Modeling Pharmacological Clock and Memory Patterns of Interval Timing in a Striatal Beat-Frequency Model with Realistic, Noisy Neurons

Abstract

In most species, the capability of perceiving and using the passage of time in the seconds-to-minutes range (interval timing) is not only accurate but also scalar: errors in time estimation are linearly related to the estimated duration. The ubiquity of scalar timing extends over behavioral, lesion, and pharmacological manipulations. For example, in mammals, dopaminergic drugs induce an immediate, scalar change in the perceived time (clock pattern), whereas cholinergic drugs induce a gradual, scalar change in perceived time (memory pattern). How do these properties emerge from unreliable, noisy neurons firing in the milliseconds range? Neurobiological information relative to the brain circuits involved in interval timing provide support for an striatal beat frequency (SBF) model, in which time is coded by the coincidental activation of striatal spiny neurons by cortical neural oscillators. While biologically plausible, the impracticality of perfect oscillators, or their lack thereof, questions this mechanism in a brain with noisy neurons. We explored the computational mechanisms required for the clock and memory patterns in an SBF model with biophysically realistic and noisy Morris–Lecar neurons (SBF–ML). Under the assumption that dopaminergic drugs modulate the firing frequency of cortical oscillators, and that cholinergic drugs modulate the memory representation of the criterion time, we show that our SBF–ML model can reproduce the pharmacological clock and memory patterns observed in the literature. Numerical results also indicate that parameter variability (noise) – which is ubiquitous in the form of small fluctuations in the intrinsic frequencies of neural oscillators within and between trials, and in the errors in recording/retrieving stored information related to criterion time – seems to be critical for the time-scale invariance of the clock and memory patterns.

Clock Speed as a Window into Dopaminergic Control of Emotion and Time Perception

“…The MSNs are able to detect these patterns, which are similar to musical cords, by acting as coincidence detectors or 'perceptrons' (e.g., . Striatal output travels to the thalamus along two pathways: the direct (dopamine D1 receptor mediated) and indirect (dopamine D2 receptor mediated), then loops back to the cortex and striatum, influencing the rate of oscillatory activity and permitting alterations in clock speed by changing the input to MSNs (Oprisan & Buhusi, 2011). Differential activity in the direct and indirect pathways of the basal ganglia may serve to start, stop (pause), or reset the timing process .…”

Interval timing under a behavioral microscope: Dissociating motivational and timing processes in fixed-interval performance

“…Interval timing is the entrainment of an animal's behavior to a target periodicity in the environment, on the basis of an endogenous time-keeping mechanism (Buhusi & Meck, 2005;Oprisan & Buhusi, 2011). This process is often studied using fixed interval (FI) schedules of reinforcement.…”

“…For instance, the behavioral theory of timing suggests that transitions between behavioral states constitute pulses (Killeen & Fetterman, 1988;Machado 1997), whereas the multiple timescales theory of timing suggests that the clock is a form of memory decay (Staddon, 2005;Staddon, Chelaru, & Higa, 2002;Staddon & Higa, 1999). More recently, theorists have attempted to ground this basic mechanism into biologically plausible neural networks (Karmarkar & Buonomano, 2007;Oprisan & Buhusi, 2011) and in drift diffusion models that appear to approximate neuronal dynamics (Simen, Balci, Desouza, Cohen, & Holmes, 2011;Simen, Rivest, Ludvig, Balcı, & Killeen, 2013). The challenge for these theories is to account for a variety of classic properties of interval timing.…”

GAMIT – A Fading-Gaussian Activation Model of Interval-Timing: Unifying Prospective and Retrospective Time Estimation

“…This required globally varying oscillators to retain a significant degree of correlation with each other even as they drift out of synchrony. Recent work by Buhusi and Oprisan (e.g., Buhusi & Oprisan 2013;Oprisan & Buhusi, 2011) addresses this issue using more realistic, noisy neural oscillators and validates the initial approach of Matell and Meck (2004) which as subsequently been extended to include an unified account of duration-based and beat-based timing mechanisms (e.g., Allman et al, 2014;Teki et al, 2012).…”

“…Working memory, timing and attention all depend on dopaminergic pathways (Cools & D'Esposito, 2011;Lake & Meck, 2013;Meck et al, 2012a). The changes observed in interval timing estimates following pharmacological interventions that modulate clock speed (Coull et al, 2011;Meck, 1996) have been modeled by letting dopamine levels affect oscillator frequency (e.g., Allman & Meck, 2012;Buhusi & Oprisan, 2013;Oprisan & Buhusi, 2011). Nevertheless, none of these models can account for the increase in retrospective estimates under high cognitive load.…”