publication . Conference object . Other literature type . Contribution for newspaper or weekly magazine . Article . Preprint . 2018

No title available

Molano-Mazon, Manuel; Onken, Arno; Piasini, Eugenio; Panzeri, Stefano;
Open Access
  • Published: 02 May 2018
  • Country: United Kingdom
Abstract
Comment: Published as a conference paper at ICLR 2018 V2: minor changes in supp. material
Subjects
arXiv: Quantitative Biology::Neurons and CognitionCondensed Matter::Materials Science
free text keywords: Neuroscience, Biological Sciences not elsewhere classified, Mathematical Sciences not elsewhere classified, Information Systems not elsewhere classified, Generative Adversarial Networks, neuron, dataset, systems neuroscience, higher-order statistics, salamander retina, population activity patterns, spike trains, population recordings, Wasserstein-GAN variant, Spike-GAN, framework, spike train, information processing, alternative approaches, GANs, spike train analysis, Quantitative Biology - Neurons and Cognition, Computer Science - Neural and Evolutionary Computing
Funded by
EC| ETIC
Project
ETIC
Encoding and Transmission of Information in the Mouse Somatosensory Cortex
  • Funder: European Commission (EC)
  • Project Code: 699829
  • Funding stream: H2020 | MSCA-IF-EF-ST
,
EC| STOMMAC
Project
STOMMAC
Stochastic Multi-Scale Modelling for the Analysis of Closed-Loop Interactions among Brain Networks
  • Funder: European Commission (EC)
  • Project Code: 659227
  • Funding stream: H2020 | MSCA-IF-EF-ST
FigShare
Conference object . 2018
Provider: FigShare
Edinburgh Research Explorer
Contribution for newspaper or weekly magazine . 2018
Zenodo
Other literature type . 2018
Provider: Datacite
ZENODO
Conference object . 2018
Provider: ZENODO
44 references, page 1 of 3

Alireza Alemi-Neissi, Federica Bianca Rosselli, and Davide Zoccolan. Multifeatural shape processing in rats engaged in invariant visual object recognition. Journal of Neuroscience, 33(14): 5939-5956, 2013. [OpenAIRE]

Takafumi Arakaki, Gregory Barello, and Yashar Ahmadian. Capturing the diversity of biological tuning curves using generative adversarial networks. arXiv preprint arXiv:1707.04582, 2017. [OpenAIRE]

Martin Arjovsky, Soumith Chintala, and Le´on Bottou. arXiv:1701.07875, 2017.

Natasha A. Cayco-Gajic, Joel Zylberberg, and Eric Shea-Brown. Triplet correlations among similarly tuned cells impact population coding. Frontiers in Computational Neuroscience, 9: 57, 2015. ISSN 1662-5188. doi: 10.3389/fncom.2015.00057. URL http://journal. frontiersin.org/article/10.3389/fncom.2015.00057. [OpenAIRE]

Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel. Infogan: Interpretable representation learning by information maximizing generative adversarial nets. CoRR, abs/1606.03657, 2016. URL http://arxiv.org/abs/1606.03657.

Soumith Chintala, Emily Denton, Martin Arjovsky, and Michael Mathieu. How to train a GAN? Tips and tricks to make GANs work. GitHub, 2016.

Mark M Churchland, M Yu Byron, Maneesh Sahani, and Krishna V Shenoy. Techniques for extracting single-trial activity patterns from large-scale neural recordings. Current Opinion in Neurobiology, 17(5):609-618, 2007.

Valentina Emiliani, Adam E Cohen, Karl Deisseroth, and Michael Ha¨usser. All-optical interrogation of neural circuits. Journal of Neuroscience, 35(41):13917-13926, 2015.

Pascal Fries, John H Reynolds, Alan E Rorie, and Robert Desimone. Modulation of oscillatory neuronal synchronization by selective visual attention. Science, 291(5508):1560-1563, 2001.

Wulfram Gerstner and Werner M Kistler. Spiking neuron models: Single neurons, populations, plasticity. Cambridge University Press, 2002. URL http://icwww.epfl.ch/˜gerstner/ BUCH.html. [OpenAIRE]

Ian Goodfellow. NIPS 2016 tutorial: arXiv:1701.00160, 2016.

Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. In Advances in Neural Information Processing Systems, pp. 2672-2680, 2014.

Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron Courville. Improved training of Wasserstein GANs. arXiv preprint arXiv:1704.00028, 2017.

Robin AA Ince, Stefano Panzeri, and Christoph Kayser. Neural codes formed by small and temporally precise populations in auditory cortex. Journal of Neuroscience, 33(46):18277-18287, 2013. [OpenAIRE]

Justin Keat, Pamela Reinagel, R Clay Reid, and Markus Meister. Predicting every spike: a model for the responses of visual neurons. Neuron, 30(3):803-817, 2001. [OpenAIRE]

44 references, page 1 of 3
Abstract
Comment: Published as a conference paper at ICLR 2018 V2: minor changes in supp. material
Subjects
arXiv: Quantitative Biology::Neurons and CognitionCondensed Matter::Materials Science
free text keywords: Neuroscience, Biological Sciences not elsewhere classified, Mathematical Sciences not elsewhere classified, Information Systems not elsewhere classified, Generative Adversarial Networks, neuron, dataset, systems neuroscience, higher-order statistics, salamander retina, population activity patterns, spike trains, population recordings, Wasserstein-GAN variant, Spike-GAN, framework, spike train, information processing, alternative approaches, GANs, spike train analysis, Quantitative Biology - Neurons and Cognition, Computer Science - Neural and Evolutionary Computing
Funded by
EC| ETIC
Project
ETIC
Encoding and Transmission of Information in the Mouse Somatosensory Cortex
  • Funder: European Commission (EC)
  • Project Code: 699829
  • Funding stream: H2020 | MSCA-IF-EF-ST
,
EC| STOMMAC
Project
STOMMAC
Stochastic Multi-Scale Modelling for the Analysis of Closed-Loop Interactions among Brain Networks
  • Funder: European Commission (EC)
  • Project Code: 659227
  • Funding stream: H2020 | MSCA-IF-EF-ST
FigShare
Conference object . 2018
Provider: FigShare
Edinburgh Research Explorer
Contribution for newspaper or weekly magazine . 2018
Zenodo
Other literature type . 2018
Provider: Datacite
ZENODO
Conference object . 2018
Provider: ZENODO
44 references, page 1 of 3

Alireza Alemi-Neissi, Federica Bianca Rosselli, and Davide Zoccolan. Multifeatural shape processing in rats engaged in invariant visual object recognition. Journal of Neuroscience, 33(14): 5939-5956, 2013. [OpenAIRE]

Takafumi Arakaki, Gregory Barello, and Yashar Ahmadian. Capturing the diversity of biological tuning curves using generative adversarial networks. arXiv preprint arXiv:1707.04582, 2017. [OpenAIRE]

Martin Arjovsky, Soumith Chintala, and Le´on Bottou. arXiv:1701.07875, 2017.

Natasha A. Cayco-Gajic, Joel Zylberberg, and Eric Shea-Brown. Triplet correlations among similarly tuned cells impact population coding. Frontiers in Computational Neuroscience, 9: 57, 2015. ISSN 1662-5188. doi: 10.3389/fncom.2015.00057. URL http://journal. frontiersin.org/article/10.3389/fncom.2015.00057. [OpenAIRE]

Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel. Infogan: Interpretable representation learning by information maximizing generative adversarial nets. CoRR, abs/1606.03657, 2016. URL http://arxiv.org/abs/1606.03657.

Soumith Chintala, Emily Denton, Martin Arjovsky, and Michael Mathieu. How to train a GAN? Tips and tricks to make GANs work. GitHub, 2016.

Mark M Churchland, M Yu Byron, Maneesh Sahani, and Krishna V Shenoy. Techniques for extracting single-trial activity patterns from large-scale neural recordings. Current Opinion in Neurobiology, 17(5):609-618, 2007.

Valentina Emiliani, Adam E Cohen, Karl Deisseroth, and Michael Ha¨usser. All-optical interrogation of neural circuits. Journal of Neuroscience, 35(41):13917-13926, 2015.

Pascal Fries, John H Reynolds, Alan E Rorie, and Robert Desimone. Modulation of oscillatory neuronal synchronization by selective visual attention. Science, 291(5508):1560-1563, 2001.

Wulfram Gerstner and Werner M Kistler. Spiking neuron models: Single neurons, populations, plasticity. Cambridge University Press, 2002. URL http://icwww.epfl.ch/˜gerstner/ BUCH.html. [OpenAIRE]

Ian Goodfellow. NIPS 2016 tutorial: arXiv:1701.00160, 2016.

Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial nets. In Advances in Neural Information Processing Systems, pp. 2672-2680, 2014.

Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron Courville. Improved training of Wasserstein GANs. arXiv preprint arXiv:1704.00028, 2017.

Robin AA Ince, Stefano Panzeri, and Christoph Kayser. Neural codes formed by small and temporally precise populations in auditory cortex. Journal of Neuroscience, 33(46):18277-18287, 2013. [OpenAIRE]

Justin Keat, Pamela Reinagel, R Clay Reid, and Markus Meister. Predicting every spike: a model for the responses of visual neurons. Neuron, 30(3):803-817, 2001. [OpenAIRE]

44 references, page 1 of 3
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