Testing the state-dependent model of subsecond time perception against experimental evidence

Abstract

Coordinated movements, speech and other actions are impossible without precise timing. Realistic computational models of interval timing in the mammalian brain are expected to provide key insights into the underlying mechanisms of timing. Existing computational models of time perception have only been partially replicating experimental observations, such as the linear increase of time, the dopaminergic modulation of this increase, and the scalar property, i.e., the linear increase of the standard deviation of temporal estimates. In this work, we incorporate the state-dependent computational model, which encodes time in the dynamic evolution of network states without the need for a specific network structure into a biologically plausible prefrontal cortex (PFC) model based on in vivo and in vitro recordings of rodents. Specifically, we stimulated 1000 neurons in the beginning and in the end of a range of different time intervals, extracted states of neurons and trained the readout layer based on these states using least squares to predict the respective inter stimulus interval. We show that the naturally occurring heterogeneity in cellular and synaptic parameters in the PFC is sufficient to encode time over several hundreds of milliseconds. The readout faithfully represents the duration between two stimuli applied to the superficial layers of the network, thus fulfilling the requirement of a linear encoding of time. A simulated activation of the D2 dopamine receptor leads to an overestimation and an inactivation to an underestimation of time, in line with experimental results. Furthermore, we show that the scalar property holds true for intervals of several hundred milliseconds, and provide a mechanistic explanation for the origin of the scalar property as well as its deviations. We conclude that this model can represent durations up to 750 ms in a biophysically plausible setting, compatible with experimental findings in this regime.

Autobiographical memory in contact tracing: evidence from the COVID-19 pandemic

“…We all have lives full of social encounters and places that we go to in our daily routines, and sometimes people tend to omit details about a specific event because one unconsciously prioritizes some information to the detriment of others (Mather and Sutherland, 2011), and therefore fails to recognize how relevant they may be for CT. Another issue is related to our ability to report specific details about an event. For example, one may report a visit to a shop and the interactions with other people without being able to recall how long it lasted (Grondin, 2010;Ravichandran-Schmidt and Hass, 2022) or the names of the people encountered (Cohen, 1990;Fawcett and Hulbert, 2020). In terms of CT this is a severe limitation as it makes it impossible to estimate exposure to infection and to trace potentially infected individuals.…”