Biology subjects

Chen, Z. S.

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

23 records · Page 2Linked to original sources

bioRxiv2021

A compact spatial map in V2 visual cortex

Vision plays a critical role in guiding spatial navigation. A traditional view of the visual cortex is to compute a world-centered map of visual space, and visual neurons exhibit diverse tunings to simple or complex visual features. The neural representation of spatio-visual map in the visual cortex is thought to be transformed from spatial modulation signals at the hippocampal-entorhinal system. Although visual thalamic and cortical neurons have been shown to be modulated by spatial signals during navigation, the exact source of spatially modulated neurons within the visual circuit has never been identified, and the neural correlate underpinning a visuospatial or spatio-visual map remains elusive. To search for direct visuospatial and visuodirectional signals, here we record in vivo extracellular spiking activity in the secondary visual cortex (V2) from freely foraging rats in a naturalistic environment. We identify that V2 neurons forms a complete spatio-visual map with a wide range of spatial tunings, which resembles the classical spatial map that includes the place, head-direction, border, grid and conjunctive cells reported in the hippocampal-entorhinal network. These spatially tuned V2 neurons display stable responses to external visual cues, and are robust with respect to non- spatial environmental changes. Spatially and directionally tuned V2 neuronal firing persists in darkness, suggesting that this spatio-visual map is not completely dependent on visual inputs. Identification of functionally distinct spatial cell types in visual cortex expands its classical role of information coding beyond a retinotopic map of the eye-centered world.

neuroscience↗

bioRxiv2021

Spiking recurrent neural networks represent task-relevant neural sequences in rule-dependent computation

Prefrontal cortical neurons play in important roles in performing rule-dependent tasks and working memory-based decision making. Motivated by experimental data, we develop an excitatory-inhibitory spiking recurrent neural network (SRNN) to perform a rule-dependent two-alternative forced choice (2AFC) task. We imposed several important biological constraints onto the SRNN, and adapted the spike frequency adaptation (SFA) and SuperSpike gradient methods to update the network parameters. These proposed strategies enabled us to train the SRNN efficiently and overcome the vanishing gradient problem during error back propagation through time. The trained SRNN produced rule-specific tuning in single-unit representations, showing rule-dependent population dynamics that strongly resemble experimentally observed data in rodent and monkey. Under varying test conditions, we further manipulated the parameters or configuration in computer simulation setups and investigated the impacts of rule-coding error, delay duration, weight connectivity and sparsity, and excitation/inhibition (E/I) balance on both task performance and neural representations. Overall, our modeling study provides a computational framework to understand neuronal representations at a fine timescale during working memory and cognitive control. Author SummaryWorking memory and decision making are fundamental cognitive functions of the brain, but the circuit mechanisms of these brain functions remain incompletely understood. Neuroscientists have trained animals (rodents or monkeys) to perform various cognitive tasks while simultaneously recording the neural activity from specific neural circuits. To complement the experimental investigations, computational modeling may provide an alternative way to examine the neural representations of neuronal assemblies during task behaviors. Here we develop and train a spiking recurrent neural network (SRNN) consisting of balanced excitatory and inhibitory neurons to perform the rule-dependent working memory tasks Our computer simulations produce qualitatively similar results as the experimental findings. Moreover, the imposed biological constraints on the trained network provide additional channel to investigate cell type-specific population responses, cortical connectivity and robustness. Our work provides a computational platform to investigate neural representations and dynamics of cortical circuits a fine timescale during complex cognitive tasks.

neuroscience↗

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↗