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Object Selectivity of Local Field Potentials and Spikes in the Macaque Inferior Temporal Cortex

Gabriel Kreiman, Chou Hung, Alexander Kraskov, Rodrigo Quiroga, Tomaso Poggio & James J. DiCarlo

Although the inferior temporal cortex (IT) is important for visual object recognition and IT neuronal spike responses are selective for complex visual shapes [1,2], we know little about how objects are represented across the IT population or the spatial organization of that selectivity.  Because local field potentials (LFPs) are thought to constitute a weighted average of the spiking and non-spiking activity of many neurons [3,5], they might provide insight into the spatial organization of IT object selectivity.

To examine LFPs in IT and their relationship to spiking activity, we recorded multi-unit activity (MUA) and LFPs at 364 sites across IT of two macaque monkeys while they passively viewed a fixed set of 77 complex objects.  Strikingly, the LFPs showed significant stimulus selectivity at 44% of the recording sites.  However, we found little similarity in the MUA-determined and the LFP-determined object preferences at most sites.  By examining the similarity of selectivity at pairs of spatially separate sites, we further observed that MUA selectivity was similar on a spatial scale of up to 800 microns [see also 4] while the LFP selectivity was similar on a spatial scale of at least several mm.

The selectivity of LFPs suggests some spatial organization of feature selectivity in the temporal lobe, but LFP-related and spiking-related measures likely report different aspects of that organization.


This report describes research done at the Center for Biological & Computational Learning, which is in the McGovern Institute for Brain Research at MIT, as well as in the Dept. of Brain & Cognitive Sciences, and which is affiliated with the Computer Sciences & Artificial Intelligence Laboratory (CSAIL).

This research was sponsored by grants from: Office of Naval Research (DARPA) Contract No. MDA972-04-1-0037, Office of Naval Research (DARPA) Contract No. N00014-02-1-0915, National Science Foundation (ITR/SYS) Contract No. IIS-0112991, National Science Foundation (ITR) Contract No. IIS-0209289, National Science Foundation-NIH (CRCNS) Contract No. EIA-0218693, National Science Foundation-NIH (CRCNS) Contract No. EIA-0218506, and National Institutes of Health (Conte) Contract No. 1 P20 MH66239-01A1.

Additional support was provided by: Central Research Institute of Electric Power Industry (CRIEPI), Daimler-Chrysler AG, Compaq/Digital Equipment Corporation, Eastman Kodak Company, Honda R&D Co., Ltd., Industrial Technology Research Institute (ITRI), Komatsu Ltd., Eugene McDermott Foundation, Merrill-Lynch, NEC Fund, Oxygen, Siemens Corporate Research, Inc., Sony, Sumitomo Metal Industries, and Toyota Motor Corporation.


[1] Logothetis, N.K. & Sheinberg, D.L.  “Visual Object Recognition,” Annual Review of Neuroscience 19, 577-621 (1996).

[2] Tanaka, K.  “Neuronal Mechanism of Object Recognition,” Science 262, 685-688 (1993).

[3] Logothetis, N.K. “The Underpinnings of the BOLD Functional Magnetic Resonance Imaging Signal,” J. Neuroscience, 23, 3963-71 (2003).

[4] Fujita, I., Tanaka, K., Ito, M. & Cheng, K. “Columns for Visual Features of Objects in Monkey Inferotemporal Cortex,” Nature, 360, 343-346 (1992).

[5] Mitzdorf, U. “Current Source-density Method and Application in Cat Cerebral Cortex: Investigation of Evoked Potentials and EEG Phenomena,” Physiological Reviews, 65, 37-99 (1985).

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