Biology subjects

Chen, Z. S.

Publications and source records attributed to Chen, Z. S..

3 recordsLinked to original sources

bioRxiv2019

Predictive Coding Models for Pain Perception

Pain is a complex, multidimensional experience that involves dynamic interactions between sensory-discriminative and affective-emotional processes. Pain experiences have a high degree of variability depending on their context and prior anticipation. Viewing pain perception as a perceptual inference problem, we propose a predictive coding paradigm to characterize evoked and non-evoked pain. We record the local field potentials (LFPs) from the primary somatosensory cortex (S1) and the anterior cingulate cortex (ACC) of freely behaving rats--two regions known to encode the sensory-discriminative and affective-emotional aspects of pain, respectively. We further use predictive coding to investigate the temporal coordination of oscillatory activity between the S1 and ACC. Specifically, we develop a phenomenological predictive coding model to describe the macroscopic dynamics of bottom-up and top-down activity. Supported by recent experimental data, we also develop a biophysical neural mass model to describe the mesoscopic neural dynamics in the S1 and ACC populations, in both naive and chronic pain-treated animals. Our proposed predictive coding models not only replicate important experimental findings, but also provide new prediction about the impact of the model parameters on the physiological or behavioral read-out--thereby yielding mechanistic insight into the uncertainty of expectation, placebo or nocebo effect, and chronic pain.

neuroscience↗

bioRxiv2019

Ultrafast Readout of Representations from Spatially Distributed Rodent Hippocampal Field Potentials

Spatiotemporal patterns of large-scale spiking and field potentials of the rodent hippocampus encode spatial representations during maze run, immobility and sleep. Here, we showed that multi-site hippocampal field potential amplitude at ultra-high frequency band (FPAuhf) provides not only a fast and reliable reconstruction of the rodents position in wake, but also a readout of replay content during sharp wave ripples. This FPAuhf feature may serve as robust real-time decoding strategy from large-scale (up to 100,000 electrodes) recordings in closed-loop experiments. Furthermore, we developed unsupervised learning approaches to extract low-dimensional spatiotemporal FPAuhf features during run and ripple periods, and to infer latent dynamical structures from lower-rank FPAuhf features. We also developed a novel optical flow-based method to identify propagating spatiotemporal LFP patterns from multi-site array recordings, which can be used for decoding application. Finally, we developed a prospective decoding strategy to predict animals future decision in goal-directed navigation.

neuroscience↗

bioRxiv2019

Thalamocortical network connectivity controls spatiotemporal dynamics of cortical and thalamic traveling waves

Propagation of neural activity in spatially structured neuronal networks has been observed in awake, anesthetized and sleeping brains. However, it remains unclear how traveling waves are coordinated temporally across recurrently connected brain structures, and how network connectivity affects spatiotemporal neural activity. Here we develop a computational model of a two-dimensional thalamocortical network that enables us to investigate traveling wave characteristics in space-time. We show that thalamocortical and intracortical network connectivity, excitation/inhibition balance, thalamocortical/corticothalamic delay can independently or jointly change the spatiotemporal patterns (radial, planar and rotating waves) and characteristics (speed, direction and frequency) of cortical and thalamic traveling waves. Simulations of our model further predict that increased thalamic inhibition induces slower cortical wave frequency, and enhanced cortical excitation increases cortical wave speed and oscillation frequencies. Overall, the model study provides not only theoretical insight into the basis for spatiotemporal wave patterns, but also experimental predictions that potentially control these dynamics.\n\nAuthor SummaryCognition or sensorimotor control requires the coordination of neural activity across widespread brain circuits. Propagating waves of oscillatory neural activities have been observed at both macroscopic and mesoscopic levels, with various frequencies, spatial coverage, and modalities. However, a complete understanding how thalamocortical traveling waves are originated and temporally coordinated in the thalamus and cortex are still unclear. Furthermore, it remains unknown how the network connectivity, excitation/inhibition balance, thalamocortical or corticothalamic delay determine the spatiotemporal wave patterns and characteristics of cortical and thalamic traveling waves. Here we develop a computational model of a two-dimensional thalamocortical network to investigate the thalamic and neocortical traveling wave characteristics in space-time, which allows us to quantitatively assess the impact of thalamocortical network properties on the formation and maintenance of complex traveling wave patterns. Our computational model provides strong theoretical insight into the basis of spatiotemporal wave propagation, as well as experimental predictions that control these wave dynamics.

neuroscience↗