Biology subjects

Lupkin, S. M.

Publications and source records attributed to Lupkin, S. M..

5 recordsLinked to original sources

bioRxiv2026

Learning to select computations in recurrent neural circuits

Two hallmarks of biological computation are its flexibility and efficiency. These features are often attributed to cognitive control processes that balance external utility against computational cost. However, how the brain could implement such adaptive control remains unknown. Here, we provide one possible answer by combining the computational theory of rational meta-reasoning with a meta-learning algorithm recently proposed as a model of prefrontal cortex. This yields a recurrent neural network model that learns to select computations. In simple choice tasks, the model approximates the algorithms and representations of optimal symbolic models and reproduces neural dynamics observed in macaque orbitofrontal cortex. In multi-step planning tasks, the model replicates key behavioral signatures of human planning strategies and captures human neural dynamics associated with step-by-step mental simulation. Our framework unifies meta-reasoning and meta-learning by showing that learning to reason can be understood as learning to learn from information generated by ones own cognitive operations, providing a mechanistic account of how adaptive control of thought can be implemented in neural systems.

neuroscience↗

bioRxiv2026

A Neural Population Code for Value in Human Orbitofrontal Cortex.

Adaptive behavior depends on the ability to rapidly evaluate options and select those that promise the greatest benefit. Such decisions rely on neural representations of value distributed across multiple brain regions, including the orbitofrontal (OFC) and ventromedial prefrontal cortex (vmPFC), yet the neural code underlying these value representations remains unresolved. The dominant account proposes that OFC/vmPFC neurons encode value through a linear rate code, resulting in a single point estimate at the population level. However, this framework is difficult to reconcile with the heterogeneous tuning observed in individual OFC/vmPFC neurons, which can exhibit both positive and negative correlations with subjective value. Here, we test the alternative hypothesis--derived from theories of neural coding in perceptual systems-- that the OFC/vmPFC implements a probabilistic population code based on non-linear tuning functions. Such tuning allows population activity to represent not only subjective value but also the uncertainty surrounding it, in the form of a flexible posterior probability distribution. Using a population receptive field framework, we fitted non-linear value-tuning functions to functional magnetic resonance imaging data acquired during a value judgment task. Bayesian inversion of this encoding model enabled robust out-of-sample decoding of subjective value across several brain regions, including the OFC/vmPFC. Importantly, value uncertainty estimated from decoded medial OFC/vmPFC posteriors predicted within-subject preference instability, choice stochasticity, and confidence in option values, demonstrating its behavioral relevance and suggesting that participants had conscious access to this information. Complementary single-unit recordings from a subset of monkey OFC neurons similarly revealed nonlinear value-selective tuning. Together, these findings establish a probabilistic, non-linear population code for value in the OFC/vmPFC. This provides a neural foundation for the probabilistic code through which value, and uncertainty about value, can guide choice.

neuroscience↗

bioRxiv2024

Orbitofrontal high-gamma reflects spike-dissociable value and decision mechanisms

The orbitofrontal cortex (OFC) plays a crucial role in value-based decision-making. While previous research has focused on spiking activity in OFC neurons, the role of OFC local field potentials (LFPs) in decision-making remains unclear. LFPs are important because they can reflect synaptic and subthreshold activity not directly coupled to spiking, and because they are potential targets for less invasive forms of brain-machine interface (BMI). We recorded LFPs and spiking activity using multi-channel vertical probes while monkeys performed a two-option value-based decision-making task. We compared the value- and decision-coding properties of high-gamma range LFPs (HG, 50-150 Hz) to the coding properties of spiking multi-unit activity (MUA) recorded concurrently on the same electrodes. Results show that HG and MUA both represent the values of decision targets, and that their representations have similar temporal profiles in a trial. However, we also identified value-coding properties of HG that were dissociable from the concurrently-measured MUA. On average across channels, HG amplitude increased monotonically with value, whereas the average value encoding in MUA was net neutral. HG also encoded a signal consistent with a comparison between the values of the two targets, a signal which was much weaker in MUA. In individual channels, HG was better able to predict choice outcomes than MUA; however, when simultaneously recorded channels were combined in population-based decoder, MUA provided more accurate predictions than HG. Interestingly, HG value representations were accentuated in channels in or near shallow cortical layers, suggesting a dissociation between neuronal sources of HG and MUA. In summary, we find that HG signals are dissociable from MUA with respect to cognitive variables encoded in prefrontal cortex, evident in the monotonic encoding of value, stronger encoding of value comparisons, and more accurate predictions about behavior. High-frequency LFPs may therefore be a viable - or even preferable - target for BMIs to assist cognitive function, opening the possibility for less invasive access to mental contents that would otherwise be observable only with spike-based measures.

neuroscience↗

bioRxiv2022

Monkeys exhibit human-like gaze biases in economic decisions

In economic decision-making individuals choose between items based on their perceived value. For both humans and nonhuman primates, these decisions are often carried out while shifting gaze between the available options. Recent studies in humans suggest that these shifts in gaze actively influence choice, manifesting as a bias in favor of the items that are viewed first, viewed last, or viewed for the overall longest duration in a given trial. This suggests a mechanism that links gaze behavior to the neural computations underlying value-based choices. In order to identify this mechanism, it is first necessary to develop and validate a suitable animal model of this behavior. To this end, we have created a novel value-based choice task for macaque monkeys that captures the essential features of the human paradigms in which gaze biases have been observed. Using this task, we identified gaze biases in the monkeys that were both qualitatively and quantitatively similar to those in humans. In addition, the monkeys gaze biases were well-explained using a sequential sampling model framework previously used to describe gaze biases in humans--the first time this framework has been used to assess value-based decision mechanisms in nonhuman primates. Together, these findings suggest a common mechanism that can explain gaze-related choice biases across species, and open the way for mechanistic studies to identify the neural origins of this behavior.

neuroscience↗

bioRxiv2021

Value signals in orbitofrontal cortex predict economic decisions on a trial-to-trial basis

The primate orbitofrontal cortex (OFC) has long been recognized for its role in value-based decisions; however, the exact mechanism linking OFC value representations to decision outcomes has remained elusive. To address this question we show for the first time that trial-wise variability in choices can be explained by variability in value signals decoded from many simultaneously recorded OFC neurons. Mechanistically, this relationship is consistent with the projection of activity within a low-dimensional value-encoding subspace onto a potentially higher-dimensional, behaviorally-potent output subspace.

neuroscience↗