The University of Tokyo · Neuroscience
Professor Zenas C. Chao's research lab investigates the neural mechanisms underlying learning, memory, and cognitive function through a multidisciplinary approach that integrates computational modeling, in vitro neural networks, and in vivo electrophysiology in animal models. The lab focuses on understanding how cortical circuits reorganize after injury—particularly spinal cord injury—and how dysfunctions in predictive coding and network dynamics contribute to neuropsychiatric and neurodevelopmental disorders such as depression and autism spectrum disorder. By leveraging advanced signal analysis techniques like center of activity trajectory (CAT) and quantitative decomposition of EEG/ECoG signals, the lab uncovers spatiotemporal dynamics of functional plasticity across hierarchical brain networks. Their work bridges cellular-level synaptic plasticity with systems-level network behavior in health and disease.
Figures are computed from collected data and may differ slightly.
The acts of learning and memory are thought to emerge from the modifications of synaptic connections between neurons, as guided by sensory feedback during behavior. However, much is unknown about how such synaptic processes can sculpt and are sculpted by neuronal population dynamics and an interaction with the environment. Here, we embodied a simulated network, inspired by dissociated cortical neuronal cultures, with an artificial animal (an animat) through a sensory-motor loop consisting of str
Electrically interfaced cortical networks cultured in vitro can be used as a model for studying the network mechanisms of learning and memory. Lasting changes in functional connectivity have been difficult to detect with extracellular multi-electrode arrays using standard firing rate statistics. We used both simulated and living networks to compare the ability of various statistics to quantify functional plasticity at the network level. Using a simulated integrate-and-fire neural network, we com
After spinal cord injury (SCI), the motor-related cortical areas can be a potential substrate for functional recovery in addition to the spinal cord. However, a dynamic description of how motor cortical circuits reorganize after SCI is lacking. Here, we captured the comprehensive dynamics of motor networks across SCI in a nonhuman primate model. Using electrocorticography over the sensorimotor areas in monkeys, we collected broadband neuronal signals during a reaching-and-grasping task at differ
The human brain is proposed to harbor a hierarchical predictive coding neuronal network underlying perception, cognition, and action. In support of this theory, feedforward signals for prediction error have been reported. However, the identification of feedback prediction signals has been elusive due to their causal entanglement with prediction-error signals. Here, we use a quantitative model to decompose these signals in electroencephalography during an auditory task, and identify their spatio-
Our findings revealed altered coordination between 2 functional networks in MDD and suggest that weak suppression of the task-negative α-band parietal network contributes to blunted positive emotional responses in adults with depression. The subnetworks identified here could be used for diagnosis or targeted for treatment in the future.
Creativity is defined by three key factors: novelty, feasibility and value. While many creativity tests focus primarily on novelty, they often neglect feasibility and value, thereby limiting their reflection of real-world creativity. In this study, we employ GPT-4, a large language model, to assess these three dimensions in a Japanese-language Alternative Uses Test (AUT). Using a crowdsourced evaluation method, we acquire ground truth data for 30 question items and test various GPT prompt design
In autism spectrum disorder (ASD), atypical sensory experiences are often associated with irregularities in predictive coding, which proposes that the brain creates hierarchical sensory models via a bidirectional process of predictions and prediction errors. However, it remains unclear how these irregularities manifest across different functional hierarchies in the brain. To address this, we study a marmoset model of ASD induced by valproic acid (VPA) treatment. We record high-density electrocor
Context is information linked to a situation that can guide behavior. In the brain, context is encoded by sensory processing and can later be retrieved from memory. How context is communicated within the cortical network in sensory and mnemonic forms is unknown due to the lack of methods for high-resolution, brain-wide neuronal recording and analysis. Here, we report the comprehensive architecture of a cortical network for context processing. Using hemisphere-wide, high-density electrocorticogra
MATLAB code to run quantitative values of prediction and prediction errors as performed in the publication "A quantitative model reveals a frequency ordering of prediction and prediction-error signals in the human brain".
Sensory areas of cortex respond more strongly to infrequent stimuli when these violate previously established regularities, a phenomenon known as deviance detection (DD). Previous modeling work has mainly attempted to explain DD on the basis of synaptic plasticity. However, a large fraction of cortical neurons also exhibit firing rate adaptation, an underexplored potential mechanism. Here, we investigate DD in a spiking neuronal network model with two types of short-term plasticity, fast synapti
Abstract Predictive coding posits the brain predicts incoming sensory information and signals prediction errors when actual input differs from expectations. Positive prediction errors occur when input exceeds predictions, while negative prediction errors arise when input falls short. Specific neurons are theorized to encode negative prediction errors, linked to responses to omitted expected inputs. However, the information encoded in omission responses remains unclear. We recorded single-unit ac
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