dc.contributor.author | Arend, Luke | |
dc.contributor.author | Han, Yena | |
dc.contributor.author | Schrimpf, Martin | |
dc.contributor.author | Bashivan, Pouya | |
dc.contributor.author | Kar, Kohitij | |
dc.contributor.author | Poggio, Tomaso | |
dc.contributor.author | DiCarlo, James J. | |
dc.contributor.author | Boix, Xavier | |
dc.date.accessioned | 2018-11-02T18:33:39Z | |
dc.date.available | 2018-11-02T18:33:39Z | |
dc.date.issued | 2018-11-02 | |
dc.identifier.uri | http://hdl.handle.net/1721.1/118847 | |
dc.description.abstract | Deep neural networks have been shown to predict neural responses in higher visual cortex. The mapping from the model to a neuron in the brain occurs through a linear combination of many units in the model, leaving open the question of whether there also exists a correspondence at the level of individual neurons. Here we show that there exist many one-to-one mappings between single units in a deep neural network model and neurons in the brain. We show that this correspondence at the single- unit level is ubiquitous among state-of-the-art deep neural networks, and grows more pronounced for models with higher performance on a large-scale visual recognition task. Comparing matched populations—in the brain and in a model—we demonstrate a further correspondence at the level of the population code: stimulus category can be partially decoded from real neural responses using a classifier trained purely on a matched population of artificial units in a model. This provides a new point of investigation for phenomena which require fine-grained mappings between deep neural networks and the brain. | en_US |
dc.description.sponsorship | This material is based upon work supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF-1231216. | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | Center for Brains, Minds and Machines (CBMM) | en_US |
dc.relation.ispartofseries | CBMM Memo Series;093 | |
dc.title | Single units in a deep neural network functionally correspond with neurons in the brain: preliminary results | en_US |
dc.type | Technical Report | en_US |
dc.type | Working Paper | en_US |
dc.type | Other | en_US |