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#neural

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The vOICe vision BCI 🧠🇪🇺

Pyramidal cell types and 5-HT2A receptors are essential for #psilocybin's lasting drug action biorxiv.org/content/10.1101/20 on #structural #neural #plasticity #neuroscience

"We find that a single dose of psilocybin increased the density of dendritic spines in both the subcortical-projecting, pyramidal tract (PT) and intratelencephalic (IT) cell types."

bioRxiv · Pyramidal cell types and 5-HT2A receptors are essential for psilocybin's lasting drug actionPsilocybin is a serotonergic psychedelic with therapeutic potential for treating mental illnesses. At the cellular level, psychedelics induce structural neural plasticity, exemplified by the drug-evoked growth and remodeling of dendritic spines in cortical pyramidal cells. A key question is how these cellular modifications map onto cell type-specific circuits to produce psychedelics' behavioral actions. Here, we use in vivo optical imaging, chemogenetic perturbation, and cell type-specific electrophysiology to investigate the impact of psilocybin on the two main types of pyramidal cells in the mouse medial frontal cortex. We find that a single dose of psilocybin increased the density of dendritic spines in both the subcortical-projecting, pyramidal tract (PT) and intratelencephalic (IT) cell types. Behaviorally, silencing the PT neurons eliminates psilocybin's ability to ameliorate stress-related phenotypes, whereas silencing IT neurons has no detectable effect. In PT neurons only, psilocybin boosts synaptic calcium transients and elevates firing rates acutely after administration. Targeted knockout of 5-HT2A receptors abolishes psilocybin's effects on stress-related behavior and structural plasticity. Collectively these results identify a pyramidal cell type and the 5-HT2A receptor in the medial frontal cortex as playing essential roles for psilocybin's long-term drug action. ### Competing Interest Statement A.C.K. has been a scientific advisor or consultant for Boehringer Ingelheim, Empyrean Neuroscience, Freedom Biosciences, and Psylo. A.C.K. has received research support from Intra-Cellular Therapies. The other authors report no financial relationships with commercial interests.
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Gabriel Weindel

Very happy of our post-review preprint on single-trial detection of cognitive events in #neural time-series

biorxiv.org/content/10.1101/20

TLDR: with the hidden multivariate pattern method (HMP), using a few assumptions and the E/#MEG signal during the reaction time, we can:
- recover how many task related events appear in the #EEG
- get their SINGLE-TRIAL time location and therefore also voltage activity 🤯

@cognition @eeg @cogneurophys

bioRxiv · Trial-by-trial detection of cognitive events in neural time-seriesMeasuring the time-course of neural events that make up cognitive processing is crucial to understand the relation between brain and behavior. To this aim, we formulated a method to discover a trial-wise sequence of events in multivariate neural signals such as electro- or magneto-encephalograpic (E/MEG) recordings. This sequence of events is assumed to be represented by multivariate patterns in neural time-series, with inter-event durations following probability distributions. By estimating event-specific multivariate patterns, and between-event duration distributions, the method allows to recover the by-trial onsets of brain responses. We demonstrate the properties and robustness of this hidden multivariate pattern (HMP) method through simulations, including robustness to low signal-to-noise ratio, as typically observed in EEG recordings. The applicability of HMP is illustrated using previously published data from a speed-accuracy trade-off task. We show how HMP provides, for any experiment or condition, an estimate of the number of events, the sensors contributing to each event (e.g. EEG scalp topography), and the durations between each event. Traditional exploration of tasks' cognitive architectures can thus be enhanced by HMP estimates. ### Competing Interest Statement The authors have declared no competing interest.
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W3C Developers

During the WebEvolve 2024 conference in #Shanghai 🇨🇳, Ningxin Hu (Intel) explained how #WebNN provides a unified abstraction of #neural networks for the web, and enables access to #AI hardware acceleration via native OS #ML APIs. This approach delivers near-native performance and supports next-generation use cases.

Check slides and event report:
▶️ w3.org/2024/01/webevolve-serie
▶️ w3.org/2024/01/webevolve-serie #W3CChina

... and watch the 🎬 ! youtu.be/juTRGIIEx_8

Snapshot of the video recording of Ningxin Hu presenting "WebNN Overview and Status Update", during the WebEvolve 2024 conference (May 2024)
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Scientific Frontline

A team from the University of Geneva has succeeded in modeling an artificial #neural #network capable of this #cognitive prowess. After learning and performing a series of basic tasks, this #AI was able to provide a linguistic description of them to a ‘‘sister’’ AI, which in turn performed them.
#ArtificialIntelligence #Neuroscience #sflorg
sflorg.com/2024/03/ai03182401.

www.sflorg.comTwo artificial intelligences talk to each otherThe UNIGE team worked on artificial neural networks, inspired by our biological neurons and the way they transmit electrical signals to each other in
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Scientific Frontline

A surge of a #neural-specific protein in the #brain is the earliest-yet biomarker for #Alzheimer’s disease, report University of Illinois Urbana-Champaign researchers studying a mouse model of the disease. Furthermore, the increased protein activity leads to #seizures associated with the earliest stages of #neurodegeneration
#Biology #Neuroscience #sflorg
sflorg.com/2024/03/bio03052401

www.sflorg.comEarliest-yet Alzheimer’s biomarker found in mouse model could point to new targetsThe neural-specific protein, PSD-95, could pose a new target for Alzheimer’s research
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Scientific Frontline

Deep #neural #networks have achieved remarkable results across #science and #technology, but it remains largely unclear what makes them work so well. A new study sheds light on the inner workings of deep learning models that learn from relational datasets
#AI #ArtificialIntelligence #sflorg
sflorg.com/2024/03/ai03052401.

www.sflorg.comHow artificial intelligence learns from complex networksMulti-layered, so-called deep neural networks are highly complex constructs
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