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135 words Main text: 2824 words AbstractFeedforward networks (FFN) are ubiquitous structures in neural systems and have been studied to understand mechanisms of reliable signal and information transmission. In many FFNs, neurons in one layer have intrinsic properties that are distinct from those in their pre-/postsynaptic layers, but how this affects network-level information processing remains unexplored. Here we show that layerto-layer heterogeneity arising from lamina-specific cellular properties facilitates signal and information transmission in FFNs. Specifically, we found that signal transformations, made by neighboring layers of neurons on an input-driven spike signal, are complementary to each other. This mechanism boosts information transfer carried by a propagating spike signal, and thereby supports reliable spike signal and information transmission in a deep FFN. Our study suggests that distinct cell types in neural circuits have complementary computational functions and facilitate information processing on the whole. Significance StatementNeural systems have many cell types that differ in properties such as size, shape, cellular mechanisms, etc. Furthermore, neurons often propagate signals to other neurons that have properties very different from their own. We investigated what this phenomenon implies in neural information processing by using computational network models, inspired by a recent experimental study on the olfactory neural pathway of fruit flies. We found that different types of neurons can perform complementary functions in a network, which boosts information transfer on the whole and supports robust, stable signal propagation in a "deep" network with many layers. Our study demonstrates that diverse cell types with different intrinsic properties are crucial for optimal signal and information transfer in neural networks.

Electron Cyclotron Emission Imaging (ECEI) is a diagnostic system which measures 2-D electron temperature profiles with high spatial-temporal resolution. Usually only the normalized electron temperature fluctuations are utilized to investigate the magnetohydrodynamics modes due to the difficulties of ECEI calibration. In this paper, we developed a self-dependent calibration method for 24 × 16 channel high-resolution ECEI on the Experimental Advanced Superconducting Tokamak. The technique of shape matching is applied to solve for the matrix of the calibration coefficients. The calibrated area is further expanded to an occupation ratio of 88% observation area by utilizing the features of sawtooth crash. The result is self-consistent and consistent with calibrated 1D ECE measurement.

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What is the difference between goal-directed and habitual behavior? We propose a novel computational framework of decision making with Bayesian inference, in which everything is integrated as an entire neural network model. The model learns to predict environmental state transitions by self-exploration and generating motor actions by sampling stochastic internal states z. Habitual behavior, which is obtained from the prior distribution of z, is acquired by reinforcement learning. Goal-directed behavior is determined from the posterior distribution of z by planning, using active inference which optimizes the past, current and future z by minimizing the variational free energy for the desired future observation constrained by the observed sensory sequence. We demonstrate the effectiveness of the proposed framework by experiments in a sensorimotor navigation task with camera observations and continuous motor actions.

How to behave efficiently and flexibly is a central problem for understanding biological agents and creating intelligent embodied AI. It has been well known that behavior can be classified as two types: reward-maximizing habitual behavior, which is fast while inflexible; and goal-directed behavior, which is flexible while slow. Conventionally, habitual and goal-directed behaviors are considered handled by two distinct systems in the brain. Here, we propose to bridge the gap between the two behaviors, drawing on the principles of variational Bayesian theory. We incorporate both behaviors in one framework by introducing a Bayesian latent variable called "intention". The habitual behavior is generated by using prior distribution of intention, which is goal-less; and the goal-directed behavior is generated by the posterior distribution of intention, which is conditioned on the goal. Building on this idea, we present a novel Bayesian framework for modeling behaviors. Our proposed framework enables skill sharing between the two kinds of behaviors, and by leveraging the idea of predictive coding, it enables an agent to seamlessly generalize from habitual to goal-directed behavior without requiring additional training. The proposed framework suggests a fresh perspective for cognitive science and embodied AI, highlighting the potential for greater integration between habitual and goal-directed behaviors.