A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Filters
Dendritic cortical microcircuits approximate the backpropagation algorithm
[article]
2018
arXiv
pre-print
Preserved Fulltext
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]
2017
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
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
2022
Neurons, Behavior, Data analysis, and Theory
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
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]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
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]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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
2016
Frontiers in Neuroinformatics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Other Versions
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]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Note that this fulltext copy is not of the "primary" version of this work. The version it corresponds to is:
2021 pre-print arXiv:2105.05382v1 fatcat:4pruygtpn5gs3fn47qjf3sfxzu
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]
2021
bioRxiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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
2020
Proceedings of the National Academy of Sciences of the United States of America
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Other Versions
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
2021
Neural Computation
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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]
2016
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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
2016
Frontiers in Computational Neuroscience
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
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]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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]
2016
bioRxiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
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
« Previous
Showing results 1 — 15 out of 142 results