29th Annual Computational Neuroscience Meeting: CNS*2020

Meeting abstracts This publication was funded by OCNS. The Supplement Editors declare that they have no competing interests. Virtual | 18-22 July 202

Simulating and optimizing electrical current flow and neuronal activation in retinal implants

© 2018 Dr Timothy Bede Esler[Abstract Withheld

Physical principles for scalable neural recoding

Simultaneously measuring the activities of all neurons in a mammalian brain at millisecond resolution is a challenge beyond the limits of existing techniques in neuroscience. Entirely new approaches may be required, motivating an analysis of the fundamental physical constraints on the problem. We outline the physical principles governing brain activity mapping using optical, electrical, magnetic resonance, and molecular modalities of neural recording. Focusing on the mouse brain, we analyze the scalability of each method, concentrating on the limitations imposed by spatiotemporal resolution, energy dissipation, and volume displacement. Based on this analysis, all existing approaches require orders of magnitude improvement in key parameters. Electrical recording is limited by the low multiplexing capacity of electrodes and their lack of intrinsic spatial resolution, optical methods are constrained by the scattering of visible light in brain tissue, magnetic resonance is hindered by the diffusion and relaxation timescales of water protons, and the implementation of molecular recording is complicated by the stochastic kinetics of enzymes. Understanding the physical limits of brain activity mapping may provide insight into opportunities for novel solutions. For example, unconventional methods for delivering electrodes may enable unprecedented numbers of recording sites, embedded optical devices could allow optical detectors to be placed within a few scattering lengths of the measured neurons, and new classes of molecularly engineered sensors might obviate cumbersome hardware architectures. We also study the physics of powering and communicating with microscale devices embedded in brain tissue and find that, while radio-frequency electromagnetic data transmission suffers from a severe power–bandwidth tradeoff, communication via infrared light or ultrasound may allow high data rates due to the possibility of spatial multiplexing. The use of embedded local recording and wireless data transmission would only be viable, however, given major improvements to the power efficiency of microelectronic devices

Toward a multiscale modeling framework for understanding serotonergic function

Despite its importance in regulating emotion and mental wellbeing, the complex structure and function of the serotonergic system present formidable challenges toward understanding its mechanisms. In this paper, we review studies investigating the interactions between serotonergic and related brain systems and their behavior at multiple scales, with a focus on biologically-based computational modeling. We first discuss serotonergic intracellular signaling and neuronal excitability, followed by neuronal circuit and systems levels. At each level of organization, we will discuss the experimental work accompanied by related computational modeling work. We then suggest that a multiscale modeling approach that integrates the various levels of neurobiological organization could potentially transform the way we understand the complex functions associated with serotonin

Approaches for unveiling the kinetic mechanisms of voltage gated ion channels in neurons

Includes vitaIonic currents drive cellular function within all living cells to perform highly specific tasks. For excitable cells, such as muscle and neurons, voltage-gated ion channels have finely tuned kinetics that allow the transduction of Action potentials to other cells. Voltage-gated ion channels are molecular machines that open and close depending on electrical potential. Neuronal firing rates are largely determined by the overall availability of voltage-gated Na+ and K+ currents.This work describes new approaches for collecting and analyzing experimental data that can be used to streamline experiments. Ion channels are composed of multimeric complexes regulated by intracellular factors producing complex kinetics. The stochastic behavior of thousands of individual ion hannels coordinates to produce cellular activity. To describe their activity and test hypotheses about the channel, experimenters often fit Markov models to a set of experimental data. Markov models are defined by a set of states, whose transitions described by rate constants. To improve the modeling process, we have developed computational approaches to introduce kinetic constraints that reduces the parameter search space. This work describes the implementation and mathematical transformations required to describe linear and non-linear parameter constraints that govern rate constants. Not all ion channel behaviors can easily be described by rate constants. Therefore, we developed and implemented a penalty-based mechanism that can be used to guide the optimization engine to produce a model with a desired behavior, such as single-channel open probability and use dependent effects. To streamline data collection for experiments in brain slice preparations, we developed a 3D virtual software environment that incorporates data from micro-positioning motors and scientific cameras in real-time. This environment provides positional feedback to the investigator and allows for the creation of data maps including both images and electrical recordings. We have also produced semi-automatic targeting procedures that simplifies the overall experimental experience. Experimentally, this work also examines how the kinetic mechanism of voltage gated Na channels regulates the neuronal firing of brainstem respiratory neurons. These raphe neurons are intrinsic pacemakers that do not rely on synaptic connections to elicit activity. I explored how intracellular calcium regulates the kinetics of TTX-sensitive Na+ currents using whole-cell patch clamp electrophysiology. Established with intracellular Ca2+ buffers, high [Ca2+] levels greater than ~7 [micro]M did not change the voltage dependence of steady-state activation and inactivation, but slightly slowed inactivation time course. However, the recovery from inactivation and use dependence inactivation is slowed by high intracellular [Ca2+]. Overall, these approaches described in this work have improved data acquisition and data analysis to create better ion channel models and enhance the electrophysiology experience.Includes bibliographical reference

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)

The role of direction-selective visual interneurons T4 and T5 in Drosophila orientation behavior

In order to safely move through the environment, visually-guided animals use several types of visual cues for orientation. Optic flow provides faithful information about ego-motion and can thus be used to maintain a straight course. Additionally, local motion cues or landmarks indicate potentially interesting targets or signal danger, triggering approach or avoidance, respectively. The visual system must reliably and quickly evaluate these cues and integrate this information in order to orchestrate behavior. The underlying neuronal computations for this remain largely inaccessible in higher organisms, such as in humans, but can be studied experimentally in more simple model species. The fly Drosophila, for example, heavily relies on such visual cues during its impressive flight maneuvers. Additionally, it is genetically and physiologically accessible. Hence, it can be regarded as an ideal model organism for exploring neuronal computations during visual processing. In my PhD studies, I have designed and built several autonomous virtual reality setups to precisely measure visual behavior of walking flies. The setups run in open-loop and in closed-loop configuration. In an open-loop experiment, the visual stimulus is clearly defined and does not depend on the behavioral response. Hence, it allows mapping of how specific features of simple visual stimuli are translated into behavioral output, which can guide the creation of computational models of visual processing. In closedloop experiments, the behavioral response is fed back onto the visual stimulus, which permits characterization of the behavior under more realistic conditions and, thus, allows for testing of the predictive power of the computational models. In addition, Drosophila’s genetic toolbox provides various strategies for targeting and silencing specific neuron types, which helps identify which cells are needed for a specific behavior. We have focused on visual interneuron types T4 and T5 and assessed their role in visual orientation behavior. These neurons build up a retinotopic array and cover the whole visual field of the fly. They constitute major output elements from the medulla and have long been speculated to be involved in motion processing. This cumulative thesis consists of three published studies: In the first study, we silenced both T4 and T5 neurons together and found that such flies were completely blind to any kind of motion. In particular, these flies could not perform an optomotor response anymore, which means that they lost their normally innate following responses to motion of large-field moving patterns. This was an important finding as it ruled out the contribution of another system for motion vision-based behaviors. However, these flies were still able to fixate a black bar. We could show that this behavior is mediated by a T4/T5-independent flicker detection circuitry which exists in parallel to the motion system. In the second study, T4 and T5 neurons were characterized via twophoton imaging, revealing that these cells are directionally selective and have very similar temporal and orientation tuning properties to directionselective neurons in the lobula plate. T4 and T5 cells responded in a contrast polarity-specific manner: T4 neurons responded selectively to ON edge motion while T5 neurons responded only to OFF edge motion. When we blocked T4 neurons, behavioral responses to moving ON edges were more impaired than those to moving OFF edges and the opposite was true for the T5 block. Hence, these findings confirmed that the contrast polarityspecific visual motion pathways, which start at the level of L1 (ON) and L2 (OFF), are maintained within the medulla and that motion information is computed twice independently within each of these pathways. Finally, in the third study, we used the virtual reality setups to probe the performance of an artificial microcircuit. The system was equipped with a camera and spherical fisheye lens. Images were processed by an array of Reichardt detectors whose outputs were integrated in a similar way to what is found in the lobula plate of flies. We provided the system with several rotating natural environments and found that the fly-inspired artificial system could accurately predict the axes of rotation