Ultra-FDst Object Recognition from Few Spikes
MASSACHUSETTS INST OF TECH CAMBRIDGE MA CENTER FOR BIOLOGICAL AND COMPUTATIONAL LEARNING
Pagination or Media Count:
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 readout 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.
- Humanities and History
- Medicine and Medical Research
- Operations Research