Talks

We uncover activity-driven crossover from phase separation to a new turbulent state in a two- dimensional system of counter-rotating spinners. We study the statistical properties of this active- rotor turbulence using the active-rotor Cahn-Hilliard-Navier-Stokes model, and show that the vor- ticity ω ∝ ϕ, the scalar field that distinguishes regions with different rotating states. We explain this intriguing proportionality theoretically, and we characterize power-law energy and concentra- tion spectra, intermittency, and flow-topology statistics. We suggest biological implications of such turbulence. This work has been done with Biswajit Maji and Nadia Bihari Padhan. https://arxiv.org/abs/2503.03843

Quasars - accreting super-massive black holes - are the most luminous non-transient sources known and can be seen at redshifts of z > 7, when the Universe was just ~5% of its current age.  This implies that black holes with masses of up to ~10^9 M_Sun formed less than 800 Myr after the Big Bang, impossible under the default paradigm of Eddington-limited accretion onto stellar mass black holes.  The greatest barrier to understanding the formation and growth of these objects is the lack of data: quasars are very rare at these distances/times with fewer than ten known with z > 7 at present.  I will report on recent observational developments in this field, with a particular focus on the early results from the Euclid mission, for which the Wide Survey has the necessary combination of area, wavelength coverage and depth to increase the number of known quasars by an order of magnitude and to push to redshifts z > 8 and beyond.

Flying insects must balance the demands of speed and agility with the precision of their movements to swiftly and accurately respond to environmental stimuli. Achieving this balance requires them to integrate sensory information from various modalities. Of particular importance are visual inputs from their compound eyes and mechanosensory inputs from their antennae, which are crucial for maintaining flight stability. This challenge is particularly pronounced in insects like hawkmoths, which navigate under low light conditions. Prior studies on diverse hawkmoths and other insects have highlighted the critical role of antennal mechanosensory feedback in flight control, akin to the function of halteres in flies. How is such multisensory integration achieved? We addressed this question by conducting recordings from descending neurons in the cervical connective nerve in the Oleander hawkmoths, Daphnis nerii. The moths were provided with visual stimuli comprised of moving spots of light and mechanical stimuli to their antennae. While these stimuli were presented singly or concurrently, we recorded intracellularly from axons of descending interneurons to determine if they respond to one or both stimuli. In addition to a number of exclusively visual or mechanosensory descending neurons, we also identified several neurons that multiplex the visual and mechanosensory signals such that a single neuron encodes both visual stimuli from the compound eyes, and mechanosensory stimuli from the antennal Johnston's organs. Additional experiments at the level of behavior in intact moths reveals that integration of visual and antennal mechanosensory feedback plays a key role in gaze stabilization in flying hawkmoths. Together, these experiments underscore the importance of multisensory integration during flight in hawkmoths.

ynchronization of rhythmic units is essential for various biological functions. The synchronization mechanism is often governed by neural networks. For example, the circadian rhythm in mammals functions by synchronizing the gene expression rhythms of individual neurons within a neural tissue called the suprachiasmatic nucleus. Various movement patterns observed in animal locomotion, such as walking and swimming, are generated by neural networks known as central pattern generators. The synchronization dynamics of oscillator groups strongly depend on the heterogeneity of intrinsic frequencies, noise intensity, and the interaction network. If these factors can be estimated from observations, it can aid in understanding, predicting, and controlling the system, and contribute to elucidating the design principles of robust systems. However, estimation involves many challenges. Among them, estimation becomes exponentially difficult as the model's dimensions and the number of parameters increase. Therefore, it is desirable to assume a model with as low dimensions and as few parameters as possible. In the synchronization phenomena of oscillator groups, the phase oscillator model is expected to be useful. This presentation introduces research on estimation using the phase oscillator model [1,2]. [1]A Matsuki, H Kori, R Kobayashi: Network inference from oscillatory signals based on circle map, arXiv:2407.07445 (2024) [2]F Mori, H Kori: Noninvasive inference methods for interaction and noise intensities of coupled oscillators using only spike time data, Proceedings of the National Academy of Sciences 119, e2113620119 (2022)