Anirvan M. Sengupta, Dmitri B. Chklovskii
2020 arXiv   pre-print
We detail how, in our model, the calcium plateau potential can be interpreted as a backpropagating error signal.  ...  Neuroscience experiments demonstrate that the processing of sensory inputs by cortical neurons is modulated by instructive signals which provide context and task-relevant information.  ...  We further thank Nicholas Chua, Shiva Farashahi, Johannes Friedrich, Alexander Genkin, Tiberiu Tesileanu, and Charlie Windolf for providing feedback on the manuscript.  ... 
arXiv:2010.12660v1 fatcat:ly5hr4mr2zc2tkg2pwddzsowfm

BluePyOpt: Leveraging Open Source Software and Cloud Infrastructure to Optimise Model Parameters in Neuroscience

Werner Van Geit, Michael Gevaert, Giuseppe Chindemi, Christian Rössert, Jean-Denis Courcol, Eilif B. Muller, Felix Schürmann, Idan Segev, Henry Markram
2016 Frontiers in Neuroinformatics  
find suitable approximate solutions.  ...  Stochastic optimisation approaches, such as evolutionary algorithms, have been shown to be effective, but often the setting up of such optimisations and the choice of a specific search algorithm and its  ...  Dendritic Excitability and Gain Control in Recurrent Cortical Microcircuits [67] .  ... 
doi:10.3389/fninf.2016.00017 pmid:27375471 pmcid:PMC4896051 fatcat:n5ejvfz4crhxtbszkjvzydxg5e

Current State and Future Directions for Learning in Biological Recurrent Neural Networks: A Perspective Piece [article]

Luke Y. Prince, Roy Henha Eyono, Ellen Boven, Arna Ghosh, Joe Pemberton, Franz Scherr, Claudia Clopath, Rui Ponte Costa, Wolfgang Maass, Blake A. Richards, Cristina Savin, Katharina Anna Wilmes
2022 arXiv   pre-print
We provide a brief review of the common assumptions about biological learning with findings from experimental neuroscience and contrast them with the efficiency of gradient-based learning in recurrent  ...  The key issues discussed in this review include: synaptic plasticity, neural circuits, theory-experiment divide, and objective functions.  ...  We would also like to thank all other audience members from around the world who participated on the day in the chat, and everybody else who has watched the replay since it was posted online.  ... 
arXiv:2105.05382v2 fatcat:7vyo56k4rva7vdskumr6zxer4i

Reduced inhibition in depression impairs stimulus processing in human cortical microcircuits [article]

Heng Kang Yao, Alexandre Guet-McCreight, Frank Mazza, Homeira Moradi Chameh, Thomas D. Prevot, John Griffiths, Shreejoy J. Tripathy, Taufik A. Valiante, Etienne Sibille, Etay Hay
2021 bioRxiv   pre-print
Here, we tested the impact of reduced somatostatin interneuron inhibition on cortical processing in human microcircuits in depression using a data-driven computational approach.  ...  We integrated human cellular, circuit and gene-expression data to generate detailed models of human cortical microcircuits in health and depression.  ...  Acknowledgements HKY, AGM, FM and EH thank the Krembil Foundation for funding support. HKY and EH were also supported by a stipend award from the Department of Physiology at University of Toronto.  ... 
doi:10.1101/2021.02.17.431698 fatcat:3og5p42im5dk5lodn75katssga

The microcircuits of striatum in silico

J. J. Johannes Hjorth, Alexander Kozlov, Ilaria Carannante, Johanna Frost Nylén, Robert Lindroos, Yvonne Johansson, Anna Tokarska, Matthijs C. Dorst, Shreyas M. Suryanarayana, Gilad Silberberg, Jeanette Hellgren Kotaleski, Sten Grillner
2020 Proceedings of the National Academy of Sciences of the United States of America  
We focus on simulation at the striatal cellular/microcircuit level, in which the molecular/subcellular and systems levels meet.  ...  The membrane properties, soma–dendritic shape, and intrastriatal and extrastriatal synaptic interactions of these neurons are quite well described in the mouse, and therefore they can be simulated in sufficient  ...  Simulation of the Striatal Microcircuit with Cortical, Thalamic, and Dopaminergic Input.  ... 
doi:10.1073/pnas.2000671117 pmid:32321828 fatcat:f2glndojynb6fhqkutoi474qx4

A biologically plausible neural network for multi-channel Canonical Correlation Analysis [article]

David Lipshutz, Yanis Bahroun, Siavash Golkar, Anirvan M. Sengupta, Dmitri B. Chklovskii
2021 arXiv   pre-print
We explore the possibility that cortical microcircuits implement Canonical Correlation Analysis (CCA), an unsupervised learning method that projects the inputs onto a common subspace so as to maximize  ...  Cortical pyramidal neurons receive inputs from multiple distinct neural populations and integrate these inputs in separate dendritic compartments.  ...  Then, in Sections 4 and 5, we derive an extension of our CCA algorithm and map it onto the cortical microcircuit.  ... 
arXiv:2010.00525v4 fatcat:ttpnnb7kfnablmldvfh3wxs4t4

A Biologically Plausible Neural Network for Multichannel Canonical Correlation Analysis

David Lipshutz, Yanis Bahroun, Siavash Golkar, Anirvan M. Sengupta, Dmitri B. Chklovskii
2021 Neural Computation  
We explore the possibility that cortical microcircuits implement canonical correlation analysis (CCA), an unsupervised learning method that projects the inputs onto a common subspace so as to maximize  ...  Cortical pyramidal neurons receive inputs from multiple distinct neural populations and integrate these inputs in separate dendritic compartments.  ...  Then, in sections 4 and 5, we derive an extension of our CCA algorithm and map it onto the cortical microcircuit.  ... 
doi:10.1162/neco_a_01414 pmid:34412114 fatcat:nqx6a4koovb6dg7nfq6cpzlttq

Towards an integration of deep learning and neuroscience [article]

Adam Marblestone, Greg Wayne, Konrad Kording
2016 arXiv   pre-print
Here we think about the brain in terms of these ideas.  ...  architecture matched to the computational problems posed by behavior.  ...  participants of a Kavli Salon on Cortical Computation (Feb/Oct 2015) for helpful discussions.  ... 
arXiv:1606.03813v1 fatcat:tmmholydqbcplbc5ihg76yip6e

Toward an Integration of Deep Learning and Neuroscience

Adam H. Marblestone, Greg Wayne, Konrad P. Kording
2016 Frontiers in Computational Neuroscience  
Here we think about the brain in terms of these ideas.  ...  architecture matched to the computational problems posed by behavior.  ...  AUTHOR CONTRIBUTION All authors contributed ideas and co-wrote the paper. ACKNOWLEDGMENTS We thank Ken Hayworth for key discussions that led to this paper. We thank Ed Boyden, Chris Eliasmith, Gary  ... 
doi:10.3389/fncom.2016.00094 pmid:27683554 pmcid:PMC5021692 fatcat:yikwc4h5yvfj7gwzlimtw5n6ai

Latent Equilibrium: A unified learning theory for arbitrarily fast computation with arbitrarily slow neurons [article]

Paul Haider, Benjamin Ellenberger, Laura Kriener, Jakob Jordan, Walter Senn, Mihai A. Petrovici
2021 arXiv   pre-print
The resulting model can be interpreted as a biologically plausible approximation of error backpropagation in deep cortical networks with continuous-time, leaky neuronal dynamics and continuously active  ...  The response time of physical computational elements is finite, and neurons are no exception. In hierarchical models of cortical networks each layer thus introduces a response lag.  ...  We acknowledge the use of Fenix Infrastructure resources, which are partially funded from the European Union's Horizon2020 research and innovation programme through the ICEI project under the grant agreement  ... 
arXiv:2110.14549v1 fatcat:gbypwhc4rfcjbh5s5qlm7ds67a

Towards an integration of deep learning and neuroscience [article]

Adam Henry Marblestone, Greg Wayne, Konrad P Kording
2016 bioRxiv   pre-print
Here we think about the brain in terms of these ideas.  ...  architecture matched to the computational problems posed by behavior.  ...  Some cortical microcircuits could then, perhaps, compute the cost functions that are to be delivered to other cortical microcircuits.  ... 
doi:10.1101/058545 fatcat:4ryejpe2tnf7dgoaqhoastoiya
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