Biology subjects

Chao, Z. C.

Publications and source records attributed to Chao, Z. C..

5 recordsLinked to original sources

bioRxiv2024

Effects of Spatial Constraints of Inhibitory Connectivity on the Dynamical Development of Criticality in Spiking Networks

Neural systems are hypothesized to operate near criticality, enhancing their capacity for optimal information processing, transmission and storage capabilities. Criticality has typically been studied in spiking neural networks and related systems organized in random or full connectivity, with the balance of excitation and inhibition being a key determinant of the critical point of the system. However, given that neurons in the brain are spatially distributed, with their distances significantly influencing connectivity and signal timing, it is unclear how the spatial organization of excitatory and inhibitory connectivity influences the networks self-organization towards criticality. Here, we systematically constrain the distance and density of inhibitory connectivity in two-dimensional spiking networks and allow synaptic weights to self-organize with activity-dependent excitatory and inhibitory plasticity in the presence of a low level of stochastic intrinsic activity. We then investigate the relationship between inhibitory connectivity, synaptic weights, and the resulting network activity during and after development. We find that networks with longer-range inhibitory synapses tend towards more supercritical behavior compared to networks with a similar number of shorter-range inhibitory synapses. We show that this distance dependence is a consequence of weaker long-range synapses after development due to the presence of synaptic delays, which shift most spike pairs outside of the potentiation window of the inhibitory learning rule.

neuroscience↗

bioRxiv2024

Temporal Prediction through Integration of Probability Distributions of Event Timings at Multiple Levels

Our brain uses prior experience to anticipate the timing of upcoming events. This dynamical process can be modeled using a hazard function derived from the probability distribution of event timings. However, the contexts of an event can lead to various probability distributions for the same event, and it remains unclear how the brain integrates these distributions into a coherent temporal prediction. In this study, we create a local-global foreperiod paradigm consisting of a sequence of paired trials, where in each trial, participants respond to a target signal after a specified time interval (i.e. foreperiod) following a warning cue. The prediction of the target onset in the second trial can be based on the probability distribution of the second foreperiod (local level) and its conditional probability given the foreperiod in the first trial (global level). These probability distributions are then transformed into hazard functions to represent the local and global temporal predictions. Reaction times to the target signal are best explained by incorporating both local and global predictions, indicating that both levels of temporal information contribute to making predictions. We further show that electroencephalographic source signals are best reconstructed when integrating both predictions. Specifically, the local and global predictions are separately encoded in the posterior and anterior brain regions, and to achieve synergy between both predictions, a third region--particularly the right posterior cingulate area--is needed. Our study reveals brain networks that integrate multilevel temporal information, providing a comprehensive view of hierarchical predictive coding of time.

neuroscience↗

bioRxiv2024

Omission-responsive neurons encode negative prediction error and probability in the auditory cortex

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 activity in rat auditory cortex during an omission paradigm with varying tone probabilities. We identified neurons selectively responding to omissions, with responses increasing with evidence accumulation and correlating with tone predictability--key characteristics of negative prediction-error neurons. Interestingly, these neurons showed selective omission responses but broad tone responses, revealing an asymmetry in error signaling. We propose a circuit model with laterally interconnected prediction-error neurons reproducing this asymmetry. Our model demonstrates that lateral connections enhance precision and efficiency of prediction encoding, supported by the free energy principle.

neuroscience↗

bioRxiv2023

Dissecting Mismatch Negativity: Early and Late Subcomponents for Detecting Deviants in Local and Global Sequence Regularities

Mismatch negativity (MMN) is commonly recognized as a neural signal of prediction error evoked by deviants in the expected pattern of sensory input. Studies show that MMN diminishes when a sequence pattern becomes more predictable over a longer timescale. This implies that MMN is comprised of multiple subcomponents, each responding to different levels of temporal regularities. To probe the hypothesized subcomponents in MMN, we record human electroencephalography during an auditory local-global oddball paradigm where the tone-to-tone transition probability (local regularity) and the overall sequence probability (global regularity) are manipulated to control temporal predictabilities at two hierarchical levels. We find that the size of MMN is correlated with both probabilities and the spatiotemporal structure of MMN can be decomposed into two distinct subcomponents. Both subcomponents appear as negative waveforms which peak early in the central-frontal area and late in a more frontal area, respectively. With a quantitative predictive coding model, we map the early and late subcomponents to the prediction errors that are tied to local and global regularities, respectively. Our study highlights the hierarchical complexity of MMN and offers an experimental and analytical platform for developing a multi-tiered neural marker, applicable in clinical settings.

neuroscience↗

bioRxiv2023

Crossmodal Hierarchical Predictive Coding for Audiovisual Sequences in Human Brain

Predictive-coding theory proposes that the brain actively predicts sensory inputs based on prior knowledge. While this theory has been extensively researched within individual sensory modalities, there is a crucial need for empirical evidence supporting hierarchical predictive processing across different modalities to further generalize the theory. Here, we examine how crossmodal knowledge is represented and learned in the brain by identifying the hierarchical networks underlying crossmodal predictions when information of one sensory modality leads to a prediction in another modality. We record electroencephalogram (EEG) in humans during a crossmodal audiovisual local-global oddball paradigm, in which the predictability of transitions between tones and images are manipulated at two hierarchical levels: stimulus-to-stimulus transition (local level) and multi-stimulus sequence structure (global level). With a model-fitting approach, we decompose the EEG data using three distinct predictive-coding models: one with no audiovisual integration, one with audiovisual integration at the global level, and one with audiovisual integration at both the local and global levels. The best-fitting model demonstrates that audiovisual integration occurs at both levels. This highlights a convergence of auditory and visual information to construct crossmodal predictions, even in the more basic interactions that occur between individual stimuli. Furthermore, we reveal the spatio-spectro-temporal signatures of prediction-error signals across hierarchies and modalities, and show that auditory and visual prediction-error signals are progressively redirected to the central-parietal area of the brain as learning progresses. Our findings unveil a crossmodal predictive coding mechanism, where the unimodal framework is implemented through more distributed brain networks to process hierarchical crossmodal knowledge.

neuroscience↗