A model of interval timing by neural integration
By Patrick Simen, F. Balcı, Laura deSouza, J. Cohen, Philip J. Holmes. Journal of Neuroscience, 2011
307 citations recorded by the source corpus.
We show that simple assumptions about neural processing lead to a model of interval timing as a temporal integration process, in which a noisy firing-rate representation of time rises linearly on average toward a response threshold over the course of an interval. Our assumptions include: that neural spike trains are approximately independent Poisson processes, that correlations among them can be largely cancelled by balancing excitation and inhibition, that neural populations can act as integrators, and that the objective of timed behavior is maximal accuracy and minimal variance. The model accounts for a variety of physiological and behavioral findings in rodents, monkeys, and humans, including ramping firing rates between the onset of reward-predicting cues and the receipt of delayed rewards, and universally scale-invariant response time distributions in interval timing tasks. It furthermore makes specific, well-supported predictions about the skewness of these distributions, a feature of timing data that is usually ignored. The model also incorporates a rapid (potentially one-shot) duration-learning procedure. Human behavioral data support the learning rule's predictions regarding learning speed in sequences of timed responses. These results suggest that simple, integration-based models should play as prominent a role in interval timing theory as they do in theories of perceptual decision making, and that a common neural mechanism may underlie both types of behavior.
Subfields: Cognitive Neuroscience
View Paper: https://www.semanticscholar.org/paper/090788a314507dab4b6ec0e6b4e15b1e343b80d0
How researchers see this paper
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In Cognitive Neuroscience
- Joseph J. Paton
- Dean V. Buonomano
- Simon L. Grondin
- Mehrdad Jazayeri
- Mark S. Goldman
- Andreas Elpidorou
Citations in the corpus
- ROBUST TIMING AND MOTOR PATTERNS BY TAMING CHAOS IN RECURRENT NEURAL NETWORKS (2012, unknown): “We estimated the local LLE (λ, also known as finite-time LLE) in a manner similar to that described previously28 with (6)”
- The Neural Basis of Timing: Distributed Mechanisms for Diverse Functions (2018, methodology): “Most ramping models, however, rely on network mechanisms in which positive feedback is implemented via recurrent excitatory connections (Reutimann et al., 2004; Gavornik et al., 2009; Simen et al., 2011; Lim and Goldman, 2013).”