Characterizing neural coding performance for populations of sensory neurons: comparing a weighted spike distance metrics to other analytical methods

The identity of sensory stimuli is encoded in the spatio-temporal patterns of responses of the encoding neural population. For stimuli to be discriminated reliably, differences in population responses must be accurately decoded by downstream networks. Several methods to compare patterns of responses have been used by neurophysiologists to characterize the accuracy of the sensory responses studied. Among the most widely used analyses, we note methods based on Euclidean distances or on spike metric distances. Methods based on artificial neural networks and machine learning that recognize and/or classify specific input patterns have also gained popularity. Here, we first compare these three strategies using datasets from three different model systems: the moth olfactory system, the electrosensory system of gymnotids, and leaky-integrate-and-fire (LIF) model responses. We show that the input-weighting procedure inherent to artificial neural networks allows the efficient extraction of information relevant to stimulus discrimination. To combine the convenience of methods such as spike metric distances but leverage the advantages of weighting the inputs, we propose a measure based on geometric distances where each dimension is weighted proportionally to how informative it is. We show that the result of this Weighted Euclidian Distance (WED) analysis performs as well or better than the artificial neural network we tested and outperforms the more traditional spike distance metrics. We applied information theoretic analysis to LIF responses and compared their encoding accuracy with the discrimination accuracy quantified through this WED analysis. We show a high degree of correlation between discrimination accuracy and information content, and that our weighting procedure allowed the efficient use of information present to perform the discrimination task. We argue that our proposed measure provides the flexibility and ease of use sought by neurophysiologists while providing a more powerful way to extract relevant information than more traditional methods

Observing GW190521-like binary black holes and their environment with LISA

Binaries of relatively massive black holes like GW190521 have been proposed to form in dense gas environments, such as the disks of Active Galactic Nuclei (AGNs), and they might be associated with transient electromagnetic counterparts. The interactions of this putative environment with the binary could leave a significant imprint at the low gravitational wave frequencies observable with the Laser Interferometer Space Antenna (LISA). We show that LISA will be able to detect up to ten GW190521-like black hole binaries, with sky position errors $\lesssim1$ deg$^2$. Moreover, it will measure directly various effects due to the orbital motion around the supermassive black hole at the center of the AGN, especially the Doppler modulation and the Shapiro time delay. Thanks to a careful treatment of their frequency domain signal, we were able to perform the full parameter estimation of Doppler and Shapiro-modulated binaries as seen by LISA. We find that the Doppler and Shapiro effects will allow for measuring the AGN parameters (radius and inclination of the orbit around the AGN, central black hole mass) with up to percent-level precision. Properly modeling these low-frequency environmental effects is crucial to determine the binary formation history, as well as to avoid biases in the reconstruction of the source parameters and in tests of general relativity with gravitational waves. <br

Chasing Super-Massive Black Hole merging events with AthenaAthena and LISA

The European Space Agency is studying two large-class missions bound to operate in the $2030$s, and aiming at investigating the most energetic phenomena in the Universe. $Athena$ is poised to study the physical conditions of baryons in large-scale structures, as well as to yield a census of accreting super-massive black holes down to the epoch of reionization; the Laser Interferometer Space Antenna (LISA) will extend the hunt for Gravitational Wave (GW) events to the mHz regime. While the science cases of the two missions are independently outstanding, we discuss in this paper the $additional$ science that their concurrent operation could yield. We focus on the multi-messenger study of Super-Massive (M$\lesssim 10^7\rm M_{\odot}$) Black Hole Mergers (SMBHMs), accessible to $Athena$ up to $z\sim2$. The simultaneous measurement of their electro-magnetic (EM) and GW signals may enable unique experiments in the domains of astrophysics, fundamental physics, and cosmography. Key to achieve these results will be the LISA capability of locating a SMBHM event with an error box comparable to, or better than the field-of-view of the $Athena$ Wide Field Imager ($\simeq0.4$deg$^2$). LISA will achieve such an accuracy several hours prior to merging for the highest signal-to-noise events. While theoretical predictions of the EM emission are still uncertain, this opens in principle the possibility of truly concurrent EM and GW studies of the merger phase. LISA localization improves significantly at merging, and is likely to reach the arcminute-level for a sizeable fraction of events at $z\lesssim 0.5$ and masses $\lesssim10^6\rm M_{\odot}$, well within the detection capability of $Athena$. We also briefly discuss the prospective of $Athena$ studies for other classes of GW-emitting black hole binaries, for which theoretical predictions are admittedly extremely uncertain. [abridged]Comment: 18 pages, 8 figures. Submitted to MNRA

Bursts and Isolated Spikes Code for Opposite Movement Directions in Midbrain Electrosensory Neurons

Directional selectivity, in which neurons respond strongly to an object moving in a given direction but weakly or not at all to the same object moving in the opposite direction, is a crucial computation that is thought to provide a neural correlate of motion perception. However, directional selectivity has been traditionally quantified by using the full spike train, which does not take into account particular action potential patterns. We investigated how different action potential patterns, namely bursts (i.e. packets of action potentials followed by quiescence) and isolated spikes, contribute to movement direction coding in a mathematical model of midbrain electrosensory neurons. We found that bursts and isolated spikes could be selectively elicited when the same object moved in opposite directions. In particular, it was possible to find parameter values for which our model neuron did not display directional selectivity when the full spike train was considered but displayed strong directional selectivity when bursts or isolated spikes were instead considered. Further analysis of our model revealed that an intrinsic burst mechanism based on subthreshold T-type calcium channels was not required to observe parameter regimes for which bursts and isolated spikes code for opposite movement directions. However, this burst mechanism enhanced the range of parameter values for which such regimes were observed. Experimental recordings from midbrain neurons confirmed our modeling prediction that bursts and isolated spikes can indeed code for opposite movement directions. Finally, we quantified the performance of a plausible neural circuit and found that it could respond more or less selectively to isolated spikes for a wide range of parameter values when compared with an interspike interval threshold. Our results thus show for the first time that different action potential patterns can differentially encode movement and that traditional measures of directional selectivity need to be revised in such cases

Probing Real Sensory Worlds of Receivers with Unsupervised Clustering

The task of an organism to extract information about the external environment from sensory signals is based entirely on the analysis of ongoing afferent spike activity provided by the sense organs. We investigate the processing of auditory stimuli by an acoustic interneuron of insects. In contrast to most previous work we do this by using stimuli and neurophysiological recordings directly in the nocturnal tropical rainforest, where the insect communicates. Different from typical recordings in sound proof laboratories, strong environmental noise from multiple sound sources interferes with the perception of acoustic signals in these realistic scenarios. We apply a recently developed unsupervised machine learning algorithm based on probabilistic inference to find frequently occurring firing patterns in the response of the acoustic interneuron. We can thus ask how much information the central nervous system of the receiver can extract from bursts without ever being told which type and which variants of bursts are characteristic for particular stimuli. Our results show that the reliability of burst coding in the time domain is so high that identical stimuli lead to extremely similar spike pattern responses, even for different preparations on different dates, and even if one of the preparations is recorded outdoors and the other one in the sound proof lab. Simultaneous recordings in two preparations exposed to the same acoustic environment reveal that characteristics of burst patterns are largely preserved among individuals of the same species. Our study shows that burst coding can provide a reliable mechanism for acoustic insects to classify and discriminate signals under very noisy real-world conditions. This gives new insights into the neural mechanisms potentially used by bushcrickets to discriminate conspecific songs from sounds of predators in similar carrier frequency bands

Adaptation and Selective Information Transmission in the Cricket Auditory Neuron AN2

Sensory systems adapt their neural code to changes in the sensory environment, often on multiple time scales. Here, we report a new form of adaptation in a first-order auditory interneuron (AN2) of crickets. We characterize the response of the AN2 neuron to amplitude-modulated sound stimuli and find that adaptation shifts the stimulus–response curves toward higher stimulus intensities, with a time constant of 1.5 s for adaptation and recovery. The spike responses were thus reduced for low-intensity sounds. We then address the question whether adaptation leads to an improvement of the signal's representation and compare the experimental results with the predictions of two competing hypotheses: infomax, which predicts that information conveyed about the entire signal range should be maximized, and selective coding, which predicts that “foreground” signals should be enhanced while “background” signals should be selectively suppressed. We test how adaptation changes the input–response curve when presenting signals with two or three peaks in their amplitude distributions, for which selective coding and infomax predict conflicting changes. By means of Bayesian data analysis, we quantify the shifts of the measured response curves and also find a slight reduction of their slopes. These decreases in slopes are smaller, and the absolute response thresholds are higher than those predicted by infomax. Most remarkably, and in contrast to the infomax principle, adaptation actually reduces the amount of encoded information when considering the whole range of input signals. The response curve changes are also not consistent with the selective coding hypothesis, because the amount of information conveyed about the loudest part of the signal does not increase as predicted but remains nearly constant. Less information is transmitted about signals with lower intensity

The evolution of bicontinuous polymeric nanospheres in aqueous solution

Complex polymeric nanospheres in aqueous solution are desirable for their promising potential in encapsulation and templating applications. Understanding how they evolve in solution enables better control of the final structures. By unifying insights from cryoTEM and small angle X-ray scattering (SAXS), we present a mechanism for the development of bicontinuous polymeric nanospheres (BPNs) in aqueous solution from a semi-crystalline comb-like block copolymer that possesses temperature-responsive functionality. During the initial stages of water addition to THF solutions of the copolymer the aggregates are predominantly vesicles; but above a water content of 53% irregular aggregates of phase separated material appear, often microns in diameter and of indeterminate shape. We also observe a cononsolvency regime for the copolymer in THF–water mixtures from 22 to 36%. The structured large aggregates gradually decrease in size throughout dialysis, and the BPNs only appear upon cooling the fully aqueous dispersions from 35 °C to 5 °C. Thus, the final BPNs are ultimately the result of a reversible temperature-induced morphological transition

The Cercal Organ May Provide Singing Tettigoniids a Backup Sensory System for the Detection of Eavesdropping Bats

Conspicuous signals, such as the calling songs of tettigoniids, are intended to attract mates but may also unintentionally attract predators. Among them bats that listen to prey-generated sounds constitute a predation pressure for many acoustically communicating insects as well as frogs. As an adaptation to protect against bat predation many insect species evolved auditory sensitivity to bat-emitted echolocation signals. Recently, the European mouse-eared bat species Myotis myotis and M. blythii oxygnathus were found to eavesdrop on calling songs of the tettigoniid Tettigonia cantans. These gleaning bats emit rather faint echolocation signals when approaching prey and singing insects may have difficulty detecting acoustic predator-related signals. The aim of this study was to determine (1) if loud self-generated sound produced by European tettigoniids impairs the detection of pulsed ultrasound and (2) if wind-sensors on the cercal organ function as a sensory backup system for bat detection in tettigoniids. We addressed these questions by combining a behavioral approach to study the response of two European tettigoniid species to pulsed ultrasound, together with an electrophysiological approach to record the activity of wind-sensitive interneurons during real attacks of the European mouse-eared bat species Myotis myotis. Results showed that singing T. cantans males did not respond to sequences of ultrasound pulses, whereas singing T. viridissima did respond with predominantly brief song pauses when ultrasound pulses fell into silent intervals or were coincident with the production of soft hemi-syllables. This result, however, strongly depended on ambient temperature with a lower probability for song interruption observable at 21°C compared to 28°C. Using extracellular recordings, dorsal giant interneurons of tettigoniids were shown to fire regular bursts in response to attacking bats. Between the first response of wind-sensitive interneurons and contact, a mean time lag of 860 ms was found. This time interval corresponds to a bat-to-prey distance of ca. 72 cm. This result demonstrates the efficiency of the cercal system of tettigoniids in detecting attacking bats and suggests this sensory system to be particularly valuable for singing insects that are targeted by eavesdropping bats