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Eye movements are the primary way primates interact with the world. Understanding how the brain controls the eyes is therefore crucial for improving human health and designing visual rehabilitation devices. However, brain activity is challenging to decipher. Here, we leveraged machine learning algorithms to reconstruct tracking eye movements from high-resolution neuronal recordings. We found that continuous eye position could be decoded with high accuracy using spiking data from only a few dozen cortical neurons. We tested eight decoders and found that neural network models yielded the highest decoding accuracy. Simpler models performed well above chance with a substantial reduction in training time. We measured the impact of data quantity (e.g. number of neurons) and data format (e.g. bin width) on training time, inference time, and generalizability. Training models with more input data improved performance, as expected, but the format of the behavioral output was critical for emphasizing or omitting specific oculomotor events. Our results provide the first demonstration, to our knowledge, of continuously decoded eye movements across a large field of view. Our comprehensive investigation of predictive power and computational efficiency for common decoder architectures provides a much-needed foundation for future work on real-time gaze-tracking devices.

During fetal development the morphology and function of the organs and tissues is determined. An example occurs with the formation of the cerebral cortex. On the external surface of the brain there are numerous folds (gyri, sulci, and fissures) that determine brain function. The exact cause for the formation of patterns of these folds is unknown. This article proposes a reaction-diffusion model in conjunction with a process of surface mechanical strain to explain the morphogenesis of the superficial structure of the brain.The model is solved using finite elements. There have been tests done on the formation of brain patterns through the reaction-diffusion equations with parameters in the space of Turing and by random mechanical strain. Several numerical examples have been developed that show an acceptable correlation between the results and clinical reality. With the model we can represent, qualitatively, the formation of the cerebral cortex by the proposed model. The model can approximate, and explain, lissencephaly and polymicrogyria, diseases that develop in the cerebral cortex and lead to medical complications to sufferers.

The interplay between computational neuroscience and humanoid robotics, both historically and looking forward, is presented, with a particular focus on two significant projects: the ERATO Kawato Dynamic Brain and the ICORP Computational Brain. Discussion revolves around pivotal brain regions, namely the cerebellum, basal ganglia, and cerebrum, and the associated computational models.

Optimal human behavior depends on the expectancy of future events based on perceptual analysis of an individual's present situation using knowledge gained from past experience. This article explores the proposition that the neural processes underlying perceptual analysis, knowledge retrieval, and expectancy are all integrated through the coordination of large-scale networks of the cerebral cortex. It is proposed that expectancy is created when local networks expressing knowledge of the likely future events associated with an individual's present situation are coordinated as part of large-scale networks expressing the totality of knowledge relations concerning the situation.

To unravel the cellular/molecular mechanisms underlying the development of mammalian brain, we have improved the manipulation techniques of embryos/brain tissues whereby various gene expression vectors are easily transferred into the developing nervous system. These techniques allow us to perform time- and region-specific manipulations of candidate genes and are necessary for a better understanding of the roles of such genes in the developing cerebral cortex.

In this chapter, we update our recent two-step model of the evolution of primary, phenomenal, sensory consciousness in vertebrate animals, a model based on neurobiological naturalism (see Chapter 1). The model proposes that this consciousness first appeared in the earliest vertebrates from mental reconstructions at the top of topographically organized neural hierarchies in the optic tectum of the midbrain (for vision and other senses) and in the pallium of the forebrain (for smell perception). Here, we add more information about the optic tectum that relates to such image reconstruction. Second, sensory consciousness leaped forward independently in the first mammals and then in the first birds, to be generated by an expanding dorsal pallium (cerebral cortex). We propose more reasons for this second step, all related to the favorable location of the dorsal pallium at the nexus of all kinds of sensory input, already highly processed, and near the hippocampus so that conscious experiences could be enriched by memories.