He Chen’s research while affiliated with Peking University and other places

Neural dynamics are thought to reflect computations that relay and transform information in the brain. Previous studies have identified the neural population dynamics in many individual brain regions as a trajectory geometry, preserving a common computational motif. However, whether these populations share particular geometric patterns across brain-wide neural populations remains unclear. Here, by mapping neural dynamics widely across temporal/frontal/limbic regions in the cortical and subcortical structures of monkeys, we show that 10 neural populations, including 2,500 neurons, propagate visual item information in a stochastic manner. We found that visual inputs predominantly evoked rotational dynamics in the higher-order visual area, TE, and its downstream striatum tail, while curvy/straight dynamics appeared frequently downstream in the orbitofrontal/hippocampal network. These geometric changes were not deterministic but rather stochastic according to their respective emergence rates. Our meta-analysis results indicate that visual information propagates as a heterogeneous mixture of stochastic neural population signals in the brain.

Neural dynamics reflect canonical computations that relay and transform information in the brain. Previous studies have identified the neural population dynamics in many individual brain regions as a trajectory geometry in a low-dimensional neural space. However, whether these populations share particular geometric patterns across brain-wide neural populations remains unclear. Here, by mapping neural dynamics widely across temporal/frontal/limbic regions in the cortical and subcortical structures of monkeys, we show that 10 neural populations, including 2,500 neurons, propagate visual item information in a stochastic manner. We found that the visual inputs predominantly evoked rotational dynamics in the higher-order visual area, the TE and its downstream striatum tail, while curvy/straight dynamics appeared more frequently downstream in the orbitofrontal/hippocampal network. These geometric changes were not deterministic but rather stochastic according to their respective emergence rates. These results indicated that visual information propagates as a heterogeneous mixture of stochastic neural population signals in the brain.

Neural population dynamics provide a key computational framework for understanding information processing in the sensory, cognitive, and motor functions of the brain. They systematically depict complex neural population activity, dominated by strong temporal dynamics as trajectory geometry in a low-dimensional neural space. However, neural population dynamics are poorly related to the conventional analytical framework of single-neuron activity, the rate-coding regime that analyzes firing rate modulations using task parameters. To link the rate-coding and dynamic models, we developed a variant of state-space analysis in the regression subspace, which describes the temporal structures of neural modulations using continuous and categorical task parameters. In macaque monkeys, using two neural population datasets containing either of two standard task parameters, continuous and categorical, we revealed that neural modulation structures are reliably captured by these task parameters in the regression subspace as trajectory geometry in a lower dimension. Furthermore, we combined the classical optimal-stimulus response analysis (usually used in rate-coding analysis) with the dynamic model and found that the most prominent modulation dynamics in the lower dimension were derived from these optimal responses. Using those analyses, we successfully extracted geometries for both task parameters that formed a straight geometry, suggesting that their functional relevance is characterized as a unidimensional feature in their neural modulation dynamics. Collectively, our approach bridges neural modulation in the rate-coding model and the dynamic system, and provides researchers with a significant advantage in exploring the temporal structure of neural modulations for pre-existing datasets.

For living organisms, the ability to acquire information regarding the external space around them is critical for future actions. While the information must be stored in an allocentric frame to facilitate its use in various spatial contexts, each case of use requires the information to be represented in a particular self-referenced frame. Previous studies have explored neural substrates responsible for the linkage between self-referenced and allocentric spatial representations based on findings in rodents. However, the behaviors of rodents are different from those of primates in several aspects; for example, rodents mainly explore their environments through locomotion, while primates use eye movements. In this review, we discuss the brain mechanisms responsible for the linkage in nonhuman primates. Based on recent physiological studies, we propose that two types of neural substrates link the first-person perspective with allocentric coding. The first is the view-center background signal, which represents an image of the background surrounding the current position of fixation on the retina. This perceptual signal is transmitted from the ventral visual pathway to the hippocampus (HPC) via the perirhinal cortex and parahippocampal cortex. Because images that share the same objective-position in the environment tend to appear similar when seen from different self-positions, the view-center background signals are easily associated with one another in the formation of allocentric position coding and storage. The second type of neural substrate is the HPC neurons' dynamic activity that translates the stored location memory to the first-person perspective depending on the current spatial context.

Neural population dynamics, presumably fundamental computational units in the brain, provide a key framework for understanding information processing in the sensory, cognitive, and motor functions. However, neural population dynamics is not explicitly related to the conventional analytic framework for single-neuron activity, i.e., representational models that analyze neuronal modulations associated with cognitive and motor parameters. In this study, we applied a recently developed state-space analysis to incorporate the representational models into the dynamic model in combination with these parameters. We compared neural population dynamics between continuous and categorical task parameters during two visual recognition tasks, using the datasets originally designed for a single-neuron approach. We successfully extracted neural population dynamics in the regression subspace, which represent modulation dynamics for both continuous and categorical task parameters with reasonable temporal characteristics. Furthermore, we combined the classical optimal-stimulus analysis paradigm for the single-neuron approach (i.e., stimulus identified as maximum neural responses) into the dynamic model, and found that the most prominent modulation dynamics at the lower dimension were derived from these optimal responses. Thus, our approach provides a unified framework for incorporating knowledge acquired with the single-neuron approach into the dynamic model as a standard procedure for describing neural modulation dynamics in the brain.

Recent work has shown that the medial temporal lobe (MTL), including the hippocampus (HPC) and its surrounding limbic cortices, plays a role in scene perception in addition to episodic memory. The two basic factors of scene perception are the object (“what”) and location (“where”). In this review, we first summarize the anatomical knowledge related to visual inputs to the MTL and physiological studies examining object-related information processed along the ventral pathway briefly. Thereafter, we discuss the space-related information, the processing of which was unclear, presumably because of its multiple aspects and a lack of appropriate task paradigm in contrast to object-related information. Based on recent electrophysiological studies using non-human primates and the existing literature, we proposed the “reunification theory,” which explains brain mechanisms which construct object-location signals at each gaze. In this reunification theory, the ventral pathway signals a large-scale background image of the retina at each gaze position. This view-center background signal reflects the first person’s perspective and specifies the allocentric location in the environment by similarity matching between images. The spatially invariant object signal and view-center background signal, both of which are derived from the same retinal image, are integrated again (i.e., reunification) along the ventral pathway-MTL stream, particularly in the perirhinal cortex. The conjunctive signal, which represents a particular object at a particular location, may play a role in scene perception in the HPC as a key constituent element of an entire scene.