Theory of interaction between untuned modulatory inputs and tuned sensory inputs

Kavli Affiliate: Kenneth Miller | Authors: Tuan Nguyen, Agostina Palmigiano and Kenneth D Miller | Summary: How does the brain integrate sensory inputs with non-feature-tuned signals, such as those arising from behavioral state changes or neuromodulation? Here, we show that the dynamics of disordered E/I networks with structured, feature-dependent connectivity can be well characterized by […]


Continue.. Theory of interaction between untuned modulatory inputs and tuned sensory inputs

Presynaptic vesicles supply membrane for axonal bouton enlargement during LTP

Kavli Affiliate: Terrence Sejnowski | Authors: Patrick Leavey, Lizhi Jiang, Nicole Pannullo, Clayton Santiago and Seth BlackshawLyndsey M Kirk, Guadalupe C Garcia, Dakota C Hanka, Kyle Zatyko, Thomas M Bartol, Terrence J Sejnowski and Kristen M. Harris | Summary: Long-term potentiation (LTP) induces presynaptic bouton enlargement and a reduction in the number of synaptic vesicles. […]


Continue.. Presynaptic vesicles supply membrane for axonal bouton enlargement during LTP

Overexpression of Meis factors in late-stage retinal progenitors yields complex effects on temporal patterning and neurogenesis.

Kavli Affiliate: Seth Blackshaw | Authors: Patrick Leavey, Lizhi Jiang, Nicole Pannullo, Clayton Santiago and Seth Blackshaw | Summary: The vertebrate retina serves as a model for studying neurogenesis and cell fate specification, with retinal progenitor cells following a tightly regulated temporal sequence to generate distinct cell types. Meis1 and Meis2 are transcription factors implicated […]


Continue.. Overexpression of Meis factors in late-stage retinal progenitors yields complex effects on temporal patterning and neurogenesis.

An extremely metal-poor Lyman-$α$ emitter candidate at $z=6$ revealed through absorption spectroscopy

Kavli Affiliate: Robert A. Simcoe | First 5 Authors: Dominika Ďurovčíková, Anna-Christina Eilers, Robert A. Simcoe, Louise Welsh, Romain A. Meyer | Summary: We report the discovery of a Lyman $alpha$ emitter (LAE) candidate in the immediate foreground of the quasar PSO J158-14 at $z_{rm QSO}=6.0685$ at a projected distance $sim29 {rm pkpc}$ that is […]


Continue.. An extremely metal-poor Lyman-$α$ emitter candidate at $z=6$ revealed through absorption spectroscopy

A XRISM Observation of the Archetypal Radio-Mode Feedback System Hydra-A: Measurements of Atmospheric Motion and Constraints on Turbulent Dissipation

Kavli Affiliate: Michael McDonald | First 5 Authors: Tom Rose, B. R. McNamara, Julian Meunier, A. C. Fabian, Helen Russell | Summary: We present XRISM Resolve observations centered on Hydra-A, a redshift z = 0.054 brightest cluster galaxy which hosts one of the largest and most powerful FR-I radio sources in the nearby Universe. We […]


Continue.. A XRISM Observation of the Archetypal Radio-Mode Feedback System Hydra-A: Measurements of Atmospheric Motion and Constraints on Turbulent Dissipation

On homomorphisms from finite subgroups of $SU(2)$ to Langlands dual pairs of groups

Kavli Affiliate: Yuji Tachikawa | First 5 Authors: Yuki Kojima, Yuji Tachikawa, , , | Summary: Let $N(Gamma,G)$ be the number of homomorphisms from $Gamma$ to $G$ up to conjugation by $G$. Physics of four-dimensional $mathcal{N}=4$ supersymmetric gauge theories predicts that $N(Gamma,G)=N(Gamma , tilde G)$ when $Gamma$ is a finite subgroup of $SU(2)$, $G$ is […]


Continue.. On homomorphisms from finite subgroups of $SU(2)$ to Langlands dual pairs of groups

DexCtrl: Towards Sim-to-Real Dexterity with Adaptive Controller Learning

Kavli Affiliate: Xiang Zhang | First 5 Authors: Shuqi Zhao, Ke Yang, Yuxin Chen, Chenran Li, Yichen Xie | Summary: Dexterous manipulation has seen remarkable progress in recent years, with policies capable of executing many complex and contact-rich tasks in simulation. However, transferring these policies from simulation to real world remains a significant challenge. One […]


Continue.. DexCtrl: Towards Sim-to-Real Dexterity with Adaptive Controller Learning