Gaby Maimon Research: Brain Circuits Shaping Decision-Making

Understanding how the brain makes decisions is a fundamental question in neuroscience. Gaby Maimon’s research explores the neural circuits that determine when and why we commit to a choice, particularly through mechanisms that accumulate evidence over time before reaching a decision threshold.

By examining these processes at the level of individual neurons and broader networks, this work sheds light on how value-based judgments emerge in the brain.

The Concept Of A Rise-To-Threshold Mechanism

Decision-making in the brain often follows a rise-to-threshold mechanism, where neural activity builds until it reaches a level that triggers a commitment to a choice. This process is crucial in deliberative situations where sensory or cognitive inputs are integrated before action is taken. Maimon’s research has revealed that specific neurons exhibit ramping activity, reflecting the progressive weighting of evidence. These findings align with established theories like the drift-diffusion model, which describes decision formation as a stochastic process where neural signals accumulate until a threshold is met.

This mechanism is not uniform across all decisions but is influenced by stimulus strength, prior expectations, and internal states. Experiments in Drosophila, a key model in Maimon’s work, have shown that neurons in the central complex display graded increases in firing rates as sensory evidence builds. This gradual rise suggests that choices are not abrupt but result from a dynamic process where competing signals vie for dominance. The threshold itself is flexible, adjusting based on context—lowering when urgency demands quick action and rising when uncertainty requires prolonged deliberation.

Electrophysiological recordings have shown that neuromodulators like dopamine and serotonin influence accumulation rates and threshold levels. Dopaminergic signaling adjusts the gain of evidence accumulation, altering decision speed, while serotonin is linked to patience and delayed decision thresholds. Similar patterns have been observed in primates and humans, where fluctuations in dopamine correlate with variations in decision timing and confidence.

Core Brain Circuits Driving Relative-Value Decisions

The brain weighs competing options through specialized neural circuits that integrate relative value information. Maimon’s research has examined how these circuits function at the level of individual neurons and broader networks, particularly in value-based decision-making. Studies in invertebrates and vertebrates indicate that distinct brain regions dynamically adjust based on internal priorities and external stimuli.

In Drosophila, the central complex plays a key role in assigning value to competing options. Neurons in this structure display activity patterns that correlate with the perceived benefits of a choice, encoding relative value before commitment. Similar functions are observed in mammals, where the prefrontal cortex and basal ganglia contribute to relative-value computations. The orbitofrontal cortex represents the subjective worth of choices, modulating downstream areas that execute decisions. Electrophysiological recordings in primates show that neurons in this region fire in proportion to expected rewards. Meanwhile, the striatum integrates this information to facilitate action selection, with dopaminergic inputs refining responses based on past experiences and motivational states.

Neuromodulatory systems also shape relative-value judgments. Dopamine encodes reward prediction errors, reinforcing advantageous choices while discouraging suboptimal ones. Serotonin influences patience and delayed gratification, modulating circuits to favor long-term benefits. In Drosophila, serotonergic neurons projecting to the central complex adjust decision thresholds based on environmental stability, demonstrating a conserved role for this neurotransmitter across species.

Experimental Techniques To Measure Threshold Dynamics

Understanding how neural activity accumulates before reaching a decision threshold requires precise experimental methods. Researchers use electrophysiological recordings, optogenetic manipulations, and computational modeling to track decision processes in real time.

In vivo electrophysiology directly measures neuronal firing rates in behaving organisms, revealing how activity ramps up as evidence is integrated. Studies in Drosophila have shown that neurons in the central complex exhibit gradual increases in firing, mirroring the accumulation of sensory or cognitive inputs.

Optogenetics provides finer control over threshold-related circuits by selectively activating or inhibiting neurons with light-sensitive ion channels. Experiments using this technique have demonstrated that increasing neural excitability can prematurely trigger a decision, while suppressing activity delays commitment. This has also allowed researchers to examine how neuromodulators like dopamine and serotonin shift the threshold under different conditions.

Computational modeling further enhances our understanding by simulating stochastic evidence accumulation. Drift-diffusion models mathematically represent how noisy sensory inputs integrate over time until a decision boundary is reached. By fitting these models to empirical data, researchers infer parameters such as accumulation rates, neural variability, and threshold levels. Additionally, closed-loop behavioral paradigms, where feedback from neural activity dynamically adjusts task parameters, have revealed how urgency and uncertainty influence decision timing.

Interplay Of Sensory Inputs And Time-Based Accumulation

The brain continuously processes sensory information, filtering relevant signals from background noise to guide decisions. Sensory inputs vary in influence depending on intensity, novelty, and consistency. Weak or ambiguous stimuli require prolonged accumulation, while strong signals expedite commitment. Studies in Drosophila show that neurons in the central complex adjust firing rates in response to fluctuating sensory inputs, highlighting the dynamic interplay between external cues and internal accumulation mechanisms.

Time-based accumulation depends on both sensory reliability and decision urgency. In stable environments, extended deliberation improves accuracy. In time-sensitive situations, faster integration occurs, often at the expense of precision. Research in primates has shown that neurons in the lateral intraparietal cortex accelerate ramping activity when rapid responses are required, demonstrating the brain’s adaptability in modulating accumulation rates based on context.

Behavioral Studies Linking Threshold Shifts And Valuation

Decision thresholds fluctuate based on expected rewards, perceived risks, and prior experiences, balancing speed and accuracy. When an option has a higher payoff, the threshold for committing to that choice lowers, enabling quicker decisions. Conversely, when uncertainty is high or potential losses are significant, the threshold rises, leading to more deliberation.

Studies in humans and animal models illustrate these shifts in real-world scenarios. In primates performing reward-based tasks, neurons in prefrontal and striatal circuits adjust thresholds in response to changing expected values. When rewards are more immediate or substantial, neural ramping activity steepens, accelerating accumulation. When rewards are delayed or uncertain, accumulation slows, reflecting a more cautious approach. Similar patterns appear in Drosophila, where dopaminergic modulation influences sensory information integration before a decision is made. These findings reinforce the idea that decision-making is an active process shaped by motivational and contextual factors.

Implications For Complex Decision-Making

As decisions grow more complex, the interaction between evidence accumulation, valuation, and threshold dynamics becomes increasingly critical. High-stakes choices—such as those in medicine, finance, or strategy—require weighing multiple variables over time. The brain’s ability to adjust thresholds ensures flexibility, balancing timely action with accuracy.

Understanding these mechanisms has clinical applications, especially in conditions like Parkinson’s disease or obsessive-compulsive disorder, where decision-making processes are impaired. Patients with these conditions often struggle with maladaptive threshold settings, either committing prematurely or becoming trapped in prolonged deliberation.

Beyond clinical relevance, insights from threshold dynamics research have informed artificial intelligence and machine learning models designed to mimic human decision-making. By incorporating principles of evidence accumulation and adaptive threshold modulation, these systems improve efficiency in probabilistic reasoning tasks. In robotics and autonomous systems, algorithms that adjust decision thresholds based on environmental uncertainty enhance performance in dynamic settings.