{"extern":"1","type":"conference","date_updated":"2021-01-12T08:17:03Z","volume":30,"language":[{"iso":"eng"}],"publication_identifier":{"issn":["10495258"]},"intvolume":"        30","quality_controlled":"1","title":"Cortical microcircuits as gated-recurrent neural networks","_id":"8129","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/1711.02448"}],"author":[{"last_name":"Costa","first_name":"Rui Ponte","full_name":"Costa, Rui Ponte"},{"full_name":"Assael, Yannis M.","first_name":"Yannis M.","last_name":"Assael"},{"full_name":"Shillingford, Brendan","first_name":"Brendan","last_name":"Shillingford"},{"last_name":"Freitas","full_name":"Freitas, Nando de","first_name":"Nando de"},{"last_name":"Vogels","id":"CB6FF8D2-008F-11EA-8E08-2637E6697425","full_name":"Vogels, Tim P","first_name":"Tim P","orcid":"0000-0003-3295-6181"}],"conference":{"name":"NIPS: Neural Information Processing System","location":"Long Beach, CA, United States","start_date":"2017-12-04","end_date":"2017-12-09"},"abstract":[{"lang":"eng","text":"Cortical circuits exhibit intricate recurrent architectures that are remarkably similar across different brain areas. Such stereotyped structure suggests the existence of common computational principles. However, such principles have remained largely elusive. Inspired by gated-memory networks, namely long short-term memory networks (LSTMs), we introduce a recurrent neural network in which information is gated through inhibitory cells that are subtractive (subLSTM). We propose a natural mapping of subLSTMs onto known canonical excitatory-inhibitory cortical microcircuits. Our empirical evaluation across sequential image classification and language modelling tasks shows that subLSTM units can achieve similar performance to LSTM units. These results suggest that cortical circuits can be optimised to solve complex contextual problems and proposes a novel view on their computational function.\r\nOverall our work provides a step towards unifying recurrent networks as used in machine learning with their biological counterparts."}],"oa_version":"Preprint","publication":"Advances in Neural Information Processing Systems","publication_status":"published","article_processing_charge":"No","month":"12","date_created":"2020-07-16T19:13:10Z","citation":{"apa":"Costa, R. P., Assael, Y. M., Shillingford, B., Freitas, N. de, &#38; Vogels, T. P. (2017). Cortical microcircuits as gated-recurrent neural networks. In <i>Advances in Neural Information Processing Systems</i> (Vol. 30, pp. 272–283). Long Beach, CA, United States: Neural Information Processing Systems Foundation.","chicago":"Costa, Rui Ponte, Yannis M. Assael, Brendan Shillingford, Nando de Freitas, and Tim P Vogels. “Cortical Microcircuits as Gated-Recurrent Neural Networks.” In <i>Advances in Neural Information Processing Systems</i>, 30:272–83. Neural Information Processing Systems Foundation, 2017.","ista":"Costa RP, Assael YM, Shillingford B, Freitas N de, Vogels TP. 2017. Cortical microcircuits as gated-recurrent neural networks. Advances in Neural Information Processing Systems. NIPS: Neural Information Processing System vol. 30, 272–283.","ieee":"R. P. Costa, Y. M. Assael, B. Shillingford, N. de Freitas, and T. P. Vogels, “Cortical microcircuits as gated-recurrent neural networks,” in <i>Advances in Neural Information Processing Systems</i>, Long Beach, CA, United States, 2017, vol. 30, pp. 272–283.","short":"R.P. Costa, Y.M. Assael, B. Shillingford, N. de Freitas, T.P. Vogels, in:, Advances in Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2017, pp. 272–283.","ama":"Costa RP, Assael YM, Shillingford B, Freitas N de, Vogels TP. Cortical microcircuits as gated-recurrent neural networks. In: <i>Advances in Neural Information Processing Systems</i>. Vol 30. Neural Information Processing Systems Foundation; 2017:272-283.","mla":"Costa, Rui Ponte, et al. “Cortical Microcircuits as Gated-Recurrent Neural Networks.” <i>Advances in Neural Information Processing Systems</i>, vol. 30, Neural Information Processing Systems Foundation, 2017, pp. 272–83."},"day":"01","external_id":{"arxiv":["1711.02448"]},"publisher":"Neural Information Processing Systems Foundation","status":"public","year":"2017","page":"272-283","oa":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_published":"2017-12-01T00:00:00Z"}