マイクロおよびマクロ表情のスポッティングにおける任意の単一フレームを用いた弱教示

e-syn-e-graph-rewriting-for-logic-synthesis-with-technology-aware-cost-functions

dermacen-analytica-integrating-large-language-models-in-dermatology

リアルタイム放射輝度場レンダリングのための等方性ガウススプラッティング

layoutllm-large-language-model-instruction-tuning-for-visually-rich-document-understanding

erd-a-framework-for-improving-llm-reasoning-for-cognitive-distortion-classification

k-act2emo-korean-commonsense-knowledge-graph-for-indirect-emotional-expression

特許からの矛盾のllmベース抽出


통증 기대의 생성과 통증 지각 간의 통합에 대한 신경 기제를 탐구하였다. 기대 생성 단계와 통증 지각 단계에서 서로 다른 신경 표현이 관찰되었다.


coremsg

Pain is heavily modulated by expectations. Whereas the integration of expectations with sensory information has been examined in some detail, little is known about how positive and negative expectations are generated and their neural dynamics from generation over anticipation to the integration with sensory information. The present preregistered study employed a novel paradigm to induce positive and negative expectations on a trial-by-trial basis and examined the neural mechanisms using combined EEG-fMRI measurements (n=50). We observed substantially different neural representations between the anticipatory and the actual pain period. In the anticipation phase i.e., before the nociceptive input, the insular cortex, dorsolateral prefrontal cortex (DLPFC), and anterior cingulate cortex (ACC) showed increased activity for expectations regardless of their valence. Interestingly, a differentiation between positive and negative expectations within the majority of areas only occurred after the arrival of nociceptive information. FMRI-informed EEG analyses could reliably track the temporal sequence of processing showing an early effect in the DLPFC, followed by the anterior insula and late effects in the ACC. The observed effects indicate the involvement of different expectation-related subprocesses, including the transformation of visual information into a value signal that is maintained and differentiated according to its valence only during stimulus processing.

### Competing Interest Statement

The authors have declared no competing interest.

The neural dynamics of positive and negative expectations of pain

긍정적-및-부정적-통증-기대의-신경-역학


긍정적 및 부정적 통증 기대의 신경 역학


title_rewrite


해마 장소 세포 중 빠른 감마 리듬에 강하게 동기화된 세포군(FG-세포)이 세타 순차 발달에 핵심적인 역할을 하며, 이는 느린 감마 리듬의 위상 선행 현상과 관련되어 있다.


The experience-dependent spatial cognitive process requires sequential organization of hippocampal neural activities by theta rhythm, which develops to represent highly compressed information for rapid learning. However, how the theta sequences were developed in a finer time scale within theta cycles remains unclear. In this study, we found that sweep-ahead structure of theta sequences developing with exploration was predominantly dependent on a relatively large proportion of FG-cells, i.e. a subset of place cells dominantly phase-locked to fast gamma rhythms. These ensembles integrated compressed spatial information entrained in a theta sequence by cells consistently firing at precessing slow gamma phases within the theta cycle. Accordingly, the sweep-ahead structure of FG-cell sequences was positively correlated with the intensity of slow gamma phase precession, in particular during early sequence development. These findings highlight the dynamic network-modulation by fast and slow gamma in the development of theta sequences which may further facilitate memory encoding and retrieval.

### Competing Interest Statement

The authors have declared no competing interest.

Dynamic Gamma Modulation of Hippocampal Place Cells Predominates Development of Theta Sequences

동물의-경험에-따른-해마-장소-세포의-감마-리듬-변조가-세타-순차-발달을-주도한다


동물의 경험에 따른 해마 장소 세포의 감마 리듬 변조가 세타 순차 발달을 주도한다



순환 시간 제한 볼츠만 기계(RTRBM)는 확률적 탐색과 목표 지향적 행동을 모두 포착할 수 있는 통합적인 신경 활동 모델을 제공한다.


Animal behaviour alternates between stochastic exploration and goal-directed actions, which are generated by the underlying neural dynamics. Previously, we demonstrated that the compositional Restricted Boltzmann Machine (cRBM) can decompose whole-brain activity of larval zebrafish data at the neural level into a small number (∼100-200) of assemblies that can account for the stochasticity of the neural activity (van der Plas et al., eLife, 2023). Here we advance this representation by extending to a combined stochastic-dynamical representation to account for both aspects using the Recurrent Temporal RBM (RTRBM) and transfer-learning based on the cRBM estimate. We demonstrate that the functional advantage of the RTRBM is captured in the temporal weights on the hidden units, representing neural assemblies, both in simulated and experimental data. Our results show that the temporal expansion outperforms the stochastic-only cRBM in terms of generalisation error and achieves more accurate representation of the moments in time. Lastly, we demonstrate that we can identify the original time-scale of assembly dynamics, by estimating multiple RTRBMs at different temporal resolutions. Together, we propose that RTRBMs are a valuable tool for capturing the combined stochastic and time-predictive dynamics of large-scale data sets.

### Competing Interest Statement

The authors have declared no competing interest.

The Recurrent Temporal Restricted Boltzmann Machine Captures Neural Assembly Dynamics in Whole-brain Activity

신경-조립체-역학을-전체-뇌-활동에-포착하는-순환-시간-제한-볼츠만-기계


신경 조립체 역학을 전체 뇌 활동에 포착하는 순환 시간 제한 볼츠만 기계



딥러닝 기반 시냅스 이벤트 탐지 기법 miniML은 정확하고 신뢰할 수 있는 자동화된 시냅스 이벤트 분석을 가능하게 한다.


Quantitative information about synaptic transmission is key to our understanding of neural function. Spontaneously occurring synaptic events carry fundamental information about synaptic function and plasticity. However, their stochastic nature and low signal-to-noise ratio present major challenges for the reliable and consistent analysis. Here, we introduce miniML, a supervised deep learning-based method for accurate classification and automated detection of spontaneous synaptic events. Comparative analysis using simulated ground-truth data shows that miniML outperforms existing event analysis methods in terms of both precision and recall. miniML enables precise detection and quantification of synaptic events in electrophysiological recordings. We demonstrate that the deep learning approach generalizes easily to diverse synaptic preparations, different electrophysiological and optical recording techniques, and across animal species. miniML provides not only a comprehensive and robust framework for automated, reliable, and standardized analysis of synaptic events, but also opens new avenues for high-throughput investigations of neural function and dysfunction.

### Competing Interest Statement

The authors have declared no competing interest.

A deep learning framework for automated and generalized synaptic event analysis

딥러닝-기반-시냅스-이벤트-탐지-기법


딥러닝 기반 시냅스 이벤트 탐지 기법



균형 잡힌 시냅스 조절은 신경 집단 코드 모델의 학습을 최적화한다.


Studying and understanding the code of large neural populations hinge on accurate statistical models of population activity. A novel class of models, based on learning to weigh sparse nonlinear Random Projections (RP) of the population, has demonstrated high accuracy, efficiency, and scalability. Importantly, these RP models have a clear and biologically-plausible implementation as shallow neural networks. We present a new class of RP models that are learned by optimizing the randomly selected sparse projections themselves. This “reshaping” of projections is akin to changing synaptic connections in just one layer of the corresponding neural circuit model. We show that Reshaped RP models are more accurate and efficient than the standard RP models in recapitulating the code of tens of cortical neurons from behaving monkeys. Incorporating more biological features and utilizing synaptic normalization in the learning process, results in even more efficient and accurate models. Remarkably, these models exhibit homeostasis in firing rates and total synaptic weights of projection neurons. We further show that these sparse homeostatic reshaped RP models outperform fully connected neural network models. Thus, our new scalable, efficient, and highly accurate population code models are not only biologically-plausible but are actually optimized due to their biological features. These findings suggest a dual functional role of synaptic normalization in neural circuits: maintaining spiking and synaptic homeostasis while concurrently optimizing network performance and efficiency in encoding information and learning.

### Competing Interest Statement

The authors have declared no competing interest.

Homeostatic synaptic normalization optimizes learning in network models of neural population codes

대규모-신경-집단-코드에-대한-최적의-균형-있는-시냅스-조절


대규모 신경 집단 코드에 대한 최적의 균형 있는 시냅스 조절



신경 코드의 삼원성은 오류 신호와 선명화 신호를 나타내며, 이는 주의력과 학습에 대한 이론을 뒷받침한다.


Theories of attention and learning have hypothesized a central role for high-frequency bursting in cognitive functions, but experimental reports of burst-mediated representations in vivo have been limited. Here we used a novel demultiplexing approach by considering a conjunctive burst code. We studied this code in vivo while animals learned to report direct electrical stimulation of the somatosensory cortex and found two acquired yet independent representations. One code, the event rate, showed a sparse and succint stiumulus representation and a small modulation upon detection errors. The other code, the burst fraction, correlated more globally with stimulation and more promptly responded to detection errors. Bursting modulation was potent and its time course evolved, even in cells that were considered unresponsive based on the firing rate. During the later stages of training, this modulation in bursting happened earlier, gradually aligning temporally with the representation in event rate. The alignment of bursting and event rate modulation sharpened the firing rate response, and was strongly associated behavioral accuracy. Thus a fine-grained separation of spike timing patterns reveals two signals that accompany stimulus representations: an error signal that can be essential to guide learning and a sharpening signal that could implement attention mechanisms.

### Competing Interest Statement

The authors have declared no competing interest.

Fast burst fraction transients convey information independent of the firing rate

신경-코드의-삼원성이-오류-및-선명화-신호를-해결한다


신경 코드의 삼원성이 오류 및 선명화 신호를 해결한다



신경심리학자 AI 시스템은 ROCF 테스트 결과를 자동으로 신뢰성 있게 채점할 수 있다.


Background Memory deficits are a hallmark of many different neurological and psychiatric conditions. The Rey-Osterrieth complex figure (ROCF) is the state–of-the-art assessment tool for neuropsychologists across the globe to assess the degree of non-verbal visual memory deterioration. To obtain a score, a trained clinician inspects a patient’s ROCF drawing and quantifies deviations from the original figure. This manual procedure is time-consuming, slow and scores vary depending on the clinician’s experience, motivation and tiredness.

Methods Here, we leverage novel deep learning architectures to automatize the rating of memory deficits. For this, we collected more than 20k hand-drawn ROCF drawings from patients with various neurological and psychiatric disorders as well as healthy participants. Unbiased ground truth ROCF scores were obtained from crowdsourced human intelligence. This dataset was used to train and evaluate a multi-head convolutional neural network.

Results The model performs highly unbiased as it yielded predictions very close to the ground truth and the error was similarly distributed around zero. The neural network outperforms both online raters and clinicians. The scoring system can reliably identify and accurately score individual figure elements in previously unseen ROCF drawings, which facilitates explainability of the AI-scoring system. To ensure generalizability and clinical utility, the model performance was successfully replicated in a large independent prospective validation study that was pre-registered prior to data collection.

Conclusions Our AI-powered scoring system provides healthcare institutions worldwide with a digital tool to assess objectively, reliably and time-efficiently the performance in the ROCF test from hand-drawn images.

### Competing Interest Statement

The authors have declared no competing interest.

Automating clinical assessments of memory deficits: Deep Learning based scoring of the Rey-Osterrieth Complex Figure

신경심리학자-ai-심층-학습을-통한-기억-결핍의-자동-채점


신경심리학자 AI: 심층 학습을 통한 기억 결핍의 자동 채점



FOXG1은 신경줄기세포의 퇴출 과정에서 FoxO6를 활성화하여 중요한 역할을 한다.


The molecular mechanisms controlling the balance of quiescence and proliferation in adult neural stem cells (NSCs) are often deregulated in brain cancers such as glioblastoma (GBM). Previously, we reported that FOXG1, a forebrain-restricted neurodevelopmental transcription factor, is frequently upregulated in glioblastoma stem cells (GSCs) and limits the effects of cytostatic pathways, in part by repression of the tumour suppressor Foxo3 . Here, we show that increased FOXG1 upregulates FoxO6 , a more recently discovered FoxO family member with potential oncogenic functions. Although genetic ablation of FoxO6 in proliferating NSCs has no effect on the cell cycle or entry into quiescence, we find that FoxO6 -null NSCs can no longer efficiently exit quiescence following FOXG1 elevation. Increased FoxO6 results in the formation of large acidic vacuoles, reminiscent of Pak1-regulated macropinocytosis. Consistently, Pak1 expression is upregulated by FOXG1 overexpression and downregulated upon FoxO6 loss in proliferative NSCs. These data suggest a pro-oncogenic role for FoxO6 in controlling the exit from quiescence in NSCs, and shed light on the functions of this underexplored FoxO family member.

Research highlights 

### Competing Interest Statement

The authors have declared no competing interest.

Elevated FOXG1 supports exit from quiescence in neural stem cells through FoxO6

신경줄기세포의-퇴출에-foxg1이-지지하는-foxo6의-역할


신경줄기세포의 퇴출에 FOXG1이 지지하는 FoxO6의 역할



마우스 하부 언덕 신경 집단의 단일 시행 반응 패턴이 소음에도 불구하고 청각 공간 정보를 효과적으로 인코딩한다.


Sound location coding has been extensively studied at the central nucleus of the mammalian inferior colliculus (CNIC), supporting a population code. However, this population code has not been extensively characterized on the single-trial level with simultaneous recordings or at other anatomical regions like the dorsal cortex of inferior colliculus (DCIC), which is relevant for learning-induced experience dependent plasticity. To address these knowledge gaps, here we made in two complementary ways large-scale recordings of DCIC populations from awake mice in response to sounds delivered from 13 different frontal horizontal locations (azimuths): volumetric two-photon calcium imaging with ∼700 cells simultaneously recorded at a relatively low temporal resolution, and high-density single-unit extracellular recordings with ∼20 cells simultaneously recorded at a high temporal resolution. Independent of the method, the recorded DCIC population responses revealed substantial trial-to-trial variation (neuronal noise) which was significantly correlated across pairs of neurons (noise correlations) in the passively listening condition. Nevertheless, decoding analysis supported that these noisy response patterns encode sound location on the single-trial basis, reaching errors that match the discrimination ability of mice. The detected noise correlations contributed to minimize the error of the DCIC population code of sound azimuth. Altogether these findings point out that DCIC can encode sound location in a similar format to what has been proposed for CNIC, opening exciting questions about how noise correlations could shape this code in the context of cortico-collicular input and experience dependent plasticity.

### Competing Interest Statement

The authors have declared no competing interest.

Noisy neuronal populations effectively encode sound localization in the dorsal inferior colliculus of awake mice

청각-공간-정보-처리를-위한-마우스-하부-언덕-신경-집단의-효과적인-소음-코딩


청각 공간 정보 처리를 위한 마우스 하부 언덕 신경 집단의 효과적인 소음 코딩



유연한 의사결정 과정에서 감각 평가와 행동 선택이라는 서로 다른 신경 과정 간의 복잡한 상호작용이 선택성 특화 피드백 연결에 의해 매개된다.


Neural activity in the primate brain correlates with both sensory evaluation and action selection aspects of decision-making. However, the intricate interaction between these distinct neural processes and their impact on decision behaviors remains unexplored. Here, we examined the interplay of these decision processes in posterior parietal cortex (PPC) when monkeys performed a flexible decision task. We found that the PPC activity related to monkeys’ abstract decisions about visual stimuli was nonlinearly modulated by monkeys’ following saccade choices directing outside each neuron’s response field. Recurrent neural network modeling indicated that the feedback connections, matching the learned stimuli-response associations during the task, mediated such feedback modulation. Further analysis on network dynamics revealed that selectivity-specific feedback connectivity intensified the attractor basins of population activity underlying saccade choices, thereby increasing the consistency of flexible decisions. These results highlight an iterative computation between different decision processes, mediated primarily by precise feedback connectivity, contributing to the optimization of flexible decision-making.

### Competing Interest Statement

The authors have declared no competing interest.

Nonlinear feedback modulation contributes to the optimization of flexible decision-making

유연한-의사결정-과정에서-선택성-특화-피드백-연결이-서로-다른-신경-과정의-상호작용을-매개한다


유연한 의사결정 과정에서 선택성 특화 피드백 연결이 서로 다른 신경 과정의 상호작용을 매개한다
