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1810.11393
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Dendritic cortical microcircuits approximate the backpropagation algorithm
26 October 2018
João Sacramento
Rui Ponte Costa
Yoshua Bengio
Walter Senn
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Papers citing
"Dendritic cortical microcircuits approximate the backpropagation algorithm"
50 / 131 papers shown
Context-sensitive neocortical neurons transform the effectiveness and efficiency of neural information processing
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K. Ahmed
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The least-control principle for local learning at equilibrium
Neural Information Processing Systems (NeurIPS), 2022
Alexander Meulemans
Nicolas Zucchet
Seijin Kobayashi
J. Oswald
João Sacramento
233
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04 Jul 2022
Single-phase deep learning in cortico-cortical networks
Neural Information Processing Systems (NeurIPS), 2022
Will Greedy
He Zhu
Joe Pemberton
J. Mellor
Rui Ponte Costa
163
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23 Jun 2022
Biologically-plausible backpropagation through arbitrary timespans via local neuromodulators
Neural Information Processing Systems (NeurIPS), 2022
Yuhan Helena Liu
Stephen Smith
Stefan Mihalas
E. Shea-Brown
Uygar Sumbul
248
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02 Jun 2022
Beyond accuracy: generalization properties of bio-plausible temporal credit assignment rules
Neural Information Processing Systems (NeurIPS), 2022
Yuhan Helena Liu
Arna Ghosh
Blake A. Richards
E. Shea-Brown
Guillaume Lajoie
506
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0
02 Jun 2022
A Computational Framework of Cortical Microcircuits Approximates Sign-concordant Random Backpropagation
Yukun Yang
Peng Li
303
1
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15 May 2022
Minimizing Control for Credit Assignment with Strong Feedback
International Conference on Machine Learning (ICML), 2022
Alexander Meulemans
Matilde Tristany Farinha
Maria R. Cervera
João Sacramento
Benjamin Grewe
249
22
0
14 Apr 2022
Deep Learning in Spiking Phasor Neural Networks
Connor Bybee
E. P. Frady
Friedrich T. Sommer
120
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01 Apr 2022
Learning by non-interfering feedback chemical signaling in physical networks
Physical Review Research (Phys. Rev. Res.), 2022
Vidyesh Rao Anisetti
B. Scellier
J. M. Schwarz
127
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22 Mar 2022
Constrained Parameter Inference as a Principle for Learning
Nasir Ahmad
Ellen Schrader
Marcel van Gerven
408
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22 Mar 2022
Deep Layer-wise Networks Have Closed-Form Weights
International Conference on Artificial Intelligence and Statistics (AISTATS), 2020
Chieh-Tsai Wu
A. Masoomi
Arthur Gretton
Jennifer Dy
319
4
0
01 Feb 2022
Towards Scaling Difference Target Propagation by Learning Backprop Targets
International Conference on Machine Learning (ICML), 2022
M. Ernoult
Fabrice Normandin
A. Moudgil
Sean Spinney
Eugene Belilovsky
Irina Rish
Blake A. Richards
Yoshua Bengio
207
43
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31 Jan 2022
Learning on Arbitrary Graph Topologies via Predictive Coding
Neural Information Processing Systems (NeurIPS), 2022
Tommaso Salvatori
Luca Pinchetti
Beren Millidge
Yuhang Song
Tianyi Bao
Rafal Bogacz
Thomas Lukasiewicz
415
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31 Jan 2022
DELAUNAY: a dataset of abstract art for psychophysical and machine learning research
C. Gontier
Jakob Jordan
Mihai A. Petrovici
92
5
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28 Jan 2022
Error-driven Input Modulation: Solving the Credit Assignment Problem without a Backward Pass
International Conference on Machine Learning (ICML), 2022
Giorgia Dellaferrera
Gabriel Kreiman
391
73
0
27 Jan 2022
Including STDP to eligibility propagation in multi-layer recurrent spiking neural networks
Werner van der Veen
187
1
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05 Jan 2022
Accurate online training of dynamical spiking neural networks through Forward Propagation Through Time
Nature Machine Intelligence (NMI), 2021
Bojian Yin
Federico Corradi
S. Bohté
335
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20 Dec 2021
A Normative and Biologically Plausible Algorithm for Independent Component Analysis
Yanis Bahroun
D. Chklovskii
Anirvan M. Sengupta
157
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17 Nov 2021
How and When Random Feedback Works: A Case Study of Low-Rank Matrix Factorization
Shivam Garg
Santosh Vempala
317
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17 Nov 2021
BioLeaF: A Bio-plausible Learning Framework for Training of Spiking Neural Networks
Yukun Yang
Peng Li
167
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14 Nov 2021
A New Look at Spike-Timing-Dependent Plasticity Networks for Spatio-Temporal Feature Learning
A. Safa
I. Ocket
A. Bourdoux
Hichem Sahli
F. Catthoor
Georges G. E. Gielen
255
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01 Nov 2021
Latent Equilibrium: A unified learning theory for arbitrarily fast computation with arbitrarily slow neurons
Paul Haider
B. Ellenberger
Laura Kriener
Jakob Jordan
Walter Senn
Mihai A. Petrovici
199
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27 Oct 2021
Cortico-cerebellar networks as decoupling neural interfaces
Neural Information Processing Systems (NeurIPS), 2021
J. Pemberton
E. Boven
Richard Apps
Rui Ponte Costa
179
7
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21 Oct 2021
Assemblies of neurons learn to classify well-separated distributions
M. Dabagia
Christos H. Papadimitriou
Santosh Vempala
172
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07 Oct 2021
Variational learning of quantum ground states on spiking neuromorphic hardware
Robert Klassert
A. Baumbach
Mihai A. Petrovici
M. Gärttner
169
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30 Sep 2021
Learning through structure: towards deep neuromorphic knowledge graph embeddings
Victor Caceres Chian
Marcel Hildebrandt
Thomas Runkler
Dominik Dold
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153
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21 Sep 2021
Learning cortical representations through perturbed and adversarial dreaming
eLife (eLife), 2021
Nicolas Deperrois
Mihai A. Petrovici
Walter Senn
Jakob Jordan
GAN
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244
26
0
09 Sep 2021
Online Training of Spiking Recurrent Neural Networks with Phase-Change Memory Synapses
Yiğit Demirağ
Charlotte Frenkel
Melika Payvand
Giacomo Indiveri
257
18
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04 Aug 2021
Predictive Coding: a Theoretical and Experimental Review
Beren Millidge
A. Seth
Christopher L. Buckley
AI4CE
492
164
0
27 Jul 2021
Credit Assignment in Neural Networks through Deep Feedback Control
Neural Information Processing Systems (NeurIPS), 2021
Alexander Meulemans
Matilde Tristany Farinha
Javier García Ordónez
Pau Vilimelis Aceituno
João Sacramento
Benjamin Grewe
312
45
0
15 Jun 2021
The Backpropagation Algorithm Implemented on Spiking Neuromorphic Hardware
Nature Communications (Nat Commun), 2021
Alpha Renner
F. Sheldon
Anatoly Zlotnik
L. Tao
A. Sornborger
200
52
0
13 Jun 2021
Front Contribution instead of Back Propagation
Swaroop Mishra
Anjana Arunkumar
148
0
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10 Jun 2021
Bottom-up and top-down approaches for the design of neuromorphic processing systems: Tradeoffs and synergies between natural and artificial intelligence
Proceedings of the IEEE (Proc. IEEE), 2021
Charlotte Frenkel
D. Bol
Giacomo Indiveri
279
58
0
02 Jun 2021
Neko: a Library for Exploring Neuromorphic Learning Rules
International Conference on Systems (ICONS), 2021
Zixuan Zhao
Nathan Wycoff
N. Getty
Rick L. Stevens
Fangfang Xia
219
3
0
01 May 2021
An error-propagation spiking neural network compatible with neuromorphic processors
International Conference on Artificial Intelligence Circuits and Systems (ICAICS), 2020
M. Cartiglia
G. Haessig
Giacomo Indiveri
95
5
0
12 Apr 2021
A contrastive rule for meta-learning
Neural Information Processing Systems (NeurIPS), 2021
Nicolas Zucchet
Simon Schug
J. Oswald
Dominic Zhao
João Sacramento
MLT
303
22
0
04 Apr 2021
Self-learning Machines based on Hamiltonian Echo Backpropagation
Physical Review X (PRX), 2021
V. López-Pastor
F. Marquardt
301
45
0
08 Mar 2021
Reverse Differentiation via Predictive Coding
AAAI Conference on Artificial Intelligence (AAAI), 2021
Tommaso Salvatori
Yuhang Song
Thomas Lukasiewicz
Rafal Bogacz
Zhenghua Xu
PINN
335
27
0
08 Mar 2021
The Yin-Yang dataset
Neuro Inspired Computational Elements Workshop (NICE), 2021
Laura Kriener
Julian Goltz
Mihai A. Petrovici
3DH
121
30
0
16 Feb 2021
The Neural Coding Framework for Learning Generative Models
Nature Communications (Nat Commun), 2020
Alexander Ororbia
Daniel Kifer
GAN
513
78
0
07 Dec 2020
A biologically plausible neural network for local supervision in cortical microcircuits
Siavash Golkar
David Lipshutz
Yanis Bahroun
Anirvan M. Sengupta
D. Chklovskii
169
7
0
30 Nov 2020
Kernel Dependence Network
Chieh-Tsai Wu
A. Masoomi
Arthur Gretton
Jennifer Dy
168
0
0
04 Nov 2020
A simple normative network approximates local non-Hebbian learning in the cortex
Neural Information Processing Systems (NeurIPS), 2020
Siavash Golkar
David Lipshutz
Yanis Bahroun
Anirvan M. Sengupta
D. Chklovskii
OffRL
163
16
0
23 Oct 2020
Brain-Inspired Learning on Neuromorphic Substrates
Friedemann Zenke
Emre Neftci
266
95
0
22 Oct 2020
Local plasticity rules can learn deep representations using self-supervised contrastive predictions
Neural Information Processing Systems (NeurIPS), 2020
Bernd Illing
Jean-Paul Ventura
G. Bellec
W. Gerstner
SSL
DRL
493
85
0
16 Oct 2020
EqSpike: Spike-driven Equilibrium Propagation for Neuromorphic Implementations
Erwann Martin
M. Ernoult
Jérémie Laydevant
Shuai-shuai Li
D. Querlioz
Teodora Petrisor
Julie Grollier
385
57
0
15 Oct 2020
Investigating the Scalability and Biological Plausibility of the Activation Relaxation Algorithm
Beren Millidge
Alexander Tschantz
A. Seth
Christopher L. Buckley
142
0
0
13 Oct 2020
Relaxing the Constraints on Predictive Coding Models
Beren Millidge
Alexander Tschantz
A. Seth
Christopher L. Buckley
245
24
0
02 Oct 2020
A biologically plausible neural network for multi-channel Canonical Correlation Analysis
Neural Computation (Neural Comput.), 2020
David Lipshutz
Yanis Bahroun
Siavash Golkar
Anirvan M. Sengupta
Dmitri B. Chkovskii
294
25
0
01 Oct 2020
Activation Relaxation: A Local Dynamical Approximation to Backpropagation in the Brain
Beren Millidge
Alexander Tschantz
A. Seth
Christopher L. Buckley
ODL
325
18
0
11 Sep 2020
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