You're viewing the US edition You can switch to the UK or AU homepage at any time using this menu. Terrified electrician trapped for hours inside 130-foot high gas station sign recorded goodbye message to wife and kids: 'It was my final words' Was Meghan's supposed role in Guy Ritchie Netflix series leaked as a PR tactic? Watch: Trump's brutal killing of two suspected drug smugglers as chilling details of their US'INVASION' are exposed Lindsay Clancy's supporters are all suddenly acting strange. I'm afraid of what they might do next... something very sick is going on: KENNEDY Husband of slain therapist finally breaks silence on killing that rocked their ritzy horse country town... as fear and rumors run wild over mystery of powerful pharma exec's daughter who was knifed to death Trump's Treasury chief to launch massive financial warfare against Iran as US seeks to cripple'tyrannical' regime Sandra Bullock's devastating SIX YEARS in hiding: Insiders reveal actress was so'lonely and scared' that her'personality changed'... and how ALS death of partner Bryan at 57 nearly ruined her Horrified tourist finds note on her restaurant receipt saying'burn the eggs and spit on them' after she asked for them not to be runny Why a nose job isn't guaranteed to fix your face: From Wayne Rooney to Bella Hadid, a plastic surgeon reveals the celebrities who look better after the procedure - and who shouldn't have gone under the knife Kate shows off new lighter locks and sun-kissed glow after summer holidays - as she enjoys Balmoral break ahead of Harry and Meghan's return Dr. Oz addresses concerns about MMR vaccine after Trump calls shot'lethal' Tom Cruise's sex life crisis: Insiders reveal actor's secret'frustration' at 64... real reason Ana de Armas fling collapsed... lover's name he can't bear to hear... and Victoria Beckham's failed interventions Meghan is being cruelly used: LIZ JONES's warning to the Duchess as her film offer is abruptly'withdrawn' Two police officers fired and arrested for'egregious criminal conduct' during violent car stop in Arizona Shelley Fabares dead at 82: The Donna Reed Show actress who starred alongside Elvis Presley remembered by'heartbroken' family I've watched the full Perez Hilton knife video. The truth about this tragedy is beyond horrific but somehow it's just got worse: ROB SHUTER Yellowstone creator Taylor Sheridan hit with shock lawsuit claiming he STOLE idea for show starring Kevin Costner... days after highly anticipated spin-off was scrapped The Hollywood face never changes!

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Large Language Models (LLMs) demonstrate human-level or even superior language abilities, effectively modeling syntactic structures, yet the specific computational units responsible remain unclear. A key question is whether LLM behavioral capabilities stem from mechanisms akin to those in the human brain. To address these questions, we introduce the Hierarchical Frequency Tagging Probe (HFTP), a tool that utilizes frequency-domain analysis to identify neuron-wise components of LLMs (e.g., individual Multilayer Perceptron (MLP) neurons) and cortical regions (via intracranial recordings) encoding syntactic structures. Our results show that models such as GPT-2, Gemma, Gemma 2, Llama 2, Llama 3.1, and GLM-4 process syntax in analogous layers, while the human brain relies on distinct cortical regions for different syntactic levels. Representational similarity analysis reveals a stronger alignment between LLM representations and the left hemisphere of the brain (dominant in language processing). Notably, upgraded models exhibit divergent trends: Gemma 2 shows greater brain similarity than Gemma, while Llama 3.1 shows less alignment with the brain compared to Llama 2. These findings offer new insights into the interpretability of LLM behavioral improvements, raising questions about whether these advancements are driven by human-like or non-human-like mechanisms, and establish HFTP as a valuable tool bridging computational linguistics and cognitive neuroscience. This project is available at https://github.com/LilTiger/HFTP.

Recent studies suggest that the representations learned by large language models (LLMs) are partially aligned to those of the human brain. However, whether and why this alignment score arises from a similar sequence of computations remains elusive. In this study, we explore this question by examining temporally-resolved brain signals of participants listening to 10 hours of an audiobook. We study these neural dynamics jointly with a benchmark encompassing 17 LLMs varying in size and architecture type. Our analyses confirm that LLMs and the brain generate representations in a similar order: specifically, activations in the initial layers of LLMs tend to best align with early brain responses, while the deeper layers of LLMs tend to best align with later brain responses. This brain-LLM alignment is consistent across transformers and recurrent architectures. However, its emergence depends on both model size and context length.

The evolution of the human brain has led to the development of complex synaptic plasticity, enabling dynamic adaptation to a constantly evolving world.

Language encoding models help explain language processing in the human brain by learning functions that predict brain responses from the language stimuli that elicited them. Current word embedding-based approaches treat each stimulus word independently and thus ignore the influence of context on language understanding. In this work we instead build encoding models using rich contextual representations derived from an LSTM language model. Our models show a significant improvement in encoding performance relative to state-of-the-art embeddings in nearly every brain area. By varying the amount of context used in the models and providing the models with distorted context, we show that this improvement is due to a combination of better word embeddings learned by the LSTM language model and contextual information. We are also able to use our models to map context sensitivity across the cortex. These results suggest that LSTM language models learn high-level representations that are related to representations in the human brain.

We're about to simulate a human brain on a supercomputer The world's most powerful supercomputers can now run simulations of billions of neurons, and researchers hope such models will offer unprecedented insights into how our brains work What would it mean to simulate a human brain? Today's most powerful computing systems now contain enough computational firepower to run simulations of billions of neurons, comparable to the sophistication of real brains. We increasingly understand how these neurons are wired together, too, leading to brain simulations that researchers hope will reveal secrets of brain function that were previously hidden. Researchers have long tried to isolate specific parts of the brain, modelling smaller regions with a computer to explain particular brain functions. But "we have never been able to bring them all together into one place, into one larger brain model where we can check whether these ideas are at all consistent", says Markus Diesmann at the Jülich Research Centre in Germany.

The human visual system uses two parallel pathways for spatial processing and object recognition. In contrast, computer vision systems tend to use a single feedforward pathway, rendering them less robust, adaptive, or efficient than human vision. To bridge this gap, we developed a dual-stream vision model inspired by the human eyes and brain. At the input level, the model samples two complementary visual patterns to mimic how the human eyes use magnocellular and parvocellular retinal ganglion cells to separate retinal inputs to the brain. At the backend, the model processes the separate input patterns through two branches of convolutional neural networks (CNN) to mimic how the human brain uses the dorsal and ventral cortical pathways for parallel visual processing.

The alignment between deep neural network (DNN) features and cortical responses currently provides the most accurate quantitative explanation for higher visual areas. At the same time, these model features have been critiqued as uninterpretable explanations, trading one black box (the human brain) for another (a neural network). In this paper, we train networks to directly predict, from scratch, brain responses to images from a large-scale dataset of natural scenes (Allen et.