Parallel processing of working memory and temporal information by distinct types of cortical projection neurons

Abstract

It is unclear how different types of cortical projection neurons work together to support diverse cortical functions. We examined the discharge characteristics and inactivation effects of intratelencephalic (IT) and pyramidal tract (PT) neurons–two major types of cortical excitatory neurons that project to cortical and subcortical structures, respectively–in the medial prefrontal cortex of mice performing a delayed response task. We found that IT neurons, but not PT neurons, convey significant working memory–related signals. We also found that the inactivation of IT neurons, but not PT neurons, impairs behavioral performance. In contrast, PT neurons convey far more temporal information than IT neurons during the delay period. Our results indicate a division of labor between IT and PT projection neurons in the prefrontal cortex for the maintenance of working memory and for tracking the passage of time, respectively.

Multiplexing working memory and time: encoding retrospective and prospective information in neural trajectories

“…As stated above, either low-dimensional ramping activity or high-dimensional neural sequences can encode both WM and time. Furthermore, both types of dynamics have been observed experimentally during WM or timing tasks 12,[28][29][30][31][32][33][35][36][37][38][39] . To date, ramping activity and neural sequences have been treated as fundamentally different dynamic regimes to encode WM or time 3,9,11,45 .…”

“…Conversely, low dimensional ramping activity has also been observed across areas and tasks [35][36][37][38][39][68][69][70] . Furthermore, even within a single brain area different classes of excitatory neurons may differ in the degree to which they encode time and WM 37 . The diversity and complexity of the experimental findings make it challenging to develop area-specific computational models.…”

“…A large body of neurophysiological data across brain areas has revealed a multitude of neural signatures during WM and timing tasks, including neural sequences 12,[28][29][30][31][32][33][34] , and firing rate ramps [35][36][37][38][39] . Artificial neural networks, and RNNs in particular, have been invaluable in capturing the experimentally observed dynamics and elucidating the dynamic regimes capable of storing WM and encoding time 11,[40][41][42][43] , but to date, with some exceptions 11 , these attempts have primarily focused on either WM or timing tasks.…”

“…Neural sequences and high-dimensional activity have been observed across many brain areas during working memory and timing tasks 12,[28][29][30][31][32][33]61,[65][66][67] . Conversely, low dimensional ramping activity has also been observed across areas and tasks [35][36][37][38][39][68][69][70] . Furthermore, even within a single brain area different classes of excitatory neurons may differ in the degree to which they encode time and WM 37 .…”