The mechanisms underlying human cell diversity are unclear. Here the authors provide a single-cell epigenome map of human neural organoid development and dissect how epigenetic changes control cell fate specification from pluripotency to distinct cerebral and retina neural types.
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We present a developmental atlas that offers insight into sequential epigenetic changes underlying early human brain development modeled in organoids, which reconstructs the differentiation trajectories of all major CNS regions. It shows that epigenetic regulation via the installation of activating histone marks precedes activation of groups of neuronal genes.
Miniaturized electrode caps are fabricated and used for 3D electrical recording from brain organoids.
To capture a broader understanding of memory encoding, we expanded our experiments to include two other stimulus types: colors and face pictures (see Materials and Methods). Both monkeys demonstrated high accuracy in memorizing grating orientations in the “orientation DMTS” task, colors in the “color DMTS” task, and face pictures in the “face DMTS” task [DP: ~94% and DQ: ~87% versus 50%, all P < 0.01 (one-sample t test)] (fig. S1), indicating that they had been well trained.
We implanted a Utah array in each monkey’s V1 area (see Materials and Methods; Fig. 1B) and presented the stimuli onto the receptive field (RF) centers of the recorded neurons (fig. S2, A and D). This enabled simultaneous monitoring of neuronal activity in our experiments. Our analyses focused primarily on neuronal activity before probe stimulus onset.
Representative neuronal responses for two of the VWM content conditions in the orientation DMTS task at a selected electrode are shown in Fig. 1C. During the stimulus period (0 to 200 ms after cue onset), neurons displayed distinct firing patterns between the two content conditions (90° or 180° orientation). An off-response emerged following the cue offset, and activity gradually diminished. During the delay period, defined as 700 to 1,700 ms after cue onset (the thick gray line in Fig. 1C), neurons also exhibited a significant difference in firing rate between the two content conditions (N = 1,810 trials for 90°; N = 1,865 trials for 180°; all marked positions P < 0.01) without any behavioral performance bias (N = 16 sessions, P = 0.94; right panel in Fig. 1C). The difference in response between these two content conditions during the delay period at the same electrode was less prominent in incorrect-response trials and in the fixation task (Fig. 1D).
Brain-on-a-chip models, mimicking brain physiology, hold promise for developing treatments for neurological disorders. This Review discusses the engineering challenges and opportunities for these devices, including the integration of 3D cell cultures and electrodes and scaffold engineering strategies.
Daiki Nishioka and colleagues show a nanodevice implementation of deep reservoir computing using an ion-gating reservoir, achieving record-low error rates on a complex computational task. This device is more efficient and scalable for brain-like computing systems exploiting physical systems.
One of the ambitions of computational neuroscience is that we will continue to make improvements in the field of artificial intelligence that will be informed by advances in our understanding of how the brains of various species evolved to process information. To that end, here the authors propose an expanded version of the Turing test that involves embodied sensorimotor interactions with the world as a new framework for accelerating progress in artificial intelligence.
Beaubois et al. introduce a real-time biomimetic neural network for biohybrid experiments, providing a tool to study closed-loop applications for neuroscience and neuromorphic-based neuroprostheses.
Nearly all the neural networks that power modern artificial intelligence tools such as ChatGPT are based on a 1960s-era computational model of a living neuron. A new model developed at the Flatiron Institute’s Center for Computational Neuroscience (CCN) suggests that this decades-old approximation doesn’t capture all the computational abilities that real neurons possess and that this older model is potentially holding back AI development.
