A citation-based method for searching scientific literature

Blake A Richards, Timothy P Lillicrap. Curr Opin Neurobiol 2019
Times Cited: 40







List of co-cited articles
371 articles co-cited >1



Times Cited
  Times     Co-cited
Similarity


Towards deep learning with segregated dendrites.
Jordan Guerguiev, Timothy P Lillicrap, Blake A Richards. Elife 2017
109
50

Random synaptic feedback weights support error backpropagation for deep learning.
Timothy P Lillicrap, Daniel Cownden, Douglas B Tweed, Colin J Akerman. Nat Commun 2016
137
37


Deep learning.
Yann LeCun, Yoshua Bengio, Geoffrey Hinton. Nature 2015
30

Control of synaptic plasticity in deep cortical networks.
Pieter R Roelfsema, Anthony Holtmaat. Nat Rev Neurosci 2018
76
27


Behavioral time scale synaptic plasticity underlies CA1 place fields.
Katie C Bittner, Aaron D Milstein, Christine Grienberger, Sandro Romani, Jeffrey C Magee. Science 2017
206
27


Learning by the dendritic prediction of somatic spiking.
Robert Urbanczik, Walter Senn. Neuron 2014
77
25

Backpropagation and the brain.
Timothy P Lillicrap, Adam Santoro, Luke Marris, Colin J Akerman, Geoffrey Hinton. Nat Rev Neurosci 2020
101
25

Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs.
H Markram, J Lübke, M Frotscher, B Sakmann. Science 1997
22

Equilibrium Propagation: Bridging the Gap between Energy-Based Models and Backpropagation.
Benjamin Scellier, Yoshua Bengio. Front Comput Neurosci 2017
58
20



Theories of Error Back-Propagation in the Brain.
James C R Whittington, Rafal Bogacz. Trends Cogn Sci 2019
70
20

Dendritic spikes as a mechanism for cooperative long-term potentiation.
Nace L Golding, Nathan P Staff, Nelson Spruston. Nature 2002
435
20

Conjunctive input processing drives feature selectivity in hippocampal CA1 neurons.
Katie C Bittner, Christine Grienberger, Sachin P Vaidya, Aaron D Milstein, John J Macklin, Junghyup Suh, Susumu Tonegawa, Jeffrey C Magee. Nat Neurosci 2015
244
20



Eligibility Traces and Plasticity on Behavioral Time Scales: Experimental Support of NeoHebbian Three-Factor Learning Rules.
Wulfram Gerstner, Marco Lehmann, Vasiliki Liakoni, Dane Corneil, Johanni Brea. Front Neural Circuits 2018
62
17

Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits.
Alexandre Payeur, Jordan Guerguiev, Friedemann Zenke, Blake A Richards, Richard Naud. Nat Neurosci 2021
26
26

A deep learning framework for neuroscience.
Blake A Richards, Timothy P Lillicrap, Philippe Beaudoin, Yoshua Bengio, Rafal Bogacz, Amelia Christensen, Claudia Clopath, Rui Ponte Costa, Archy de Berker, Surya Ganguli,[...]. Nat Neurosci 2019
174
17

Canonical microcircuits for predictive coding.
Andre M Bastos, W Martin Usrey, Rick A Adams, George R Mangun, Pascal Fries, Karl J Friston. Neuron 2012
15

Using goal-driven deep learning models to understand sensory cortex.
Daniel L K Yamins, James J DiCarlo. Nat Neurosci 2016
415
15

Toward an Integration of Deep Learning and Neuroscience.
Adam H Marblestone, Greg Wayne, Konrad P Kording. Front Comput Neurosci 2016
174
15


Pyramidal neuron as two-layer neural network.
Panayiota Poirazi, Terrence Brannon, Bartlett W Mel. Neuron 2003
378
15

A critical time window for dopamine actions on the structural plasticity of dendritic spines.
Sho Yagishita, Akiko Hayashi-Takagi, Graham C R Ellis-Davies, Hidetoshi Urakubo, Shin Ishii, Haruo Kasai. Science 2014
284
15


Active properties of neocortical pyramidal neuron dendrites.
Guy Major, Matthew E Larkum, Jackie Schiller. Annu Rev Neurosci 2013
224
15

Dendritic computation.
Michael London, Michael Häusser. Annu Rev Neurosci 2005
589
15

Synaptic Plasticity Forms and Functions.
Jeffrey C Magee, Christine Grienberger. Annu Rev Neurosci 2020
86
15

Inhibitory plasticity balances excitation and inhibition in sensory pathways and memory networks.
T P Vogels, H Sprekeler, F Zenke, C Clopath, W Gerstner. Science 2011
303
12


Predictive Processing: A Canonical Cortical Computation.
Georg B Keller, Thomas D Mrsic-Flogel. Neuron 2018
173
12



Deep neural networks rival the representation of primate IT cortex for core visual object recognition.
Charles F Cadieu, Ha Hong, Daniel L K Yamins, Nicolas Pinto, Diego Ardila, Ethan A Solomon, Najib J Majaj, James J DiCarlo. PLoS Comput Biol 2014
208
12


Distinct Eligibility Traces for LTP and LTD in Cortical Synapses.
Kaiwen He, Marco Huertas, Su Z Hong, XiaoXiu Tie, Johannes W Hell, Harel Shouval, Alfredo Kirkwood. Neuron 2015
92
12

Sensory-evoked LTP driven by dendritic plateau potentials in vivo.
Frédéric Gambino, Stéphane Pagès, Vassilis Kehayas, Daniela Baptista, Roberta Tatti, Alan Carleton, Anthony Holtmaat. Nature 2014
142
12


Sparse bursts optimize information transmission in a multiplexed neural code.
Richard Naud, Henning Sprekeler. Proc Natl Acad Sci U S A 2018
42
12

GABAergic Interneurons in the Neocortex: From Cellular Properties to Circuits.
Robin Tremblay, Soohyun Lee, Bernardo Rudy. Neuron 2016
758
12

Synaptic integration in tuft dendrites of layer 5 pyramidal neurons: a new unifying principle.
Matthew E Larkum, Thomas Nevian, Maya Sandler, Alon Polsky, Jackie Schiller. Science 2009
398
12

Dendritic action potentials and computation in human layer 2/3 cortical neurons.
Albert Gidon, Timothy Adam Zolnik, Pawel Fidzinski, Felix Bolduan, Athanasia Papoutsi, Panayiota Poirazi, Martin Holtkamp, Imre Vida, Matthew Evan Larkum. Science 2020
98
12

Synaptic plasticity: taming the beast.
L F Abbott, S B Nelson. Nat Neurosci 2000
954
10

Human-level control through deep reinforcement learning.
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski,[...]. Nature 2015
10

A neural substrate of prediction and reward.
W Schultz, P Dayan, P R Montague. Science 1997
10

Toward a Neurocentric View of Learning.
Heather K Titley, Nicolas Brunel, Christian Hansel. Neuron 2017
105
10


Co-cited is the co-citation frequency, indicating how many articles cite the article together with the query article. Similarity is the co-citation as percentage of the times cited of the query article or the article in the search results, whichever is the lowest. These numbers are calculated for the last 100 citations when articles are cited more than 100 times.