MIT Libraries logoDSpace@MIT

MIT
View Item 
  • DSpace@MIT Home
  • Computer Science and Artificial Intelligence Lab (CSAIL)
  • CSAIL Digital Archive
  • CSAIL Technical Reports (July 1, 2003 - present)
  • View Item
  • DSpace@MIT Home
  • Computer Science and Artificial Intelligence Lab (CSAIL)
  • CSAIL Digital Archive
  • CSAIL Technical Reports (July 1, 2003 - present)
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Ultra-fast Object Recognition from Few Spikes

Author(s)
Hung, Chou; Kreiman, Gabriel; Poggio, Tomaso; DiCarlo, James J.
Thumbnail
DownloadMIT-CSAIL-TR-2005-045.ps (73.53Mb)
Additional downloads
Metadata
Show full item record
Abstract
Understanding the complex brain computations leading to object recognition requires quantitatively characterizing the information represented in inferior temporal cortex (IT), the highest stage of the primate visual stream. A read-out technique based on a trainable classifier is used to characterize the neural coding of selectivity and invariance at the population level. The activity of very small populations of independently recorded IT neurons (~100 randomly selected cells) over very short time intervals (as small as 12.5 ms) contains surprisingly accurate and robust information about both object ‘identity’ and ‘category’, which is furthermore highly invariant to object position and scale. Significantly, selectivity and invariance are present even for novel objects, indicating that these properties arise from the intrinsic circuitry and do not require object-specific learning. Within the limits of the technique, there is no detectable difference in the latency or temporal resolution of the IT information supporting so-called ‘categorization’ (a.k. basic level) and ‘identification’ (a.k. subordinate level) tasks. Furthermore, where information, in particular information about stimulus location and scale, can also be read-out from the same small population of IT neurons. These results show how it is possible to decode invariant object information rapidly, accurately and robustly from a small population in IT and provide insights into the nature of the neural code for different kinds of object-related information.
Date issued
2005-07-06
URI
http://hdl.handle.net/1721.1/30556
Other identifiers
MIT-CSAIL-TR-2005-045
AIM-2005-022
CBCL-253
Series/Report no.
Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory
Keywords
AI, object recognition, neural coding, inferior temporal cortex

Collections
  • CSAIL Technical Reports (July 1, 2003 - present)

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

My Account

Login

Statistics

OA StatisticsStatistics by CountryStatistics by Department
MIT Libraries
PrivacyPermissionsAccessibilityContact us
MIT
Content created by the MIT Libraries, CC BY-NC unless otherwise noted. Notify us about copyright concerns.