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In this article, we review a class of neuro-mimetic computational models that we place under the label of spiking predictive coding.
Predictive coding as a neuromorphic alternative to backpropagation: A critical evaluation
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Matching pursuits with time-frequency dictionaries
Mallat, S. G., and Zhang, Z · 1993
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Reliability of spike timing in neocortical neurons
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Emergence of simple-cell receptive field properties by learning a sparse code for natural images
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The “independent components” of natural scenes are edge filters
Bell, A. J., and Sejnowski, T. J · 1997
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Dynamic model of visual recognition predicts neural response properties in the visual cortex
Rao, R. P. N., and Ballard, D. H · 1997
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Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects
Rao, R. P. N., and Ballard, D. H · 1999
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Predictive sequence learning in recurrent neocortical circuits
Rao, R. P. N., and Sejnowski, T. J · 1999
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The information bottleneck method
Tishby, N., Pereira, F. C., and Bialek, W · 1999
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A single-spike model of predictive coding
Ballard, D. H., Rao, R. P. N., and Zhang, Z · 2000
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Action potentials reliably invade axonal arbors of rat neocortical neurons
Cox, C. L., Denk, W., Tank, D. W., and Svoboda, K · 2000
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Spike-timing-dependent hebbian plasticity as temporal difference learning
Rao, R. P. N., and Sejnowski, T. J · 2001
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Learning in networks of cortical neurons
Shahaf, G., and Marom, S · 2001
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Development, learning and memory in large random networks of cortical neurons: lessons beyond anatomy
Marom, S., and Shahaf, G · 2002
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Sparse image coding using an asynchronous spiking neural network
Perrinet, L., and Samuelides, M · 2002
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Predictive coding, cortical feedback, and spike-timing dependent plasticity
Rao, R. P. N., and Sejnowski, T. J · 2002
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Bayesian inference in spiking neurons
Denève, S · 2004
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Sparse coding of sensory inputs
Olshausen, B. A., and Field, D. J · 2004
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Sparse spike coding in an asynchronous feed-forward multi-layer neural network using matching pursuit
Perrinet, L., Samuelides, M., and Thorpe, S · 2004
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Hierarchical Bayesian inference in networks of spiking neurons
Rao, R. P. N · 2004
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A single spike model of predictive coding
Zhang, Z., and Ballard, D. H · 2004
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A theory of cortical responses
Friston, K · 2005
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Efficient coding of time-relative structure using spikes
Smith, E., and Lewicki, M. S · 2005
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Generalized Bienenstock–Cooper–Munro rule for spiking neurons that maximizes information transmission
Toyoizumi, T., Pfister, J.-P., Aihara, K., and Gerstner, W · 2005
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Efficient representation as a design principle for neural coding and computation
Bialek, W., de Ruyter van Steveninck, R. R., and Tishby, N · 2006
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Information bottleneck optimization and independent component extraction with spiking neurons
Klampfl, S., Maass, W., and Legenstein, R · 2006
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Efficient sparse coding algorithms
Lee, H., Battle, A., Raina, R., and Ng, A · 2006
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Simplified rules and theoretical analysis for information bottleneck optimization and pca with spiking neurons
Buesing, L., and Maass, W · 2007
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Bayesian Spiking Neurons I: Inference
Deneve, S · 2008
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Bayesian Spiking Neurons II: Learning
Deneve, S · 2008
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DEM: A variational treatment of dynamic systems
Friston, K. J., Trujillo-Barreto, N., and Daunizeau, J · 2008
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Independent component analysis
Hyvärinen, A., Hurri, J., Hoyer, P. O., Hyvärinen, A., Hurri, J., and Hoyer, P. O · 2009
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Spiking neurons can learn to solve information bottleneck problems and extract independent components
Klampfl, S., Legenstein, R., and Maass, W · 2009
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Connectivity reflects coding: a model of voltage-based stdp with homeostasis
Clopath, C., Büsing, L., Vasilaki, E., and Gerstner, W · 2010
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The free-energy principle: a unified brain theory?
Friston, K · 2010
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Independent component analysis in spiking neurons
Savin, C., Joshi, P., and Triesch, J · 2010
Cited alongside, same era.
Spike-based population coding and working memory
Boerlin, M., and Denève, S · 2011
Cited alongside, same era.
A triplet spike-timing–dependent plasticity model generalizes the bienenstock–cooper–munro rule to higher-order spatiotemporal correlations
Gjorgjieva, J., Clopath, C., Audet, J., and Pfister, J.-P · 2011
Cited alongside, same era.
Predictive coding
Huang, Y., and Rao, R. P. N · 2011
Cited alongside, same era.
Zylberberg, J., Murphy, J. T., and DeWeese, M. R · 2011
Cited alongside, same era.
Learning optimal spike-based representations
Green AI
Schwartz, R., Dodge, J., Smith, N. A., and Etzioni, O · 2020
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Tightening the biological constraints on gradient-based predictive coding
Alonso, N., and Neftci, E · 2021
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Local dendritic balance enables learning of efficient representations in networks of spiking neurons
Mikulasch, F. A., Rudelt, L., and Priesemann, V · 2021
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Predictive coding: A theoretical and experimental review
Millidge, B., Seth, A., and Buckley, C. L · 2021
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Feature selectivity can explain mismatch signals in mouse visual cortex
Muzzu, T., and Saleem, A. B · 2021
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Nonlinear computations in spiking neural networks through multiplicative synapses
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Bourdoukan, R., Barrett, D. G. T., Machens, C. K., and Denève, S · 2012
Cited alongside, same era.
Spectral analysis of input spike trains by spike-timing-dependent plasticity
Gilson, M., Fukai, T., and Burkitt, A. N · 2012
Cited alongside, same era.
A network of spiking neurons for computing sparse representations in an energy-efficient way
Hu, T., Genkin, A., and Chklovskii, D. B · 2012
Cited alongside, same era.
A neuronal model of predictive coding accounting for the mismatch negativity
Wacongne, C., Changeux, J.-P., and Dehaene, S · 2012
Cited alongside, same era.
Predictive Coding of Dynamical Variables in Balanced Spiking Networks
Boerlin, M., Machens, C. K., and Denève, S · 2013
Cited alongside, same era.
Optimal compensation for neuron loss
Barrett, D. G. T., Denève, S., and Machens, C. K · 2014
Cited alongside, same era.
Spike frequency adaptation
Gutkin, B. S., and Zeldenrust, F · 2014
Cited alongside, same era.
Nardin, M., Phillips, J. W., Podlaski, W. F., and Keemink, S. W · 2021
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Active Inference: Demystified and Compared
Sajid, N., Ball, P. J., Parr, T., and Friston, K. J · 2021
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The generation of cortical novelty responses through inhibitory plasticity
Schulz, A., Miehl, C., Berry II, M. J., and Gjorgjieva, J · 2021
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Efficient and robust coding in heterogeneous recurrent networks
Zeldenrust, F., Gutkin, B., and Denéve, S · 2021
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The geometry of robustness in spiking neural networks
Calaim, N., Dehmelt, F. A., Gonçalves, P. J., and Machens, C. K · 2022
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A brain-inspired robot pain model based on a spiking neural network
Feng, H., and Zeng, Y · 2022
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Emergence of associative learning in a neuromorphic inference network
Gandolfi, D., Puglisi, F. M., Boiani, G. M., Pagnoni, G., Friston, K. J., D’Angelo, E., and Mapelli, J · 2022
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Prediction-error neurons in circuits with multiple neuron types: Formation, refinement, and functional implications
Hertäg, L., and Clopath, C · 2022
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Predictive Coding Theories of Cortical Function
Jiang, L. P., and Rao, Rajesh P. N · 2022
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Biologically plausible solutions for spiking networks with efficient coding
Koren, V., and Panzeri, S · 2022
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Lan, M., Xiong, X., Jiang, Z., and Lou, Y · 2022
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Brain-inspired computing needs a master plan
Mehonic, A., and Kenyon, A. J · 2022
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Optimal noise level for coding with tightly balanced networks of spiking neurons in the presence of transmission delays
Timcheck, J., Kadmon, J., Boahen, K., and Ganguli, S · 2022
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Evaluating the extent to which homeostatic plasticity learns to compute prediction errors in unstructured neuronal networks
Zhu, V., and Rosenbaum, R · 2022
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Embedding stochastic dynamics of the environment in spontaneous activity by prediction-based plasticity
Asabuki, T., and Clopath, C · 2023
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Learning predictive signals within a local recurrent circuit
Asabuki, T., Gillon, C. J., and Clopath, C · 2023
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Feed-forward optimization with delayed feedback for neural networks
Flügel, K., Coquelin, D., Weiel, M., Debus, C., Streit, A., and Götz, M · 2023
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Competition between bottom-up visual input and internal inhibition generates error neurons in a model of the mouse primary visual cortex
Fraile, J. G., Scherr, F., Ramasco, J. J., Arkhipov, A., Maass, W., and Mirasso, C. R · 2023
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Where is the error? hierarchical predictive coding through dendritic error computation
Mikulasch, F. A., Rudelt, L., Wibral, M., and Priesemann, V · 2023
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Predictive Coding Light: learning compact visual codes by combining excitatory and inhibitory spike timing-dependent plasticity*
N’Dri, A. W., Barbier, T., Teulière, C., and Triesch, J · 2023
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Spiking neural predictive coding for continually learning from data streams
Ororbia, A · 2023
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Mortal computation: A foundation for biomimetic intelligence
Ororbia, A., and Friston, K · 2023
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Brain-inspired computational intelligence via predictive coding
Salvatori, T., Mali, A., Buckley, C. L., Lukasiewicz, T., Rao, R. P. N., Friston, K., and Ororbia, A · 2023
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Sequence anticipation and spike-timing-dependent plasticity emerge from a predictive learning rule
Saponati, M., and Vinck, M · 2023
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Closed-Form Control With Spike Coding Networks
Slijkhuis, F. S., Keemink, S. W., and Lanillos, P · 2023
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Prediction mismatch responses arise as corrections of a predictive spiking code
van Driel, K., Rudelt, L., Priesemann, V., and Mikulasch, F. A · 2023
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Neural heterogeneity controls computations in spiking neural networks
Gast, R., Solla, S. A., and Kennedy, A · 2024
Closest in time.
Design and evaluation of brain-inspired predictive coding networks based on the free-energy principle for novel neuromorphic hardware
Hagiwara, N., Kunimi, T., Ando, K., Akai-Kasaya, M., and Asai, T · 2024
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Integrating predictive coding with reservoir computing in spiking neural network model of cultured neurons
Ishikawa, Y., Shinkawa, T., Sumi, T., Kato, H., Yamamoto, H., and Katori, Y · 2024
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Predictive coding with spiking neurons and feedforward gist signaling
Lee, K., Dora, S., Mejias, J. F., Bohte, S. M., and Pennartz, C. M · 2024
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A review of neuroscience-inspired machine learning
Ororbia, A., Mali, A., Kohan, A., Millidge, B., and Salvatori, T · 2024
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A sensory-motor theory of the neocortex
Rao, R. P. N · 2024
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Active predictive coding: A unifying neural model for active perception, compositional learning, and hierarchical planning
Rao, R. P. N., Gklezakos, D. C., and Sathish, V · 2024
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The tuning of tuning: How adaptation influences single cell information transfer
Zeldenrust, F., Calcini, N., Yan, X., Bijlsma, A., and Celikel, T · 2024
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Energy optimization induces predictive-coding properties in a multicompartment spiking neural network model
Zhang, M., and Bohte, S · 2024
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The neural coding framework for learning generative models
Ororbia, A., and Kifer, D · 2064
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