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Top-down feedback in cortex is critical for guiding sensory processing, which has prominently been formalized in the theory of hierarchical predictive coding (hPC).
“Generation of end-inhibition in the visual cortex via interlaminar connections”
Jürgen Bolz and Charles Gilbert · 1986
Earlier work this paper cites.
“Forming sparse representations by local anti-Hebbian learning”
Peter Földiak · 1990
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“Different voltage-dependent thresholds for inducing long-term depression and long-term potentiation in slices of rat visual cortex”
Alain Artola, S Bröcher and Wolf Singer · 1990
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“Emergence of simple-cell receptive field properties by learning a sparse code for natural images”
Bruno. Olshausen and David. Field · 1996
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“Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects” Bandiera_abtest: a Cg_type: Nature Research Journals Number: 1 Primary_atype: Research Publisher: Nature Publishing Group
Rajesh.. Rao and Dana. Ballard · 1999
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“Inhibitory control of LTP and LTD: stability of synapse strength”
Philip Steele and Michael Mauk · 1999
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“A unified model of NMDA receptor-dependent bidirectional synaptic plasticity”
Harel Shouval, Mark Bear and Leon Cooper · 2002
Earlier work this paper cites.
“Hierarchical Bayesian inference in the visual cortex”
Tai Lee and David Mumford · 2003
Earlier work this paper cites.
“Balanced inhibition underlies tuning and sharpens spike timing in auditory cortex”
Michael Wehr and Anthony Zador · 2003
Earlier work this paper cites.
“Local calcium signaling in neurons”
George Augustine, Fidel Santamaria and Keiko Tanaka · 2003
Earlier work this paper cites.
“Maturation of long-term potentiation induction rules in rodent hippocampus: role of GABAergic inhibition”
Rhiannon Meredith, Anna Floyer-Lea and Ole Paulsen · 2003
Earlier work this paper cites.
“Neuronal Circuits of the Neocortex”
Rodney. Douglas and Kevan.C. Martin · 2004
Earlier work this paper cites.
“Local structural balance and functional interaction of excitatory and inhibitory synapses in hippocampal dendrites”
Guosong Liu · 2004
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“Postsynaptic depolarization requirements for LTP and LTD: a critique of spike timing-dependent plasticity”
John Lisman and Nelson Spruston · 2005
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“A cooperative switch determines the sign of synaptic plasticity in distal dendrites of neocortical pyramidal neurons”
Per Sjöström and Michael Häusser · 2006
Earlier work this paper cites.
“Learning rules for spike timing-dependent plasticity depend on dendritic synapse location”
Johannes Letzkus, Björn Kampa and Greg Stuart · 2006
Earlier work this paper cites.
“Neocortical inhibitory terminals innervate dendritic spines targeted by thalamocortical afferents”
Yoshiyuki Kubota et al · 2007
Earlier work this paper cites.
“Predictive coding as a model of biased competition in visual attention”
M.. Spratling · 2008
Earlier work this paper cites.
“Instantaneous correlation of excitation and inhibition during ongoing and sensory-evoked activities”
Michael Okun and Ilan Lampl · 2008
Earlier work this paper cites.
“Towards understanding of the cortical network underlying associative memory”
Takahiro Osada, Yusuke Adachi, Hiroko Kimura and Yasushi Miyashita · 2008
Earlier work this paper cites.
“Statistically optimal perception and learning: from behavior to neural representations”
József Fiser, Pietro Berkes, Gergő Orbán and Máté Lengyel · 2010
Earlier work this paper cites.
“Questions about STDP as a general model of synaptic plasticity”
John Lisman and Nelson Spruston · 2010
Earlier work this paper cites.
“Voltage and spike timing interact in STDP–a unified model”
Claudia Clopath and Wulfram Gerstner · 2010
Earlier work this paper cites.
“Predictive coding as a model of response properties in cortical area V1”
Michael Spratling · 2010
Earlier work this paper cites.
“Connectivity reflects coding: a model of voltage-based STDP with homeostasis”
Claudia Clopath, Lars Büsing, Eleni Vasilaki and Wulfram Gerstner · 2010
Earlier work this paper cites.
“Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons”
Lars Buesing, Johannes Bill, Bernhard Nessler and Wolfgang Maass · 2011
Earlier work this paper cites.
“Spike-based population coding and working memory”
Martin Boerlin and Sophie Denève · 2011
Earlier work this paper cites.
“Coordination of size and number of excitatory and inhibitory synapses results in a balanced structural plasticity along mature hippocampal CA1 dendrites during LTP”
Jennifer. Bourne and Kristen. Harris · 2011
Earlier work this paper cites.
“Activity-Dependent Clustering of Functional Synaptic Inputs on Developing Hippocampal Dendrites”
Thomas Kleindienst et al · 2011
Earlier work this paper cites.
“Canonical Microcircuits for Predictive Coding”
Andre. Bastos et al · 2012
Earlier work this paper cites.
“Learning optimal spike-based representations”
Ralph Bourdoukan, David Barrett, Sophie Deneve and Christian Machens · 2012
Earlier work this paper cites.
“Normalization as a canonical neural computation”
Matteo Carandini and David Heeger · 2012
Earlier work this paper cites.
“Multistability and perceptual inference”
Samuel Gershman, Edward Vul and Joshua Tenenbaum · 2012
Earlier work this paper cites.
“Microcircuits of excitatory and inhibitory neurons in layer 2/3 of mouse barrel cortex”
Michael Avermann et al · 2012
Earlier work this paper cites.
“Clustered Dynamics of Inhibitory Synapses and Dendritic Spines in the Adult Neocortex”
Jerry. Chen et al · 2012
Earlier work this paper cites.
“Cell-type homologies and the origins of the neocortex”
Jennifer Dugas-Ford, Joanna Rowell and Clifton Ragsdale · 2012
Earlier work this paper cites.
“A cellular mechanism for cortical associations: an organizing principle for the cerebral cortex”
Matthew Larkum · 2013
Earlier work this paper cites.
“GABA promotes the competitive selection of dendritic spines by controlling local Ca 2+ signaling”
Tatsuya Hayama et al · 2013
Earlier work this paper cites.
“Synaptic computation and sensory processing in neocortical layer 2/3”
Carl Petersen and Sylvain Crochet · 2013
Cited alongside, same era.
“Corticocortical feedback contributes to surround suppression in V1 of the alert primate”
Jonathan Nassi, Stephen Lomber and Richard Born · 2013
Cited alongside, same era.
“Top-down influences on visual processing”
Charles Gilbert and Wu Li · 2013
Cited alongside, same era.
“Predictive Coding of Dynamical Variables in Balanced Spiking Networks” Publisher: Public Library of Science
Martin Boerlin, Christian. Machens and Sophie Denève · 2013
Cited alongside, same era.
“Neocortical evolution: neuronal circuits arise independently of lamination”
Harvey Karten · 2013
Cited alongside, same era.
“Cortical connectivity and sensory coding”
Kenneth Harris and Thomas Mrsic-Flogel · 2013
“How do expectations shape perception?”
Floris De, Micha Heilbron and Peter Kok · 2018
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“Dendritic cortical microcircuits approximate the backpropagation algorithm”
João Sacramento, Rui Costa, Yoshua Bengio and Walter Senn · 2018
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“Dendritic solutions to the credit assignment problem”
Blake Richards and Timothy Lillicrap · 2018
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“Predictive Processing: A Canonical Cortical Computation”
Georg. Keller and Thomas. Mrsic-Flogel · 2018
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“PV interneurons: critical regulators of E/I balance for prefrontal cortex-dependent behavior and psychiatric disorders”
Brielle Ferguson and Wen-Jun Gao · 2018
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“Top-down feedback controls spatial summation and response amplitude in primate visual cortex”
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Cited alongside, same era.
“Learning by the dendritic prediction of somatic spiking”
Robert Urbanczik and Walter Senn · 2014
Cited alongside, same era.
“STDP Installs in Winner-Take-All Circuits an Online Approximation to Hidden Markov Model Learning”
David Kappel, Bernhard Nessler and Wolfgang Maass · 2014
Cited alongside, same era.
“Neuronal dynamics: From single neurons to networks and models of cognition”
Wulfram Gerstner, Werner Kistler, Richard Naud and Liam Paninski · 2014
Cited alongside, same era.
“Localized GABAergic inhibition of dendritic Ca 2+ signalling”
Michael Higley · 2014
Cited alongside, same era.
“Inhibitory plasticity dictates the sign of plasticity at excitatory synapses”
Lang Wang and Arianna Maffei · 2014
Cited alongside, same era.
“Untangling GABAergic wiring in the cortical microcircuit”
Yoshiyuki Kubota · 2014
Cited alongside, same era.
Lauri Nurminen et al · 2018
Later among the works it cites.
“The functional organization of cortical feedback inputs to primary visual cortex”
Tiago Marques, Julia Nguyen, Gabriela Fioreze and Leopoldo Petreanu · 2018
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“Homology, neocortex, and the evolution of developmental mechanisms”
Steven Briscoe and Clifton Ragsdale · 2018
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“Does predictive coding have a future?”
Karl Friston · 2018
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“Theories of error back-propagation in the brain”
James Whittington and Rafal Bogacz · 2019
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“Endocannabinoid signaling mediates local dendritic coordination between excitatory and inhibitory synapses”
Hai Hu et al · 2019
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“Between perfectly critical and fully irregular: A reverberating model captures and predicts cortical spike propagation”
Jens Wilting and Viola Priesemann · 2019
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“Relaxing the constraints on predictive coding models”
Beren Millidge, Alexander Tschantz, Anil Seth and Christopher Buckley · 2020
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“Predictive coding in balanced neural networks with noise, chaos and delays”
Jonathan Kadmon, Jonathan Timcheck and Surya Ganguli · 2020
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“Poisson balanced spiking networks”
Camilleán Buxó and Jonathan Pillow · 2020
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“Evaluating the neurophysiological evidence for predictive processing as a model of perception”
Kevin Walsh, David McGovern, Andy Clark and Redmond O’Connell · 2020
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“Learning to represent signals spike by spike”
Wieland Brendel et al · 2020
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“Whole-neuron synaptic mapping reveals spatially precise excitatory/inhibitory balance limiting dendritic and somatic spiking”
Daniel Iascone et al · 2020
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“Heterosynaptic plasticity determines the set point for cortical excitatory-inhibitory balance”
Rachel Field et al · 2020
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“Learning prediction error neurons in a canonical interneuron circuit”
Loreen Hertäg and Henning Sprekeler · 2020
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“Opposing influence of top-down and bottom-up input on excitatory layer 2/3 neurons in mouse primary visual cortex”
Rebecca Jordan and Georg Keller · 2020
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“Encoding of 3d head orienting movements in the primary visual cortex”
Grigori Guitchounts, Javier Masis, Steffen Wolff and David Cox · 2020
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“Head movements control the activity of primary visual cortex in a luminance-dependent manner”
Guy Bouvier, Yuta Senzai and Massimo Scanziani · 2020
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“Playing the piano with the cortex: role of neuronal ensembles and pattern completion in perception and behavior”
Luis Carrillo-Reid and Rafael Yuste · 2020
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“Predictive Coding: a Theoretical and Experimental Review”
Beren Millidge, Anil Seth and Christopher. Buckley · 2021
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“Local Dendritic Balance Enables Learning of Efficient Representations in Networks of Spiking Neurons”
Fabian. Mikulasch, Lucas Rudelt and Viola Priesemann · 2021
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“Tightening the Biological Constraints on Gradient-Based Predictive Coding”
Nick Alonso and Emre Neftci · 2021
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“Learning divisive normalization in primary visual cortex”
Max Burg et al · 2021
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“Learning from unexpected events in the neocortical microcircuit”
Colleen Gillon et al · 2021
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“Network control through coordinated inhibition”
Lotte Herstel and Corette Wierenga · 2021
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“A cortical circuit for audio-visual predictions”
Aleena Garner and Georg Keller · 2021
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“Sparse deep predictive coding captures contour integration capabilities of the early visual system” Publisher: Public Library of Science
Victor Boutin et al · 2021
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“Emergence of synaptic organization and computation in dendrites”
Jan Kirchner and Julijana Gjorgjieva · 2021
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“Active dendrites enable strong but sparse inputs to determine orientation selectivity”
Lea Goetz, Arnd Roth and Michael Häusser · 2021
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“Github - Priesemann-Group/plasticity-experiment-dendritic-balance” Online; accessed 2022-04-25, https://github.com/Priesemann-Group/plasticity-experiment-dendritic-balance , 2022
Lucas Rudelt · 2022
Closest in time.
“Visuomotor mismatch responses as a hallmark of explaining away in causal inference”
Fabian Mikulasch, Lucas Rudelt and Viola Priesemann · 2022
Closest in time.
“Spatio-temporal representations of uncertainty in spiking neural networks”
Cristina Savin and Sophie Deneve · 2032
Closest in time.