神经芯片解码大脑区域之间的相互作用
EPFL研究者开发出可植入神经芯片来跟踪跨大脑区域的通信，实时识别认知和情感状态。
深脑刺激(DBS)是一种治疗技术，通过植入来将电脉冲传递到大脑的特定区域来调节神经活性。 它已经被广泛用于诸如帕金森病之类的运动障碍，研究人员正在越来越多地探索它可能出现严重精神病，包括重大抑郁症、创伤后应激障碍和强迫症。
然而，大多数现有的DBS系统以工程师所谓的“开放循环”方式运作：刺激是根据预先定义的设置提供的，而不是不断适应病人的大脑活动。 即使在闭路或适应系统中，许多人都依赖于单个大脑区域记录的信号。 这是精神疾病的一个主要限制，在这种疾病中，与临床相关的信息可能分布在相互作用的大脑区域网络中。
“我们的工作是基于越来越多的研究表明，精神障碍更多地来自脑区域之间通信中断，而不是任何单一区域的功能失调，”
Shoaran和她的团队发展出一种低能可植入的神经系统-芯片。 Dubbed LogSync，它记录了大脑活性并提取了描述脑波强度(振荡)的生物标记和来自不同大脑区域的信号之间的同步。 然后一种机器学习算法分析这些网络层次的生物标记，以区分认知和情感状态，并在检测出特定状态时产生刺激触发。
这部作品发表在IEEE《固体态电路学报》上，是美国国家心理健康研究所(NIMH)660万瑞士法郎赠款的一部分，用于开发能够测量和控制严重精神疾病，特别是情绪和焦虑症的振荡性脑网的可植入系统。 它以实验室之前关于脑到文字的解码和闭锁-放电神经调制的工作为基础，将这些概念扩展到网络层面的生物标记和未来精神疗法的适应性解码。
更有针对性的低能刺激
研究人员利用以前精神病学实验中的人脑录音来评价LogSync. 一个数据集在完成一项精神任务时记录了认知参与的变化，而另一个数据集在参与者观看视频剪辑时记录了情绪激动。 算法将这些认知状态和情感状态单独与神经录音区分开来。 在明尼苏达大学对大鼠进行的进一步实验中，植入物跟踪了大脑活动预先确定的阶段正在进行的振荡并产生刺激触发。
Shoaran 称的主要挑战是实现所有这些功能，同时在植入系统非常有限的动力预算范围内工作。
“我们的整个系统是共同设计的，即算法和硬件是与植入的制约一起开发的。 这个方法帮助我们实现网络层面的神经信号处理和单一芯片上的解码，同时将功率消耗保持在200微瓦以下——这是许多可植入神经介质系统通常需要的一小部分，"她称。 另一个创新是LogSync使用对数计算，简化了大脑网络分析和实时生物标记提取和解码所需的复杂操作。
LogSync最初的验证侧重于精神病学应用。 但提取脑网络生物标记以精确地触发定时刺激的能力广泛适用于下一代神经技术，包括适应运动障碍的DBS，闭路非侵入刺激，以及新出现的有重点-超声接口。
与此同时，实验室正在开发新的机器学习方法，直接从神经记录而不是预先定义的生物标记中学习。 他们最近的ICML 2026工作，利用自我监督的学习来捕捉跨区域，跨渠道，跨时间的神经活性，支持更广泛的努力来使智能神经界面更接近临床应用，包括通过由Shoaran共同创立的EPFL副作用Infera Neuro.
美国国家心理健康研究所
瑞士教育、研究和创新国务秘书处(SERI)资助
D. Alex等，"LogSync：用于跨区域同步提取和适应性精神病国家分类的低功率对称SoC",IEEE Journal of Solid-State Circuits (JSSC), September 2026 doi:10.11109/JSSC.2026.3728617.
J.Liu和M.Shoaran,“RECTOR:Masked Region-Channel-Temporal Modeling for Affective and Conditional Relections (ICML)”，机器学习国际会议，2026年 https://proceedings.mlr.press/v306/liu26dt.html
D. Alex等，"大脑网络连通性提取和适应性精神病症状分类的32-Channel 196-μW对称SoC",2025年关于VLSI技术和电路(VLSI Technology and Circuits)的专题讨论会，日本京都，2025年，第1-3页 doi:10.23919/VLSITechnologyand Cir65189.2025.11075022.
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Neural chip decodes interactions between brain regions
EPFL researchers have developed an implantable neural chip that tracks communication across brain regions, identifying cognitive and emotional states in real time.
Deep brain stimulation (DBS) is a therapeutic technique that delivers electrical pulses to specific areas of the brain via an implant to modulate neural activity. It is already widely used for movement disorders such as Parkinson’s disease, and researchers are increasingly exploring its potential for severe psychiatric illnesses including major depression, post-traumatic stress disorder, and obsessive-compulsive disorder.
However, most existing DBS systems operate in what engineers call an “open loop” manner: stimulation is delivered according to predefined settings rather than continuously adapting to the patient’s brain activity. Even among closed-loop, or adaptive systems, many rely on signals recorded from a single brain region. This is a major limitation for psychiatric disorders, where clinically relevant information may be distributed across networks of interacting brain regions.
“Our work is based on the growing body of research suggesting that psychiatric disorders arise more from disruptions in communication between brain regions than from dysfunction of any single region,” explains Mahsa Shoaran, head of the Integrated Neurotechnologies Lab in EPFL’s School of Engineering and Neuro-X Institute.
Shoaran and her team have developed a low-power, implantable neural system-on-chip. Dubbed LogSync, it records brain activity and extracts biomarkers that describe the strength of brain waves (oscillations) and synchrony between signals from different brain regions. A machine-learning algorithm then analyzes these network-level biomarkers to distinguish cognitive and emotional states, and generate stimulation triggers when specific states are detected.
The work, published in the IEEE Journal of Solid-State Circuits, was developed as part of a CHF 6.6 million grant from the US National Institute for Mental health (NIMH) to develop implantable systems capable of measuring and controlling oscillatory brain networks for severe psychiatric illnesses, particularly mood and anxiety disorders. It builds on the lab's previous work on brain-to-text decoding and closed-loop neuromodulation, extending these concepts to network-level biomarkers and adaptive decoding for future psychiatric therapies.
More targeted stimulation at lower power
The researchers evaluated LogSync using human brain recordings from previous psychiatric experiments. One dataset captured changes in cognitive engagement while performing a mental task, while another captured emotional arousal as participants viewed video clips. The algorithm distinguished these cognitive and emotional states from the neural recordings alone. In further experiments conducted in rats at the University of Minnesota, the implant tracked ongoing oscillations and generated stimulation triggers at predefined phases of brain activity.
Shoaran says that major challenge was achieving all this functionality while working within the very limited power budget of an implantable system.
“Our entire system is co-designed, meaning the algorithms and hardware are developed together with the constraints of implant in mind. This approach helped us achieve network-level neural signal processing and decoding on a single chip, while keeping power consumption under 200 microwatts — a fraction of that typically required by many implantable neural-interface systems,” she says. Another innovation is LogSync’s use of logarithmic computations, which simplify the complex operations needed for brain-network analysis and real-time biomarker extraction and decoding.
LogSync's initial validation focused on psychiatric applications. But the capability to extract brain-network biomarkers to trigger precisely timed stimulation is broadly applicable across next-generation neurotechnology, including adaptive DBS for movement disorders, closed-loop non-invasive stimulation, and emerging focused-ultrasound interfaces.
In parallel, the laboratory is developing new machine-learning methods that learn directly from neural recordings rather than predefined biomarkers. Their recent ICML 2026 work, which uses self-supervised learning to capture neural activity across regions, channels, and time, supports broader efforts to bring intelligent neural interfaces closer to clinical applications, including through Infera Neuro, an EPFL spin-off co-founded by Shoaran.
United States National Institute of Mental Health
Swiss State Secretariat for Education, Research, and Innovation (SERI)-funded ERC Starting Grant
D. Alex et al., "LogSync: A Low-Power Logarithmic SoC for Cross-Regional Synchrony Extraction and Adaptive Psychiatric State Classification," IEEE Journal of Solid-State Circuits (JSSC), Sept 2026 doi: 10.1109/JSSC.2026.3728617.
J. Liu and M. Shoaran, “RECTOR: Masked Region-Channel-Temporal Modeling for Affective and Cognitive Representation Learning”, International Conference on Machine Learning (ICML), 2026 https://proceedings.mlr.press/v306/liu26dt.html
D. Alex et al., "A 32-Channel 196-μW Logarithmic SoC for Brain Network Connectivity Extraction and Adaptive Psychiatric Symptom Classification," 2025 Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits), Kyoto, Japan, 2025, pp. 1-3, doi: 10.23919/VLSITechnologyandCir65189.2025.11075022.
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