메뉴 건너뛰기




Volumn 1, Issue , 2012, Pages 773-781

Homeostatic plasticity in Bayesian spiking networks a Expectation Maximization with posterior constraints

Author keywords

[No Author keywords available]

Indexed keywords

CORTICAL MICROCIRCUITS; EXPECTATION - MAXIMIZATIONS; MATHEMATICAL FRAMEWORKS; MATHEMATICAL TREATMENTS; NEURAL IMPLEMENTATIONS; NEURONAL ACTIVATION FUNCTION; PROBABILISTIC INFERENCE; SYNAPTIC PLASTICITY RULES;

EID: 84877744982     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (25)

References (25)
  • 1
    • 1642553405 scopus 로고    scopus 로고
    • Bayesian integration in sensorimotor learning
    • K. P. Körding and D. M. Wolpert. Bayesian integration in sensorimotor learning. Nature, 427(6971):244-247, 2004.
    • (2004) Nature , vol.427 , Issue.6971 , pp. 244-247
    • Körding, K.P.1    Wolpert, D.M.2
  • 3
    • 76749113376 scopus 로고    scopus 로고
    • Statistically optimal perception and learning: From behavior to neural representation
    • J. Fiser, P. Berkes, G. Orban, and M. Lengyel. Statistically optimal perception and learning: from behavior to neural representation. Trends in Cogn. Sciences, 14(3):119-130, 2010.
    • (2010) Trends in Cogn. Sciences , vol.14 , Issue.3 , pp. 119-130
    • Fiser, J.1    Berkes, P.2    Orban, G.3    Lengyel, M.4
  • 4
    • 78650972934 scopus 로고    scopus 로고
    • Spontaneous cortical activity reveals hallmarks of an optimal internal model of the environment
    • P. Berkes, G. Orban, M. Lengyel, and J. Fiser. Spontaneous cortical activity reveals hallmarks of an optimal internal model of the environment. Science, 331:83-87, 2011.
    • (2011) Science , vol.331 , pp. 83-87
    • Berkes, P.1    Orban, G.2    Lengyel, M.3    Fiser, J.4
  • 5
    • 33747688922 scopus 로고    scopus 로고
    • Optimal predictions in everyday cognition
    • T. L. Griffiths and J. B. Tenenbaum. Optimal predictions in everyday cognition. Psychological Science, 17(9):767-773, 2006.
    • (2006) Psychological Science , vol.17 , Issue.9 , pp. 767-773
    • Griffiths, T.L.1    Tenenbaum, J.B.2
  • 6
    • 69449087069 scopus 로고    scopus 로고
    • Multisensory integration: Psychophysics, neurophysiology and computation
    • D. E. Angelaki, Y. Gu, and G. C. DeAngelis. Multisensory integration: psychophysics, neurophysiology and computation. Current opinion in neurobiology, 19(4):452-458, 2009.
    • (2009) Current Opinion in Neurobiology , vol.19 , Issue.4 , pp. 452-458
    • Angelaki, D.E.1    Gu, Y.2    DeAngelis, G.C.3
  • 7
    • 37749042762 scopus 로고    scopus 로고
    • Bayesian spiking neurons I: Inference
    • S. Deneve. Bayesian spiking neurons I: Inference. Neural Computation, 20(1):91-117, 2008.
    • (2008) Neural Computation , vol.20 , Issue.1 , pp. 91-117
    • Deneve, S.1
  • 8
    • 70349235964 scopus 로고    scopus 로고
    • Belief propagation in networks of spiking neurons
    • A. Steimer, W. Maass, and R.J. Douglas. Belief propagation in networks of spiking neurons. Neural Computation, 21:2502-2523, 2009.
    • (2009) Neural Computation , vol.21 , pp. 2502-2523
    • Steimer, A.1    Maass, W.2    Douglas, R.J.3
  • 9
    • 81355133300 scopus 로고    scopus 로고
    • Neural dynamics as sampling: A model for stochastic computation in recurrent networks of spiking neurons
    • 11
    • L. Buesing, J. Bill, B. Nessler, and W. Maass. Neural dynamics as sampling: A model for stochastic computation in recurrent networks of spiking neurons. PLoS Comput Biol, 7(11):e1002211, 11 2011.
    • (2011) PLoS Comput Biol , vol.7 , Issue.11
    • Buesing, L.1    Bill, J.2    Nessler, B.3    Maass, W.4
  • 10
    • 84855256984 scopus 로고    scopus 로고
    • Probabilistic inference in general graphical models through sampling in stochastic networks of spiking neurons
    • D. Pecevski, L. Buesing, and W. Maass. Probabilistic inference in general graphical models through sampling in stochastic networks of spiking neurons. PLoS Comput Biol, 7(12), 12 2011.
    • (2011) PLoS Comput Biol , vol.7 , Issue.12 , pp. 12
    • Pecevski, D.1    Buesing, L.2    Maass, W.3
  • 11
    • 37749055358 scopus 로고    scopus 로고
    • Bayesian spiking neurons II: Learning
    • S. Deneve. Bayesian spiking neurons II: Learning. Neural Computation, 20(1):118-145, 2008.
    • (2008) Neural Computation , vol.20 , Issue.1 , pp. 118-145
    • Deneve, S.1
  • 12
    • 79955090327 scopus 로고    scopus 로고
    • STDP enables spiking neurons to detect hidden causes of their inputs
    • MIT Press
    • B. Nessler, M. Pfeiffer, and W. Maass. STDP enables spiking neurons to detect hidden causes of their inputs. In Proc. of NIPS 2009, volume 22, pages 1357-1365. MIT Press, 2010.
    • (2010) Proc. of NIPS 2009 , vol.22 , pp. 1357-1365
    • Nessler, B.1    Pfeiffer, M.2    Maass, W.3
  • 13
    • 85162350170 scopus 로고    scopus 로고
    • Sequence learning with hidden units in spiking neural networks
    • MIT Press
    • J. Brea, W. Senn, and J.-P. Pfister. Sequence learning with hidden units in spiking neural networks. In Proc. of NIPS 2011, volume 24, pages 1422-1430. MIT Press, 2012.
    • (2012) Proc. of NIPS 2011 , vol.24 , pp. 1422-1430
    • Brea, J.1    Senn, W.2    Pfister, J.-P.3
  • 14
    • 85103867881 scopus 로고    scopus 로고
    • Variational learning for recurrent spiking networks
    • MIT Press
    • D. J. Rezende, D. Wierstra, and W. Gerstner. Variational learning for recurrent spiking networks. In Proc. of NIPS 2011, volume 24, pages 136-144. MIT Press, 2012.
    • (2012) Proc. of NIPS 2011 , vol.24 , pp. 136-144
    • Rezende, D.J.1    Wierstra, D.2    Gerstner, W.3
  • 15
    • 84861110370 scopus 로고    scopus 로고
    • Feedforward inhibition and synaptic scaling-two sides of the same coin?
    • C. Keck, C. Savin, and J. Lücke. Feedforward inhibition and synaptic scaling-two sides of the same coin? PLoS Computational Biology, 8(3):e1002432, 2012.
    • (2012) PLoS Computational Biology , vol.8 , Issue.3
    • Keck, C.1    Savin, C.2    Lücke, J.3
  • 16
    • 79952512265 scopus 로고    scopus 로고
    • How to grow a mind: Statistics, structure, and abstraction
    • Joshua B. Tenenbaum, Charles Kemp, Thomas L. Griffiths, and Noah D. Goodman. How to grow a mind: Statistics, structure, and abstraction. Science, 331(6022):1279-1285, 2011.
    • (2011) Science , vol.331 , Issue.6022 , pp. 1279-1285
    • Tenenbaum, J.B.1    Kemp, C.2    Griffiths, T.L.3    Goodman, N.D.4
  • 18
    • 0033360297 scopus 로고    scopus 로고
    • Plasticity in the intrinsic excitability of cortical pyramidal neurons
    • N.S. Desai, L.C. Rutherford, and G.G. Turrigiano. Plasticity in the intrinsic excitability of cortical pyramidal neurons. Nature Neuroscience, 2(6):515, 1999.
    • (1999) Nature Neuroscience , vol.2 , Issue.6 , pp. 515
    • Desai, N.S.1    Rutherford, L.C.2    Turrigiano, G.G.3
  • 19
    • 78649405204 scopus 로고    scopus 로고
    • Homeostatic plasticity and STDP: Keeping a neurons cool in a fluctuating world
    • A. Watt and N. Desai. Homeostatic plasticity and STDP: keeping a neurons cool in a fluctuating world. Frontiers in Synaptic Neuroscience, 2, 2010.
    • Frontiers in Synaptic Neuroscience , vol.2 , pp. 2010
    • Watt, A.1    Desai, N.2
  • 20
    • 85162012703 scopus 로고    scopus 로고
    • Expectation maximization and posterior constraints
    • MIT Press
    • J. Graca, K. Ganchev, and B. Taskar. Expectation maximization and posterior constraints. In Proc. of NIPS 2007, volume 20. MIT Press, 2008.
    • (2008) Proc. of NIPS 2007 , vol.20
    • Graca, J.1    Ganchev, K.2    Taskar, B.3
  • 22
    • 0032031687 scopus 로고    scopus 로고
    • A model of neuronal responses in visual area MT
    • E.P. Simoncelli and D.J. Heeger. A model of neuronal responses in visual area MT. Vision Research, 38(5):743-761, 1998.
    • (1998) Vision Research , vol.38 , Issue.5 , pp. 743-761
    • Simoncelli, E.P.1    Heeger, D.J.2
  • 25
    • 0032203257 scopus 로고    scopus 로고
    • Gradient-based learning applied to document recognition
    • Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. In Proceedings of the IEEE, volume 86, pages 2278-2324, 11 1998.
    • (1998) Proceedings of the IEEE , vol.86 , Issue.11 , pp. 2278-2324
    • LeCun, Y.1    Bottou, L.2    Bengio, Y.3    Haffner, P.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.