搜索结果：brain-destroying

Brain foundation models (BFMs) are self-supervised Transformers pretrained on fMRI data. We posit that these models should capture each subject's cognitive performance from their fMRI signal. Yet across three state-of-the-art BFMs and every readout we test, they predict cognition worse than a linear regression from the $\sim$80K parameters of the functional connectivity matrix (FC). The gap widens with scale: BrainLM's 650M model predicts cognition worse than its 111M. We attribute this to a \textbf{variance allocation problem}: BFM pretraining captures the variance components that dominate fMRI but not the higher-order structure that predicts cognition. Our per-cumulant analysis of the reconstructed signal shows that the second-order covariance is partially preserved, while the third-order co-skewness tensor is largely destroyed. To recover what BFMs lose, we design a linear pipeline that projects the fMRI signal into the subspace that best preserves its co-skewness and computes FC there. This \textbf{exceeds raw FC and every pretrained BFM} on every dataset and parcellation we test, outperforming prior state-of-the-art under controlled evaluation \textbf{with no pretraining and n

Deploying a medical imaging model that must later accommodate a modality it has never seen is a recurring practical problem: retraining the shared representation is expensive and destroys performance on the modalities already in service. We study this \emph{onboarding} problem under a strict leave-one-domain-out protocol, in which a convolutional backbone is pre-trained on source modalities (Kidney CT and Brain MRI), frozen permanently, and then required to accommodate an unseen modality (Chest X-ray). Under this protocol we establish three findings. First, decision-layer parameter-efficient fine-tuning is insufficient when the backbone has never observed the target modality: a linear probe and fully-connected LoRA both fall well short, whereas convolutional LoRA recovers most of the achievable accuracy, showing that adaptation must reach the convolutional features. Second, and centrally, the low-rank convolutional \emph{basis} learned on the source modalities \emph{transfers}: freezing that basis and training only its up-projections onboards the unseen modality using just $0.78\%$ of full fine-tuning's parameters, at an accuracy $6.11$ percentage points above a random basis of ide

In a series of works of ours we have shown that we can represent the critical and tricritical points of the Statistical Physics of critical phenomena as a Dynamical phenomenon expressed by time series produced by the type I intermittency that exhibits a weak chaos. Recently we have also shown that if we couple these two chaotic dynamics, namely critical and tricritical, we can produce a time sequence which is a temporal Spike Train (ST) of biological-type . In the present work we generalize this issue producing superpositions of critical-tricritical intermittencies with different parameter values. Now arise the question whether the coupling occurs between time series that have resulted from the superposition, will preserved or destroyed the ST biological type , as the number of intermittencies in the superposition will increase? In the other side in present work we find that the spikes produced by the chaotic dynamics of the intermittencies, under the action of superpositions and coupling remain biological-type too. Thus we can say that the dynamics of the fluctuations of the values of the time series produced by the coupling of the superpositions of the intermittencies is the same

Deploying spiking neural networks (SNNs) on neuromorphic hardware demands aggressive synaptic pruning while preserving temporal computation integrity. Existing strategies either neglect neuronal criticality or rely on convex relaxations of the inherently combinatorial pruning problem whose fractional masks, upon binarisation, destroy accuracy at moderate-to-high sparsity. We present Criticality-Constrained Quadratic Pruning (CQP), a native PyTorch pipeline that fuses weight magnitude with surrogate-gradient criticality into an analytically exact importance metric, eliminating the rounding artefacts endemic to solver-based approaches. We formally characterise a continuous-relaxation trap wherein OSQP-solver fractional masks overshoot the intended sparsity by up to 12 percentage points (pp), precipitating a 44 pp accuracy collapse. We identify and remediate a zombie-weight failure mode in which Adam's first-moment tensors resurrect pruned synapses, violating the binary sparsity guarantee. An iterative schedule - prune, fine-tune with gradient masking, recompute criticality, and repeat - eliminates gradient staleness at high sparsity. A KL-divergence temporal analysis identifies a red

Brain-computer interfaces aim to decode naturalistic stimuli from neural signals, yet most progress to date has focused on vision and language. In this article, we study a more challenging but far less explored setting, EEG-to-music reconstruction, where signals are weak, distributed, and highly susceptible to noise and channel variability. Our central finding is that early channel mixing destroys weak but discriminative EEG signals. To address this, we propose a channel-oriented design with three key components. Specifically, channel-wise tokenization treats each electrode as an explicit token to retain spatially localized neural evidence, channel-wise multi-view self-distillation enforces consistency across temporal crops and random channel subsets to learn robust and distributed representations, and channel-wise data augmentation introduces structured channel dropout to improve invariance to noise, artifacts, and missing electrodes. Together, these components preserve weak yet informative signals across channels and enable stable alignment to a semantic music representation space. We integrate this channel-oriented design within an encoding-alignment-decoding pipeline for EEG-to

Emulating the neural-like information processing dynamics of the brain provides a time and energy efficient approach for solving complex problems. While the majority of neuromorphic hardware currently developed rely on large arrays of highly organized building units, such as in rigid crossbar architectures, in biological neuron assemblies make use of dynamic transitions within highly parallel, reconfigurable connection schemes. Neuroscience suggests that efficiency of information processing in the brain rely on dynamic interactions and signal propagations which are self-tuned and non-rigid. Brain-like dynamic and avalanche criticality have already been found in a variety of self-organized networks of nanoobjects, such as nanoparticles (NP) or nanowires. Here we report on the dynamics of the electrical spiking signals from Ag-based self-organized nanoparticle networks (NPNs) at the example of monometallic Ag NPNs, bimetallic AgAu alloy NPNs and composite Ag/ZrN NPNs, which combine two distinct NP species. We present time series recordings of the resistive switching responses in each network and showcase the determination of switching events as well as the evaluation of avalanche cri

The "criticality hypothesis", based on observed scale-free correlations in neural activity, posits that the brain operates at a critical point of transition between two phases. However, the validity of this hypothesis is still debated. Here, employing a commonly used model of cortical dynamics, we find that a phase of long-range order (LRO) in neural activity may be induced by memory (time non-locality) without invoking criticality. The cortical dynamics model contains fast and slow time scales that govern the neural and resource (memory) dynamics, respectively. When the resource dynamics are sufficiently slow, we observe an LRO phase, which manifests in avalanche size and duration probability distributions that are fit well by power laws. When the slow and fast time scales are no longer sufficiently distinct, LRO is destroyed. Since this LRO phase spans a wide range of parameters, it is robust against perturbations, unlike critical systems.

Cosmological scenarios wherein the cumulative number of spontaneously formed, cognitively impaired, disembodied transient observers is vastly larger than the corresponding number of atypical `ordinary observers' (OOs) formed in the conventional way -- essentially via cosmic evolution and gravitational instability -- are disqualified in modern cosmology on the grounds of Cognitive Instability -- the untrustworsiness of one own's reasoning -- let alone the atypicality of OOs like us. According to the concordance $Λ$CDM cosmological model -- when described in the (expanding) `cosmic frame' -- the cosmological expansion is future-eternal. In this frame we are atypical OOs, which are vastly outnumbered by typical Boltzmann Brains (BBs) that spontaneously form via sheer thermal fluctuations in the future-eternal asymptotic de Sitter spacetime. In the case that dark energy (DE) ultimately decays, the cumulative number of transient `Freak Observers' (FOs) formed and destroyed spontaneously by virtue of the quantum uncertainty principle ultimately overwhelms that of OOs. Either possibility is unacceptable. We argue that these unsettling conclusions are artifacts of employing the (default) c

Alzheimer's disease is a degenerative brain disease. Being the primary cause of Dementia in adults and progressively destroys brain memory. Though Alzheimer's disease does not have a cure currently, diagnosing it at an earlier stage will help reduce the severity of the disease. Thus, early diagnosis of Alzheimer's could help to reduce or stop the disease from progressing. In this paper, we proposed a deep convolutional neural network-based model for learning model using to determine the stage of Dementia in adults based on the Magnetic Resonance Imaging (MRI) images to detect the early onset of Alzheimer's.

It took just hours for the leading pharma group to reject the recommendations

NASA’s Curiosity rover has entered a Martian valley covered by an astonishing “sea” of tiny polygon-shaped fractures。 The honeycomb patterns, each only a few inches wide, stretch across the landscape and even wrap around a nearby 20-foot-tall butte

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Device-bound session credentials thwart an increasingly common form of account takeover

When atomic nuclei smash together at nearly the speed of light, they create the hottest fluid in the universe, but scientists may have overlooked one of its most important forces。 New simulations reveal that extreme acceleration builds up along the edges of quark-gluon plasma, helping drive its explosive expansion。 This acceleration may do far more

A black hole observed during a dramatic 2023 eruption did not simply devour gas from its nearby companion star。 It also expelled large amounts of material through powerful jets and winds, even after the outburst had nearly faded。 The results suggest black holes may continue reshaping their surroundings long after their brightest fireworks end

A person’s sleeping brain may reveal warning signs of dementia long before memory problems begin。 Researchers used machine learning to analyze EEG recordings from about 7,000 adults and found that an older-than-expected “brain age” was tied to a sharply higher dementia risk。 Every additional 10 years of brain aging raised that risk by nearly 40%

“ShieldFont” aims to poison AI training data without making pages unreadable for people