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@varellaModelPeakintervalTask2019

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A model for the peak-interval task based on neural oscillation-delimited states

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Abstract

Specific mechanisms underlying how the brain keeps track of time are largely unknown. Several existing computational models of timing reproduce behavioral results obtained with experimental psychophysical tasks, but only a few tackle the underlying biological mechanisms, such as the synchronized neural activity that occurs throughout brain areas. In this paper, we introduce a model for the peak-interval task based on neuronal network properties. We consider that Local Field Potential (LFP) oscillation cycles specify a sequence of states, represented as neuronal ensembles. Repeated presentation of time intervals during training reinforces the connections of specific ensembles to downstream networks — sets of neurons connected to the sequence of states. Later, during the peak-interval procedure, these downstream networks are reactivated by previously experienced neuronal ensembles, triggering behavioral responses at the learned time intervals. The model reproduces experimental response patterns from individual rats in the peak-interval procedure, satisfying relevant properties such as the Weber law. Finally, we provide a biological interpretation of the parameters of the model.

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Tasks

Topics

“Keyword” Scalar Expectancy Theory, SET (Gibbon, 1977; Grondin, 2014). SET is a cognitive process model which assumes the existence of a pacemaker which emits pulses at a certain rate that are stored by an accumulator. The temporal estimation comes from counting the number of pulses in the accumulator and comparing it to a number of pulses previously stored in the reference memory (i.e., past experiences). (p. 1)

Further Reading

  • Hardy, N.F., Buonomano, D.V., 2018. Encoding time in feedforward trajectories of a recurrent neural network model. Neural Comput. 30, 378–396. (p. 8) rd p5 🛫 <% tp.date.now(“YYYY-MM-DD”) %>

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Extracted Annotations and Comments

How the brain represents the passage of time and uses this information in time-related tasks is still an ongoing debate in the scientific community. There seems to be different mechanisms involved in temporal processing depending on the time scale (p. 1)

Figures

Figure 1**

(p. 2) “Keyword” drift-diffusion models (Luzardo et al., 2017) (p. 1) Fig. 1. (a) Experimental setup of a typical Peak-Interval Procedure during a Fixed-Interval trial. A time-dependent light will be used as a stimulus and at the first nose-poke after 20 s the light will turn off and the food will drop at the food pellet. During a peak-interval, the light will keep turned on and the food will not drop. (b) Summary of the nose-pokes registered during each peak-interval trial with a target time of 20 s and the distribution containing the mean number of nose-pokes during each second of a trial. (p. 2)

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