Linxing Preston Jiang's research while affiliated with University of Washington Seattle and other places
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Publications (11)
Objective: A major challenge in closed-loop brain-computer interfaces (BCIs) is finding optimal stimulation patterns as a function of ongoing neural activity for different subjects and objectives. Traditional approaches, such as those currently used for deep brain stimulation, have largely followed a trial- and-error strategy to search for effectiv...
We introduce dynamic predictive coding, a new hierarchical model of spatiotemporal prediction and sequence learning in the cortex. The model assumes that higher cortical levels modulate the temporal dynamics of lower levels, correcting their predictions of dynamics using precision-weighted prediction errors. We tested this model using a two-level n...
Predictive coding is a unifying framework for understanding perception, action and neocortical organization. In predictive coding, different areas of the neocortex implement a hierarchical generative model of the world that is learned from sensory inputs. Cortical circuits are hypothesized to perform Bayesian inference based on this generative mode...
The original predictive coding model of Rao & Ballard (1999) focused on spatial prediction to explain spatial receptive fields and contextual effects in the visual cortex. Here, we introduce a new dynamic predictive coding model that achieves spatiotemporal prediction of complex natural image sequences using time-varying transition matrices. We ove...
Learning a generative model of visual information with sparse and compositional features has been a challenge for both theoretical neuroscience and machine learning communities. Sparse coding models have achieved great success in explaining the receptive fields of mammalian primary visual cortex with sparsely activated latent representation. In thi...
Studying the neural correlates of sleep can lead to revelations in our understanding of sleep and its interplay with different neurological disorders. Sleep research relies on manual annotation of sleep stages based on rules developed for healthy adults. Automating sleep stage annotation can expedite sleep research and enable us to better understan...
Methodological advances have made it possible to generate fMRI predictions for cognitive architectures, such as ACT- R, thus expanding the range of model predictions and making it possible to distinguish between alternative models that produce otherwise identical behavioral patterns. However, for tasks associated with relatively brief response time...
Methodological advances have made it possible to generate fMRI predictions for cognitive architectures, such as ACT-R, thus expanding the range of model predictions and making it possible to distinguish between alternative models that produce otherwise identical behavioral patterns. However, for tasks associated with relatively brief response times...
We present BrainNet which, to our knowledge, is the first multi-person non-invasive direct brain-to-brain interface for collaborative problem solving. The interface combines electroencephalography (EEG) to record brain signals and transcranial magnetic stimulation (TMS) to deliver information noninvasively to the brain. The interface allows three h...
We present BrainNet which, to our knowledge, is the first multi-person non-invasive direct brain-to-brain interface for collaborative problem solving. The interface combines electroencephalography (EEG) to record brain signals and transcranial magnetic stimulation (TMS) to deliver information noninvasively to the brain. The interface allows three h...
We present BrainNet which, to our knowledge, is the first multi-person non-invasive direct brain-to-brain interface for collaborative problem solving. The interface combines electroencephalography (EEG) to record brain signals and transcranial magnetic stimulation (TMS) to deliver information noninvasively to the brain. The interface allows three h...
Citations
... D. Murray et al., 2014;Runyan et al., 2017;Siegle et al., 2021). Recent work by the authors (Jiang et al., 2021) suggests that a hierarchical predictive coding model based on dynamic synaptic connections (via "hypernetworks") can learn visual cortical space-time receptive fields and hierarchical temporal representations from natural video sequences. ...
![[object Object]](https://i1.rgstatic.net/ii/profile.image/279099928006662-1443554016841_Q64/Rajesh-Rao-2.jpg)
... It should be noted that 'important' features identified by these approaches may not be guaranteed to bear any direct statistical dependency to the predicted target variable themselves -their predictive value may derive from a conditional dependency through other features. Different architectures such as hidden Markov models were shown to learn behavioral states and can also be used to generate data in order to validate identified states (Sun et al., 2020). This semi-supervised generative approach thus yields high model interpretability. ...
... Furthermore, they are widely used for cognitive and emotional assessment and cognitive augmentation, where users can improve their mental skills, being useful for metaverse applications [29]. Besides, current literature explores the feasibility of using BCIs to allow direct communication between brains, using both neural acquisition and neurostimulation capabilities [12]. ...
![[object Object]](https://i1.rgstatic.net/ii/profile.image/1116623587028993-1643235217951_Q64/Andrea-Stocco-2.jpg)
