Biology subjects

Izawa, J.

Publications and source records attributed to Izawa, J..

3 recordsLinked to original sources

bioRxiv2022

The Computational Neuroanatomy of Predictive Dynamics of Pain Perception

Pain perception is an active process that regulates nociceptive inputs by descending opioidergic signals, in which the brain encodes pain-related predictive and corrective terms, after having made Bayesian-like inferences about noxious amplitudes. Offset analgesia (OA), a large reduction of tonic pain after a small nociceptive termination, is typical empirical evidence of on-line pain modulation through prediction and its correction. However, the basic computational structure underlying OA is not understood. Here, we adopted a constructive approach, formulated the inference of noxious amplitudes with a Kalman filter model, i.e., a recursive Bayesian computation, and then deduced the computational structure for OA, in which an interaction between two latent state variables was implemented. Simulation results suggested that the unidirectional interaction of the two states with two dissociable roles (an integral over time and a derivative of stimulus changes) is crucial for OA. Our results, combined with previous anatomical studies, suggest a computational basis of neural connectivity for pain. The ACC and aINS interact to compute a descending prediction to the brainstem, i.e. PAG, while ascending inputs are filtered in the thalamus and delivered to the cortices as prediction errors. Thus, we suggest dissociable, computational roles of the ACC and aINS in pain processing. Author SummaryUnderstanding the computational theory of pain perception is crucial for clarifying why some painful syndromes become chronic. Here, we propose a computational neuroanatomical model of endogenous pain modulation and we simulate a model for offset analgesia. We first demonstrate through model comparisons that the brain encodes at least two distinct states to estimate ongoing nociception: a derivative of input changes and its integral. We suggest that its neural substrate comprises hierarchical circuits composed of cortices, the thalamus, and brainstem. Second, we show that the computational basis of disrupted pain modulation in patients is pseudo-neglect of actual sensory inputs, with bias toward the internal prediction. Our results are the first to provide a neurocomputational mechanism of pain perception dynamics and a factor that determines its functionality.

neuroscience↗

bioRxiv2020

Reinforcement meta-learning optimizes visuomotor learning

Reinforcement learning enables the brain to learn optimal action selection, such as go or not go, by forming state-action and action-outcome associations. Does this mechanism also optimize the brains willingness to learn, such as learn or not learn? Learning to learn by rewards, i.e., reinforcement meta-learning, is a crucial mechanism for machines to develop flexibility in learning, which is also considered in the brain without empirical examinations. Here, we show that humans learn to learn or not learn to maximize rewards in visuomotor learning tasks. We also show that this regulation of learning is not a motivational bias but is a result of an instrumental, active process, which takes into account the learning-outcome structure. Our results thus demonstrate the existence of reinforcement meta-learning in the human brain. Because motor learning is a process of minimizing sensory errors, our findings uncover an essential mechanism of interaction between reward and error.

neuroscience↗

bioRxiv2018

What you predict, not what you do, updates memories in visuomotor adaptation

Sensory prediction errors are thought to update memories in motor 1 adaptation, but the role of performance errors is largely unknown. To dissociate these errors, we manipulated visual feedback during fast shooting movements under visuomotor rotation. Participants were instructed to strategically correct for performance errors by shooting to a neighboring target in one of four conditions: following the movement onset, the main target, the neighboring target, both targets, or none of the targets disappeared. Participants in all conditions experienced a drift away from the main target following the strategy. In conditions where the main target was shown, participants often tried to minimize performance errors caused by the drift by generating corrective movements. However, despite differences in performance during adaptation between conditions, memory decay in a delayed washout block was indistinguishable between conditions. Our results thus suggest that, in visuomotor adaptation, sensory predictions errors, but not performance errors, update the slow, temporally stable, component of motor memory.

neuroscience↗