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Files:: A Scalable Population Code for Time in the Striatum - 08.06.22.md; Mello et al_2015_A Scalable Population Code for Time in the Striatum.pdf; ScienceDirect Snapshot

Reading Note:: Mello, Soares, Paton 2015

A Scalable Population Code for Time in the Striatum

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Abstract

To guide behavior and learn from its consequences, the brain must represent time over many scales. Yet, the neural signals used to encode time in the seconds-to-minute range are not known. The striatum is a major input area of the basal ganglia associated with learning and motor function. Previous studies have also shown that the striatum is necessary for normal timing behavior. To address how striatal signals might be involved in timing, we recorded from striatal neurons in rats performing an interval timing task. We found that neurons fired at delays spanning tens of seconds and that this pattern of responding reflected the interaction between time and the animals’ ongoing sensorimotor state. Surprisingly, cells rescaled responses in time when intervals changed, indicating that striatal populations encoded relative time. Moreover, time estimates decoded from activity predicted timing behavior as animals adjusted to new intervals, and disrupting striatal function led to a decrease in timing performance. These results suggest that striatal activity forms a scalable population code for time, providing timing signals that animals use to guide their actions.

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A Scalable Population Code for Time in the Striatum

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Annotations(6/18/2022, 6:44:58 PM)

“Highlights d Striatal neurons fire at different times over tens of seconds during timing behavior d Response times of striatal neurons rescaled with the interval being timed d Time coding by the population predicted timing behavior from trial to trial d Striatal neurons multiplexed information about action and time” (p. 1112) “In Brief Time is fundamental for all behavior, yet how the brain encodes time is unknown. Mello, Soares, and Paton found that firing dynamics in populations of neurons in the rodent striatum robustly and flexibly encoded time over tens of seconds. These results supply new insight into how the basal ganglia might function during learning and action selection.” (p. 1112) “Multiple lines of evidence implicate the basal ganglia (BG) as a locus for the representation of such temporal information. Lesions of the striatum in rats [2], disease states that affect the BG such as Parkinson’s [3] and Huntington’s disease [4], drugs that affect dopamine (DA) signaling [5], and genetic manipulations that affect the DA system in the BG [6] all result in interval timing dysfunction. Furthermore, human fMRI studies have found that the striatum, a main input area of the BG, is activated by tasks that involve the processing of interval information [7, 8].” (p. 1113) “Strikingly, we found that temporal tuning stretched or contracted, rescaling with the interval being timed. Thus, striatal populations encoded relative time, flexibly adapting to the immediate demands of the environment. Finally, we ran a simple simulation of the task and show that neural responses resembling those we observe in the striatum are suitable as a basis for timing behavior.” (p. 1113)

==“Fig. 1”== (p. 1114)

“mental Experimental Procedures for details) shown in Figure 2A. Some cells fired just after reward delivery, others fired in the middle of the delay, and others fired leading up to the next reward (Figures 2A, S2, and S3). This produced a slow-moving ‘‘bump’’ of activity that traversed the population during each FI.” (p. 1115)

“In theory, reading out the location of this bump in the population could provide an estimate of time within the FI. However, a core feature of interval timing behavior is that timing accuracy decreases with the magnitude of the interval being timed [9]” (p. 1115)

==“Fig. 2”== (p. 1115)

Transclude of A-Scalable-Population-Code-for-Time-in-the-Striatum---08.06.22#^zlh7qj

“Based on previous studies [18–20], we expected that striatal neurons would display significant modulation by behaviors during the FI” (p. 1116)

==“Fig. 1”== (p. 1114)

==“Fig. 2”== (p. 1115)

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