Aghamohammadi, C. | bioRxiv Science

bioRxiv2026

Working memory limitations and dopamine modulation in probabilistic reasoning

Difficult decisions require gathering evidence over extended periods, placing demands on working memory. Yet, how working memory limitations affect decision-making remains largely unknown. We trained two macaque monkeys to perform a probabilistic reasoning task that involved extended sequential evidence sampling, requiring reliance on working memory. Monkeys made choices informed by a stream of briefly presented cues, each providing probabilistic evidence about which choice would be rewarded. In both animals, choices were significantly affected by working memory decay, primacy, recency, and priming. Despite individual differences in working memory limitations, both monkeys adopted sampling strategies that made their behavior nearly optimal. To test how dopamine affects working memory constraints on evidence accumulation, we systemically applied dopamine D1 receptor agonist and antagonist drugs midway during selected sessions. Activation of D1 receptors reduced priming. Blockade of D1 receptors reduced working memory decay and the subjective evidence weights assigned to individual cues. Our results reveal that complex decisions are constrained by working memory limitations and identify dopamine as a key modulator of this process, with potential implications for cognitive disorders and their treatment.

neuroscience↗

bioRxiv2024

A doubly stochastic renewal framework for partitioning spiking variability

The firing rate is a prevalent concept used to describe neural computations, but estimating dynamically changing firing rates from irregular spikes is challenging. An inhomogeneous Poisson process, the standard model for partitioning firing rate and spiking irregularity, cannot account for diverse spike statistics observed across neurons. We introduce a doubly stochastic renewal point process, a flexible mathematical framework for partitioning spiking variability, which captures the broad spectrum of spiking irregularity from periodic to super-Poisson. We validate our partitioning framework using intracellular voltage recordings and develop a method for estimating spiking irregularity from data. We find that the spiking irregularity of cortical neurons decreases from sensory to association areas and is nearly constant for each neuron under many conditions but can also change across task epochs. A spiking network model shows that spiking irregularity depends on connectivity and can change with external input. These results help improve the precision of estimating firing rates on single trials and constrain mechanistic models of neural circuits.

neuroscience↗