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   https://doi.org/10.1117/12.2505295  

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2. Convergent Brain Connectome Analysis with Brain Graph Transformers

   Han, Keqi; Kan, Xuan; Yang, Carl (May 2024, IEEE International Symposium on Biomedical Imaging (ISBI))
3. Protosequences in brain organoids model intrinsic brain states

   https://doi.org/10.1101/2023.12.29.573646  

   van_der_Molen, Tjitse; Spaeth, Alex; Chini, Mattia; Hernandez, Sebastian; Kaurala, Gregory A; Schweiger, Hunter E; Duncan, Cole; McKenna, Sawyer; Geng, Jinghui; Lim, Max; et al (December 2023, bioRxiv)

   Abstract Neuronal firing sequences are thought to be the basic building blocks of neural coding and information broadcasting within the brain. However, when sequences emerge during neurodevelopment remains unknown. We demonstrate that structured firing sequences are present in spontaneous activity of human and murine brain organoids andex vivoneonatal brain slices from the murine somatosensory cortex. We observed a balance between temporally rigid and flexible firing patterns that are emergent phenomena in human and murine brain organoids and early postnatal murine somatosensory cortex, but not in primary dissociated cortical cultures. Our findings suggest that temporal sequences do not arise in an experience-dependent manner, but are rather constrained by an innate preconfigured architecture established during neurogenesis. These findings highlight the potential for brain organoids to further explore how exogenous inputs can be used to refine neuronal circuits and enable new studies into the genetic mechanisms that govern assembly of functional circuitry during early human brain development. 

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4. Integrating Heterogeneous Brain Networks for Predicting Brain Disease Conditions

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5. An Efficient Brain-Switch for Asynchronous Brain-Computer Interfaces

   Valencia, Daniel; Mercier, Patrick; Alimohammad, Amir (May 2024, IEEE transactions on biomedical circuits and systems)

   Intracortical brain computer interfaces (iBCIs) utilizing extracellular recordings mainly employ in vivo signal processing application-specific integrated circuits (ASICs) to detect action potentials (spikes). Conventionally, “brain-switches” based on spiking activity have been employed to realize asynchronous (self-paced) iBCIs, estimating when the user involves in the underlying BCI task. Several studies have demonstrated that local field potentials (LFPs) can effectively replace action potentials, drastically reducing the power consumption and processing requirements of in vivo ASICs. This article presents the first LFP-based brain-switch design and implementation using gated recurrent neural networks (RNNs). Compared to the previously reported brain-switches, our design requires no exhaustive learning phase for the estimation of optimal recording channels or frequency band selection, making it more applicable to practical asynchronous iBCIs. The synthesized ASIC of the designed in vivo LFP-based feature extraction unit, in a standard 180-nm CMOS process, occupies only 0.09 mm^2 of silicon area, and the post place-and-route synthesis results indicate that it consumes 91.87 nW of power while operating at 2 kHz. Compared to the previously published ASICs, the proposed LFP-based brain-switch consumes the least power for in vivo digital signal processing and achieves comparable state estimation performance to that of spike-based brain-switches. 

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