Distributed coding of stimulus magnitude in rodent prefrontal cortex

Abstract

As we interact with the external world, we judge magnitudes from sensory information. The estimation of magnitudes has been characterized in primates, yet it is largely unexplored in non-primate species. Here, we show that gerbils that solve a time-interval reproduction task display primate-like magnitude estimation characteristics, most prominently a systematic overestimation of small stimuli and an underestimation of large stimuli, often referred to as regression effect. We investigated the underlying neural mechanisms by recording from medial prefrontal cortex and show that the majority of neurons respond either during the measurement or the reproduction of a time-interval. Cells that are active during both phases display distinct response patterns. We categorize the neural responses into multiple types and demonstrate that only populations with mixed responses can encode the bias of the regression effect. These results reveal the organizing neural principles of an important higher cognitive function.Animals including humans estimate the magnitude of physical stimuli, integrate path length and keep track of duration to gather behaviorally relevant information from their environment. Although such estimates may ultimately be used for binary actions, like discriminating items or events and making decisions, the estimation itself is done on a continuum of values. Behavioral analyses over the past century established specific biases in magnitude estimation [e.g. reviewed in 1] such as the regression effect, i.e. the overestimation of small and the underestimation of large stimuli across a range of values. Recently, this bias regained attention as it may be the result of an error minimization strategy [2][3][4].Despite a long history of behavioral research on magnitude estimation, its neural basis is not well understood. It is an ongoing debate whether a dedicated or distributed 1

A precise and adaptive neural mechanism for predictive temporal processing in the frontal cortex

“…Scaling during motor planning may also play a role in predicting the sensory consequences of actions, which is thought to be an integral part of motor control ( 114 ). Our findings thus point to a unifying functional explanation of the phenomenon of temporal scaling commonly observed in timing tasks across species and brain areas ( 43, 48, 55, 65–69, 75 ).…”

“…Juxtaposing our finding relating temporal scaling to the mean of an interval distribution with previous work reporting temporal scaling during time interval discrimination ( 55, 65, 109 ) and production ( 28, 29, 47, 48, 56, 69, 73, 75, 110, 111 ) raises the question of whether these phenomena are computationally related. We propose that all these observations may be unified under the theory of predictive processing, and the only thing that differentiate between them is the nature of what is being predicted.…”

“…Although the phenomenon of temporal scaling is not new ( 47, 48, 51, 54, 55, 65–72, 74, 75 ), its functional implication is not understood. Different studies have offered an interpretation in terms of attention ( 76 ), anticipation ( 77, 78 ), or reward expectation ( 71, 79 ).…”

Ramping cells in rodent mPFC encode time to past and future events via real Laplace transform

“…1 on the manifold defined by the low-rank recurrence matrix Henke, et al, (2021). On each trial, a gerbil experiences a target stimulus duration, and then reproduces the duration by running on a treadmill.…”

“…On each trial, a gerbil experiences a target stimulus duration, and then reproduces the duration by running on a treadmill. Here we limit our analysis to the neural activity during the reproduction phase (adapted from Henke, et al, 2021) The peak times of time cells smoothly tile the delay interval; as the sequence advances time cells fire for progressively longer durations (Kraus, Robinson, White, Eichenbaum, & Hasselmo, 2013;Cao, Bladon, Charczynski, Hasselmo, & Howard, 2022). Time cells thus exhibit a continuous distribution of characteristic time constants.…”