메뉴 건너뛰기




Volumn 29, Issue 5, 2017, Pages 1229-1262

An approximation of the error backpropagation algorithm in a predictive coding network with local hebbian synaptic plasticity

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION ALGORITHMS; BACKPROPAGATION; BIOINFORMATICS; COMPLEX NETWORKS; ERRORS; LEARNING ALGORITHMS; NETWORK CODING;

EID: 85017439802     PISSN: 08997667     EISSN: 1530888X     Source Type: Journal    
DOI: 10.1162/NECO_a_00949     Document Type: Letter
Times cited : (297)

References (53)
  • 9
    • 84949908580 scopus 로고    scopus 로고
    • A tutorial on the free-energy framework for modelling perception and learning
    • Bogacz, R. (2017). A tutorial on the free-energy framework for modelling perception and learning. Journal of Mathematical Psychology, 76, 198-211.
    • (2017) Journal of Mathematical Psychology , vol.76 , pp. 198-211
    • Bogacz, R.1
  • 11
    • 84980047553 scopus 로고    scopus 로고
    • Properties of neurons in external globus pallidus can support optimal action selection
    • Bogacz, R., Moraud, E. M., Abdi, A., Magill, P. J., & Baufreton, J. (2016). Properties of neurons in external globus pallidus can support optimal action selection. PLoS Comput. Biol., 12(7), e1005004.
    • (2016) PLoS Comput. Biol. , vol.12 , Issue.7
    • Bogacz, R.1    Moraud, E.M.2    Abdi, A.3    Magill, P.J.4    Baufreton, J.5
  • 12
    • 0035228401 scopus 로고    scopus 로고
    • Recognition memory: What are the roles of the perirhinal cortex and hippocampus?
    • Brown, M. W., & Aggleton, J. P. (2001). Recognition memory: What are the roles of the perirhinal cortex and hippocampus? Nature Reviews Neuroscience, 2(1), 51-61.
    • (2001) Nature Reviews Neuroscience , vol.2 , Issue.1 , pp. 51-61
    • Brown, M.W.1    Aggleton, J.P.2
  • 14
    • 0024490816 scopus 로고
    • The recent excitement about neural networks
    • Crick, F. (1989). The recent excitement about neural networks. Nature, 337, 129-132.
    • (1989) Nature , vol.337 , pp. 129-132
    • Crick, F.1
  • 19
    • 0242577959 scopus 로고    scopus 로고
    • Learning and inference in the brain
    • Friston,K. (2003). Learning and inference in the brain. Neural Networks, 16, 1325-1352.
    • (2003) Neural Networks , vol.16 , pp. 1325-1352
    • Friston, K.1
  • 21
    • 75549090229 scopus 로고    scopus 로고
    • The free-energy principle: A unified brain theory?
    • Friston, K. (2010). The free-energy principle: A unified brain theory? Nature Reviews Neuroscience, 11, 127-138.
    • (2010) Nature Reviews Neuroscience , vol.11 , pp. 127-138
    • Friston, K.1
  • 23
    • 85032751458 scopus 로고    scopus 로고
    • Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
    • Hinton, G., Deng, L., Yu, D., Dahl, G., Mohamed, A., Jaitly, N., Kingsbury, B. (2012). Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Processing Magazine, 29, 82- 97.
    • (2012) IEEE Signal Processing Magazine , vol.29 , pp. 82- 97
    • Hinton, G.1    Deng, L.2    Yu, D.3    Dahl, G.4    Mohamed, A.5    Jaitly, N.6    Kingsbury, B.7
  • 24
    • 0001504852 scopus 로고
    • Learning representations by recirculation
    • In D. Z. Anderson (Ed.), New York: American Institute of Physics.
    • Hinton, G. E., & McClelland, J. L. (1988). Learning representations by recirculation. In D. Z. Anderson (Ed.), Neural information processing systems (pp. 358-366). New York: American Institute of Physics.
    • (1988) Neural information processing systems , pp. 358-366
    • Hinton, G.E.1    McClelland, J.L.2
  • 25
    • 33745805403 scopus 로고    scopus 로고
    • A fast learning algorithm for deep belief nets
    • Hinton, G. E., Osindero, S., Teh, Y.-W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18(7), 1527-1554.
    • (2006) Neural Computation , vol.18 , Issue.7 , pp. 1527-1554
    • Hinton, G.E.1    Osindero, S.2    Teh, Y.-W.3
  • 26
    • 0033341919 scopus 로고    scopus 로고
    • Regression using independent component analysis, and its connection to multi-layer perceptrons
    • Stevenage, UK: IEE.
    • Hyvarinen, A. (1999). Regression using independent component analysis, and its connection to multi-layer perceptrons. In Proceedings of the 9th International Conference on Artificial Neural Networks (pp. 491-496). Stevenage, UK: IEE.
    • (1999) Proceedings of the 9th International Conference on Artificial Neural Networks , pp. 491-496
    • Hyvarinen, A.1
  • 28
    • 84876231242 scopus 로고    scopus 로고
    • Imagenet classification with deep convolutional neural networks
    • In F. Pereira, C. Burges, L. Bottou, & K. Weinberger (Eds.). Red Hook, NY: Curran.
    • Krizhevsky, A., Sutskever, I., Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In F. Pereira, C. Burges, L. Bottou, & K. Weinberger (Eds.), Advances in neural information processing systems, 25 (pp. 1097- 1105). Red Hook, NY: Curran.
    • (2012) Advances in neural information processing systems , vol.25 , pp. 1097-1105
    • Krizhevsky, A.1    Sutskever, I.2    Hinton, G.E.3
  • 31
    • 84994417427 scopus 로고    scopus 로고
    • Random synaptic feedback weights support error backpropagation for deep learning
    • Lillicrap, T. P., Cownden, D., Tweed, D. B., & Akerman, C. J. (2016). Random synaptic feedback weights support error backpropagation for deep learning. Nature Communications, 7, 13276.
    • (2016) Nature Communications , vol.7 , pp. 13276
    • Lillicrap, T.P.1    Cownden, D.2    Tweed, D.B.3    Akerman, C.J.4
  • 32
    • 0025735983 scopus 로고
    • A more biologically plausibile learning rule for neural networks
    • Mazzoni, P., Andersen, R. A., & Jordan, M. I. (1991). A more biologically plausibile learning rule for neural networks. Proc. Natl. Acad. Sci. USA, 88, 4433-4437.
    • (1991) Proc. Natl. Acad. Sci. USA , vol.88 , pp. 4433-4437
    • Mazzoni, P.1    Andersen, R.A.2    Jordan, M.I.3
  • 33
    • 0029340352 scopus 로고
    • Why there are complementary learning systems in the hippocampus and neocortex: Insights from the successes and failures of connectionist models of learning and memory
    • McClelland, J. L., McNaughton, B. L., & O'Reilly, R. C. (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, 419-457.
    • (1995) Psychological Review , vol.102 , pp. 419-457
    • McClelland, J.L.1    McNaughton, B.L.2    O'Reilly, R.C.3
  • 34
    • 0027192354 scopus 로고
    • The representation of stimulus familiarity in anterior inferior temporal cortex
    • Miller, L. L., & Desimone, R. (1993). The representation of stimulus familiarity in anterior inferior temporal cortex. Journal of Neurophysiology, 69(6), 1918-1929.
    • (1993) Journal of Neurophysiology , vol.69 , Issue.6 , pp. 1918-1929
    • Miller, L.L.1    Desimone, R.2
  • 35
    • 84884143038 scopus 로고    scopus 로고
    • Preconfigured, skewed distribution of firing rates in the hippocampus and entorhinal cortex
    • Mizuseki, K., Buzśaki, G. (2013). Preconfigured, skewed distribution of firing rates in the hippocampus and entorhinal cortex. Cell Reports, 4(5), 1010-1021.
    • (2013) Cell Reports , vol.4 , Issue.5 , pp. 1010-1021
    • Mizuseki, K.1    Buzśaki, G.2
  • 36
    • 0001569746 scopus 로고    scopus 로고
    • Biologically plausible error-driven learning using local activation differences: The generalized recirculation algorithm
    • O'Reilly, R. C. (1998). Biologically plausible error-driven learning using local activation differences: The generalized recirculation algorithm. Neural Computation, 8, 895-938.
    • (1998) Neural Computation , vol.8 , pp. 895-938
    • O'Reilly, R.C.1
  • 38
    • 0029691655 scopus 로고    scopus 로고
    • Understanding normal and impaired word reading: Computational principles in quasiregular domains
    • Plaut, D. C., McClelland, J. L., Seidenberg, M. S., & Patterson, K. (1996). Understanding normal and impaired word reading: Computational principles in quasiregular domains. Psychological Review, 103, 56-115.
    • (1996) Psychological Review , vol.103 , pp. 56-115
    • Plaut, D.C.1    McClelland, J.L.2    Seidenberg, M.S.3    Patterson, K.4
  • 39
    • 0033360288 scopus 로고    scopus 로고
    • Predictive coding in the visual cortex: A functional interpretation of some extra-classical receptive-field effects
    • Rao, R. P. N., & Ballard, D. H. (1999). Predictive coding in the visual cortex: A functional interpretation of some extra-classical receptive-field effects. Nature Neuroscience, 2, 79-87.
    • (1999) Nature Neuroscience , vol.2 , pp. 79-87
    • Rao, R.P.N.1    Ballard, D.H.2
  • 41
    • 0022471098 scopus 로고
    • Learning representations by back-propagating errors
    • Rumelhart, D. E., Hinton, G. E., Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533-536.
    • (1986) Nature , vol.323 , pp. 533-536
    • Rumelhart, D.E.1    Hinton, G.E.2    Williams, R.J.3
  • 43
    • 0024747015 scopus 로고
    • A distributed, developmental model of word recognition and naming
    • Seidenberg, M. S., McClelland, J. L. (1989). A distributed, developmental model of word recognition and naming. Psychological Review, 96, 523-568.
    • (1989) Psychological Review , vol.96 , pp. 523-568
    • Seidenberg, M.S.1    McClelland, J.L.2
  • 44
    • 0347362917 scopus 로고    scopus 로고
    • Learning in spiking neural networks by reinforcement of stochastic synaptic transmission
    • Seung, H. S. (2003). Learning in spiking neural networks by reinforcement of stochastic synaptic transmission. Neuron, 40, 1063-1073.
    • (2003) Neuron , vol.40 , pp. 1063-1073
    • Seung, H.S.1
  • 45
    • 69749088676 scopus 로고    scopus 로고
    • Reconciling predictive coding and biased competition models of cortical function
    • Spratling, M.W. (2008). Reconciling predictive coding and biased competition models of cortical function. Frontiers in Computational Neuroscience, 2, 4.
    • (2008) Frontiers in Computational Neuroscience , vol.2 , pp. 4
    • Spratling, M.W.1
  • 46
    • 84877724347 scopus 로고    scopus 로고
    • Multimodal learning with deep boltzmann machines
    • In F. Pereira, C. J. C. Burges, L. Bottou, & K. Q. Weinberger (Eds.), Red Hook, NY: Curran.
    • Srivastava, N., Salakhutdinov, R. (2012). Multimodal learning with deep boltzmann machines. In F. Pereira, C. J. C. Burges, L. Bottou, & K. Q. Weinberger (Eds.), Advances in neural information processing systems, 25 (pp. 2222-2230). Red Hook, NY: Curran.
    • (2012) Advances in neural information processing systems , vol.25 , pp. 2222-2230
    • Srivastava, N.1    Salakhutdinov, R.2
  • 47
    • 33845194013 scopus 로고    scopus 로고
    • Predictive codes for forthcoming perception in the frontal cortex
    • Summerfield, C., Egner, T., Greene, M., Koechlin, E., Mangels, J., & Hirsch, J. (2006). Predictive codes for forthcoming perception in the frontal cortex. Science, 314, 1311-1314.
    • (2006) Science , vol.314 , pp. 1311-1314
    • Summerfield, C.1    Egner, T.2    Greene, M.3    Koechlin, E.4    Mangels, J.5    Hirsch, J.6
  • 49
    • 0001682375 scopus 로고
    • Alopex: A correlation-based learning algorithm for feedforward and recurrent neural networks
    • Unnikrishnan, K., & Venugopal, K. (1994). Alopex: A correlation-based learning algorithm for feedforward and recurrent neural networks. Neural Computation, 6, 469-490.
    • (1994) Neural Computation , vol.6 , pp. 469-490
    • Unnikrishnan, K.1    Venugopal, K.2
  • 50
    • 27144462270 scopus 로고    scopus 로고
    • Learning curves for stochastic gradient descent in linear feedforward networks
    • Werfel, J., Xiew, X., & Seung, H. S. (2005). Learning curves for stochastic gradient descent in linear feedforward networks. Neural Computation, 17, 2699-2718.
    • (2005) Neural Computation , vol.17 , pp. 2699-2718
    • Werfel, J.1    Xiew, X.2    Seung, H.S.3
  • 52
    • 0000337576 scopus 로고
    • Simple statistical gradient-following algorithms for connectionist reinforcement learning
    • Williams, R. J. (1992). Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning, 8, 229-256.
    • (1992) Machine Learning , vol.8 , pp. 229-256
    • Williams, R.J.1
  • 53
    • 84997191933 scopus 로고    scopus 로고
    • Mismatch receptive fields in mouse visual cortex
    • Zmarz, P., Keller, G. B. (2016). Mismatch receptive fields in mouse visual cortex. Neuron, 92(4), 766-772.
    • (2016) Neuron , vol.92 , Issue.4 , pp. 766-772
    • Zmarz, P.1    Keller, G.B.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.