The representation of multiple objects in prefrontal neuronal delay activity.
The ability to retain multiple items in short-term memory is fundamental for intelligent behavior, yet little is known about its neural basis. To explore the mechanisms underlying this ability, we trained 2 monkeys to remember a sequence of 2 objects across a short delay. We then recorded the activity of neurons from the lateral prefrontal cortex during task performance and found that most neurons had activity that depended on the identity of both objects while a minority reflected just one object. Further, the activity driven by a particular combination of objects was not a simple addition of the activity elicited by individual objects. Instead, the representation of the first object was altered by the addition of the second object to memory, and the form of this change was not systematically predictable. These results indicate that multiple objects are not stored in separate groups of prefrontal neurons. Rather, they are represented by a single population of neurons in a complex fashion. We also found that the strength of the memory trace associated with each object decayed over time, leading to a relatively stronger representation of more recently seen objects. This is a potential mechanism for representing the temporal order of objects.
Warden
Melissa R
MR
The Picower Institute for Learning and Memory, RIKEN-MIT Neuroscience Research Center, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Miller
Earl K
EK
D000465:Algorithms; D000704:Analysis of Variance; D000818:Animals; D003216:Conditioning, Operant / Q000502:physiology; D003463:Cues; D003627:Data Interpretation, Statistical; D004567:Electrodes, Implanted; D004594:Electrophysiology; D005260:Female; D005403:Fixation, Ocular; D008253:Macaca mulatta; D008297:Male; D008570:Memory, Short-Term / Q000502:physiology; D009474:Neurons / Q000502:physiology*; D010775:Photic Stimulation; D017397:Prefrontal Cortex / Q000166:cytology / Q000502:physiology*; D012044:Regression Analysis; D012691:Serial Learning / Q000502:physiology