Biology subjects

Hocker, D.

Publications and source records attributed to Hocker, D..

4 recordsLinked to original sources

bioRxiv2026

A corticostriatal circuit updates subjective beliefs about latent task states

Beliefs about states of the world profoundly impact decision-making and learning, but little is known about how neural circuits represent and update beliefs. We performed projection-specific recordings and perturbations from neurons in the orbitofrontal cortex (OFC) projecting to the intermediate or rostral cau-date putamen (CPi/CPr) in rats performing a task with hidden reward states. Stimulating OFC[->]CPi neurons biased rats beliefs towards high reward states. Recordings from optogenetically-tagged OFC[->]CPi neurons showed that they encoded categorical evidence for high reward states, shaped by a saturating non-linearity in neural responses. Downstream neurons could, in principle, decode the full belief distribution over reward states as rats deliberated about deci-sions. Finally, projection-specific perturbations disrupted encoding of hidden states within OFC. These findings reveal the circuit implementation of a core cognitive computation, updating subjective beliefs about abstract latent states of the environment.

neuroscience↗

bioRxiv2024

Neural dynamics in the orbitofrontal cortex reveal cognitive strategies

While the orbitofrontal cortex (OFC) is implicated in learning and inferring latent states, the precise computation performed by OFC for state inference is unclear. Here we show that rat OFC updates beliefs about states, and this process is decipherable from OFC dynamics in rats performing state inference, but not alternative strategies. We trained rats to perform a temporal wagering task with hidden reward states. Well-trained rats used state inference when deciding how long to wait for rewards, and OFC inactivations impaired belief updating about states. Electrophysiology and novel population analysis methods identified latent neural factors reflecting inferred states in rats performing inference, but not other strategies. Neural firing rates and latent population factors showed abrupt changes following trials that were informative of state transitions. These results identify a precise computation performed by OFC, and reveal neural signatures of inference.

neuroscience↗

bioRxiv2021

Subpopulations of neurons in lOFC encode previous and current rewards at time of choice

1Studies of neural dynamics in lateral orbitofrontal cortex (lOFC) have shown that subsets of neurons that encode distinct aspects of behavior, such as value, may project to common downstream targets. However, it is unclear whether reward history, which may subserve lOFCs well-documented role in learning, is represented by functional subpopulations in lOFC. Previously, we analyzed neural recordings from rats performing a value-based decision-making task, and we documented trial-by-trial learning that required lOFC (Constantinople et al., 2019). Here we characterize functional subpopulations of lOFC neurons during behavior, including their encoding of task variables. We found five distinct clusters of lOFC neurons, either based on clustering of their trial-averaged peristimulus time histograms (PSTHs), or a feature space defined by their average conditional firing rates aligned to different task variables. We observed weak encoding of reward attributes, but stronger encoding of reward history, the animals left or right choice, and reward receipt across all clusters. Only one cluster, however, encoded the animals reward history at the time shortly preceding the choice, suggesting a possible role in integrating previous and current trial outcomes at the time of choice. This cluster also exhibits qualitatively similar responses to identified corticostriatal projection neurons in a recent study (Hirokawa et al., 2019), and suggests a possible role for subpopulations of lOFC neurons in mediating trial-by-trial learning.

neuroscience↗

bioRxiv2017

Myopic control of neural dynamics

Manipulating the dynamics of neural systems through targeted stimulation is a frontier of research and clinical neuroscience; however, the control schemes considered for neural systems are mismatched for the unique needs of manipulating neural dynamics. An appropriate control method should respect the variability in neural systems, incorporating moment to moment \"input\" to the neural dynamics and behaving based on the current neural state, irrespective of the past trajectory. We propose such a controller under a nonlinear state-space feedback framework that steers one dynamical system to function as through it were another dynamical system entirely. This \"myopic\" controller is formulated through a novel variant of a model reference control cost that manipulates dynamics in a short-sighted manner that only sets a target trajectory of a single time step into the future (hence its myopic nature), which omits the need to pre-calculate a rigid and computationally costly neural feedback control solution. To demonstrate the breadth of this controls utility, two examples with distinctly different applications in neuroscience are studied. First, we show the myopic controls utility to probe the causal link between dynamics and behavior for cognitive processes by transforming a winner-take-all decision-making system to operate as a robust neural integrator of evidence. Second, an unhealthy motor-like system containing an unwanted beta-oscillation spiral attractor is controlled to function as a healthy motor system, a relevant clinical example for neurological disorders.

neuroscience↗