Fabrication of thin-film electrodes and organic electrochemical transistors for neural implants
Related Articles
The influence of the gingival phenotype on implant survival rate and clinical parameters: a systematic review
The goal of this systematic review was to verify whether the gingival phenotype (thick or thin) could impact the dental implant survival rate by affecting the marginal bone.
Advanced electrode processing for lithium-ion battery manufacturing
Lithium-ion batteries (LIBs) need to be manufactured at speed and scale for their use in electric vehicles and devices. However, LIB electrode manufacturing via conventional wet slurry processing is energy-intensive and costly, challenging the goal to achieve sustainable, affordable and facile manufacturing of high-performance LIBs. In this Review, we discuss advanced electrode processing routes (dry processing, radiation curing processing, advanced wet processing and 3D-printing processing) that could reduce energy usage and material waste. Maxwell-type dry processing is a scalable alternative to conventional processing and has relatively low manufacturing cost and energy consumption. Radiation curing processing could enable high-throughput manufacturing, but binder selection is limited to certain radiation curable chemistries. 3D-printing processing can produce electrodes with diverse architectures and improved rate performance, but scalability is yet to be demonstrated. 3D-printing processing is good for special applications where throughput and cost can be compromised for performance.
Flexible micromachined ultrasound transducers (MUTs) for biomedical applications
The use of bulk piezoelectric transducer arrays in medical imaging is a well-established technology that operates based on thickness mode piezoelectric vibration. Meanwhile, advancements in fabrication techniques have led to the emergence of micromachined alternatives, namely, piezoelectric micromachined ultrasound transducer (PMUT) and capacitive micromachined ultrasound transducer (CMUT). These devices operate in flexural mode using piezoelectric thin films and electrostatic forces, respectively. In addition, the development of flexible ultrasound transducers based on these principles has opened up new possibilities for biomedical applications, including biomedical imaging, sensing, and stimulation. This review provides a detailed discussion of the need for flexible micromachined ultrasound transducers (MUTs) and potential applications, their specifications, materials, fabrication, and electronics integration. Specifically, the review covers fabrication approaches and compares the performance specifications of flexible PMUTs and CMUTs, including resonance frequency, sensitivity, flexibility, and other relevant factors. Finally, the review concludes with an outlook on the challenges and opportunities associated with the realization of efficient MUTs with high performance and flexibility.
Innovating beyond electrophysiology through multimodal neural interfaces
Neural circuits distributed across different brain regions mediate how neural information is processed and integrated, resulting in complex cognitive capabilities and behaviour. To understand dynamics and interactions of neural circuits, it is crucial to capture the complete spectrum of neural activity, ranging from the fast action potentials of individual neurons to the population dynamics driven by slow brain-wide oscillations. In this Review, we discuss how advances in electrical and optical recording technologies, coupled with the emergence of machine learning methodologies, present a unique opportunity to unravel the complex dynamics of the brain. Although great progress has been made in both electrical and optical neural recording technologies, these alone fail to provide a comprehensive picture of the neuronal activity with high spatiotemporal resolution. To address this challenge, multimodal experiments integrating the complementary advantages of different techniques hold great promise. However, they are still hindered by the absence of multimodal data analysis methods capable of providing unified and interpretable explanations of the complex neural dynamics distinctly encoded in these modalities. Combining multimodal studies with advanced data analysis methods will offer novel perspectives to address unresolved questions in basic neuroscience and to develop treatments for various neurological disorders.
A unified acoustic-to-speech-to-language embedding space captures the neural basis of natural language processing in everyday conversations
This study introduces a unified computational framework connecting acoustic, speech and word-level linguistic structures to study the neural basis of everyday conversations in the human brain. We used electrocorticography to record neural signals across 100 h of speech production and comprehension as participants engaged in open-ended real-life conversations. We extracted low-level acoustic, mid-level speech and contextual word embeddings from a multimodal speech-to-text model (Whisper). We developed encoding models that linearly map these embeddings onto brain activity during speech production and comprehension. Remarkably, this model accurately predicts neural activity at each level of the language processing hierarchy across hours of new conversations not used in training the model. The internal processing hierarchy in the model is aligned with the cortical hierarchy for speech and language processing, where sensory and motor regions better align with the model’s speech embeddings, and higher-level language areas better align with the model’s language embeddings. The Whisper model captures the temporal sequence of language-to-speech encoding before word articulation (speech production) and speech-to-language encoding post articulation (speech comprehension). The embeddings learned by this model outperform symbolic models in capturing neural activity supporting natural speech and language. These findings support a paradigm shift towards unified computational models that capture the entire processing hierarchy for speech comprehension and production in real-world conversations.
Responses