A precise and adaptive neural mechanism for predictive temporal processing in the frontal cortex

Abstract

The theory of predictive processing posits that the nervous system uses expectations to process information predictively. Direct empirical evidence in support of this theory however has been scarce and largely limited to sensory areas. Here, we report a precise and adaptive neural mechanism in the frontal cortex of non-human primates consistent with predictive processing of temporal events. We found that the speed at which neural states evolve over time is inversely proportional to the statistical mean of the temporal distribution of an expected stimulus. This lawful relationship was evident across multiple experiments and held true during learning: when temporal statistics underwent covert changes, neural responses underwent predictable changes that reflected the new mean. Together, these results highlight a precise mathematical relationship between temporal statistics in the environment and neural activity in the frontal cortex that could serve as a mechanistic foundation for predictive temporal processing.

Prediction-error neurons in circuits with multiple neuron types: Formation, refinement and functional implications

“…A stimulus drawn from a random distribution is perceived larger when it is smaller than the mean of the stimulus distribution, and perceived smaller when it is larger than the mean. This well-known phenomenon has been described centuries ago (51, 52), reproduced many times in tasks that involve the reproduction of a perceived variable (24–29), and attributed to Bayesian computation in which a system integrates information about prior stimulus statistics (25, 26). We speculated PE neurons can support biased perception.…”

“…Furthermore, we show that the formation of PE neurons depends on synaptic noise, the distribution of actual and predicted sensory inputs, and the initial connectivity between neurons. Finally, we connect a heterogeneous PE circuit with an attractor network and show that PE neurons can support biased perception, a phenomenon observed in tasks that involve the reproduction of a perceived variable (24–29). By means of the example of a contraction bias, we illustrate a number of functional implications for PE neurons.…”

Riding the elephant in the room: Towards a revival of the optimal level of stimulation model

“…Current research suggests that expectations or predictions happen when the brain bias the patterns of neuronal activity in the way that is most likely to be appropriate when encountering the stimulus (Meirhaeghe et al, 2021). Such biased brain activity would likely make new information processing more fluent, resulting in an intrinsically rewarding effect (in line with the hedonic fluency model mentioned before).…”

Temporal encoding: Relative and absolute representations of time guide behavior.

“…Previous research on the neural basis of temporal encoding also provided evidence consistent with the existence of temporally specific neurons in the striatum by demonstrating that neurons show distinct firing patterns for individual durations (Matell et al, 2003). Furthermore, other studies show that only a subset of neurons involved in timing rescaled (e.g., Meirhaeghe et al, 2021; Mello et al, 2015; Shimbo et al, 2021), which indicates that a proportion of neurons possibly also encodes absolute temporal information (Motanis & Buonomano, 2015).…”