Tootfinder

Trump used his address to call out ABC and NBC
for not airing his live remarks,
claiming the networks declined to do so
“because they know how corrupt our system is and they don’t want to reveal it.”
He also repeated his frequent demand that the networks’ government-issued broadcast licenses be revoked.
“They and others in the media are part of a plot,”
the president said, offering no evidence for his claims.
In fact, ABC and NBC did carry Trump’s spe…

In Prime Time, Trump Criticizes Networks for Not Carrying His Speech
The president also called for ABC and NBC to lose their broadcast licenses, after complaining that the networks were not airing his remarks.

Today's & tomorrow's #CS2NORDICS in Copenhagen will feature 3 presentations from our #bicycle #network research:

🌟 Meet our new SIG on Campus Network as a Service (SIG-CNaaS) 🌟
Bringing together #NRENs exploring how campus networks can move from traditional, locally managed setups to service-oriented, automated infrastructures.
The group just held its first meeting and a BoF at #TNC26, and already brings tog…

What if the brain doesn't create consciousness? Christof Koch proposes it might be the fabric of reality itself https://www.sciencealert.com/what-if-the-brain-doesnt-create-consciousness-scientist-proposes-i…

What if The Brain Doesn't Create Consciousness? Scientist Proposes It Might Be The Fabric of Reality Itself
For decades, neuroscientists have searched the brain for the biological basis of consciousness, assuming our thoughts, emotions, and subjective experiences emerge from networks of neurons.

We had difficulties automatically classifying chats, messaging channels, and forums. So we tested several open-weight large language models for our use case. The benchmark is published below, along with a tool supporting the classification process.
This helps us avoid manually classifying channels collected from dark-web forums and social networks, while reliably identifying which channels can be discarded and which deserve further analysis.
🔗 Publication #ai #llm #cybersecurity #darkweb #osint #opensource

Analysis of inter-spike interval statistics in neuronal networks with depolarizing and hyperpolarizing threshold potentials
Oliver Gambrell, Abhyudai Singh
https://arxiv.org/abs/2607.18428 https://arxiv.org/pdf/2607.18428 https://arxiv.org/html/2607.18428
arXiv:2607.18428v1 Announce Type: new
Abstract: Neuronal communication is mediated in part by changes in neuronal firing rates. The time interval between successive neuronal firings is referred to as the inter-spike interval (ISI), and quantifying its statistics is important for understanding neuronal communication. This paper studies the ISI statistics of a postsynaptic neuron receiving independent excitatory and inhibitory presynaptic action potentials (EI circuit). This circuit is modeled as a classical integrate-and-fire neuron, and the ISI statistics are investigated for both fixed and adaptive threshold potentials. First, a depolarizing adaptive threshold model is studied, where the threshold potential increases with the postsynaptic membrane potential. Our analysis shows that the ISI noise, quantified as the coefficient of variation, is larger in the adaptive threshold model compared to the fixed threshold model for the same mean ISI. Additionally, simulations reveal that the ISI noise can be either hypo- or hyper-exponential (defined as ISI noise smaller or larger than one, respectively) depending on the frequencies of excitatory and inhibitory inputs. Next, a hyperpolarizing adaptive threshold potential is studied, where the threshold decreases as the membrane potential hyperpolarizes. Interestingly, this model shows that the postsynaptic neuron can generate action potentials (APs) when driven solely by inhibitory inputs. Furthermore, mean and noise signatures are characterized across model parameters for both excitatory and inhibitory inputs. In summary, this work provides a systematic stochastic analysis of adaptive threshold models for AP generation to understand their role in interneuronal information processing.
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STSBench: A Large-Scale Dataset for Modeling Neuronal Activity in the Dorsal Stream of Primate Visual Cortex
Ethan B. Trepka, Ruobing Xia, Shude Zhu, Sharif Saleki, Danielle Abreu Lopes, Stephen J. Ni\~no Cital, Konstantin F. Willeke, Mindy Kim, Tirin Moore
https://arxiv.org/abs/2607.15631 https://arxiv.org/pdf/2607.15631 https://arxiv.org/html/2607.15631
arXiv:2607.15631v1 Announce Type: new
Abstract: The primate visual system is typically divided into two streams - the ventral stream, responsible for object recognition, and the dorsal stream, responsible for encoding spatial relations and motion. Recent studies have shown that convolutional neural networks (CNNs) pretrained on object recognition tasks are remarkably effective at predicting neuronal responses in the ventral stream, shedding light on the neural mechanisms underlying object recognition. However, similar models of the dorsal stream remain underdeveloped due to the lack of large scale datasets encompassing dorsal stream areas. To address this gap, we present STSBench, a dataset of large-scale, single neuron recordings from over 2,000 neurons in the superior temporal sulcus (STS), a nearly 50-fold increase over existing dorsal stream datasets, collected while Rhesus macaques viewed thousands of unique, natural videos. We show that our dataset can be used for benchmarking encoding models of dorsal stream neuronal responses and reconstructing visual input from neural activity.
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