Supervised learning with predictive coding (Whittington & Bogacz 2017)


Whittington JCR, Bogacz R. (2017). An Approximation of the Error Backpropagation Algorithm in a Predictive Coding Network with Local Hebbian Synaptic Plasticity. Neural computation. 29 [PubMed]

See more from authors: Whittington JCR · Bogacz R

References and models cited by this paper

Ballard DH, de Sa VR. (1998). Perceptual learning from cross-modal feedback Psychology of Learning and Motivation. 36

Barto AG, Jordan MI. (1987). Gradient following without back-propagation in layered networks Proc 1st IEEE Ann Conf Neural Networks. 2

Bastos AM et al. (2012). Canonical microcircuits for predictive coding. Neuron. 76 [PubMed]

Bell AH, Summerfield C, Morin EL, Malecek NJ, Ungerleider LG. (2016). Encoding of Stimulus Probability in Macaque Inferior Temporal Cortex. Current biology : CB. 26 [PubMed]

Bengio Y. (2014). How auto-encoders could provide credit assignment in deep networks via target propagation arXiv:1407.7906.

Bengio Y, Fischer A. (2015). Early inference in energy-based models approxi- mates back-propagation arXiv:1510.02777.

Bengio Y, Larochelle H. (2008). Towards biologically plausible deep learning Proceedings of the 25th International Conference on Machine Learning.

Bengio Y, Scellier B. (2016). Towards a biologically plausible backprop arXiv:1602.05179.

Bogacz R. (2017). A tutorial on the free-energy framework for modelling perception and learning Journal of Mathematical Psychology.

Bogacz R, Markowska-Kaczmar U, Kozik A. (1999). Blinking artefact recognition in eeg signal using artificial neural network Proceedings of 4th Conference on Neural Networks and Their Applications, Zakopane (Poland).

Bogacz R, Martin Moraud E, Abdi A, Magill PJ, Baufreton J. (2016). Properties of Neurons in External Globus Pallidus Can Support Optimal Action Selection. PLoS computational biology. 12 [PubMed]

Bogacz R, Whittington C. (2015). Learning in cortical networks through error back-propagation bioRxiv.

Brown MW, Aggleton JP. (2001). Recognition memory: what are the roles of the perirhinal cortex and hippocampus? Nature reviews. Neuroscience. 2 [PubMed]

Buhmann J, Balduzzi D, Vanchinathan H. (2014). Kickback cuts back- prop's red-tape: biologically plausible credit assignment in neural networks arXiv:1411.6191v1.

Crick F. (1989). The recent excitement about neural networks. Nature. 337 [PubMed]

Dayan P, Hinton GE, Neal RM, Zemel RS. (1995). The Helmholtz machine. Neural computation. 7 [PubMed]

Feldman H, Friston KJ. (2010). Attention, uncertainty, and free-energy. Frontiers in human neuroscience. 4 [PubMed]

Fiser A et al. (2016). Experience-dependent spatial expectations in mouse visual cortex. Nature neuroscience. 19 [PubMed]

Friston K. (2003). Learning and inference in the brain. Neural networks : the official journal of the International Neural Network Society. 16 [PubMed]

Friston K. (2005). A theory of cortical responses. Philosophical transactions of the Royal Society of London. Series B, Biological sciences. 360 [PubMed]

Friston K. (2010). The free-energy principle: a unified brain theory? Nature reviews. Neuroscience. 11 [PubMed]

Friston KJ, Daunizeau J, Kilner J, Kiebel SJ. (2010). Action and behavior: a free-energy formulation. Biological cybernetics. 102 [PubMed]

Hinton G et al. (2012). Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups Signal Processing Magazine. 29

Hinton GE, Krizhevsky A, Sutskever I. (2012). Imagenet classification with deep convolutional neural networks Advances in Neural Information Processing Systems. 25

Hinton GE, Osindero S, Teh YW. (2006). A fast learning algorithm for deep belief nets. Neural computation. 18 [PubMed]

Hyvarinen A. (1999). Regression using independent component analysis, and its connection to multi-layer perceptrons Proceedings of the 9th International Conference on Artificial Neural Networks, Edinburgh.

Kingma D, Ba J. (2014). Adam: A method for stochastic optimization arXiv:1412.6980.

Lecun Y et al. (1989). Backpropagation applied to handwritten zip code recognition Neural Comput. 1

Li L, Miller EK, Desimone R. (1993). The representation of stimulus familiarity in anterior inferior temporal cortex. Journal of neurophysiology. 69 [PubMed]

Lin Z, Bengio Y, Lee DH, Bornschein J. (2015). Towards biologically plausible deep learning arXiv:1502.04156.

Mazzoni P, Andersen RA, Jordan MI. (1991). A more biologically plausible learning rule for neural networks. Proceedings of the National Academy of Sciences of the United States of America. 88 [PubMed]

McClelland JL, McNaughton BL, O'Reilly RC. (1995). Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory. Psychological review. 102 [PubMed]

Mcclelland JL, Hinton GE. (1988). Learning representations by recirculation Neural Information Processing Systems.

Munakata Y, O'Reilly RC. (2000). Computational explorations in cognitive neuroscience.

O'Reilly RC. (1998). Biologically plausible error-driven learning using local activation differences: The generalized recirculation algorithm. Neural Computation. 8

Plaut DC, McClelland JL, Seidenberg MS, Patterson K. (1996). Understanding normal and impaired word reading: computational principles in quasi-regular domains. Psychological review. 103 [PubMed]

Rao RP, Ballard DH. (1999). Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. Nature neuroscience. 2 [PubMed]

Rumelhart DE, Chauvin Y. (1995). Back propagation: Theory, architectures, and applications.

Rumelhart DE, Durbin R, Chauvin Y, Golden R. (1995). Backpropagation: The basic theory Backpropagation: Theory, Architectures and Applications.

Rumelhart DE, Hinton GE, Williams RJ. (1986). Learning representations by back-propagating errors. Nature. 323

Salakhutdinov R, Srivastava N. (2012). Multimodal learning with deep boltzmann machines NIPS.

Seidenberg MS, McClelland JL. (1989). A distributed, developmental model of word recognition and naming. Psychological review. 96 [PubMed]

Sejnowski TJ, Ackley DH, Hinton GE. (1985). A learning algorithm for Bolzmann machines. Cognitive Sci. 9

Seung HS. (2003). Learning in spiking neural networks by reinforcement of stochastic synaptic transmission. Neuron. 40 [PubMed]

Spratling MW. (2008). Reconciling predictive coding and biased competition models of cortical function. Frontiers in computational neuroscience. 2 [PubMed]

Summerfield C et al. (2006). Predictive codes for forthcoming perception in the frontal cortex. Science (New York, N.Y.). 314 [PubMed]

Summerfield C, Trittschuh EH, Monti JM, Mesulam MM, Egner T. (2008). Neural repetition suppression reflects fulfilled perceptual expectations. Nature neuroscience. 11 [PubMed]

Tweed DB, Akerman CJ, Lillicrap TP, Cownden D. (2014). Random feedback weights support learning in deep neural networks arXiv:1411.0247.

Unnikrishnan KP, Venugopal KP. (1994). Alopex: A correlation-based learning algorithm for feedforward and recurrent neural networks Neural Comput. 6

Werfel J, Xie X, Seung HS. (2005). Learning curves for stochastic gradient descent in linear feedforward networks. Neural computation. 17 [PubMed]

Williams RJ. (1992). Simple statistical gradient-following algorithms for connectionist reinforcement learning Mach Learn. 8

Zmarz P, Keller GB. (2016). Mismatch Receptive Fields in Mouse Visual Cortex. Neuron. 92 [PubMed]

References and models that cite this paper
This website requires cookies and limited processing of your personal data in order to function. By continuing to browse or otherwise use this site, you are agreeing to this use. See our Privacy policy and how to cite and terms of use.