Bulletin of the American Physical Society
APS March Meeting 2023
Volume 68, Number 3
Las Vegas, Nevada (March 5-10)
Virtual (March 20-22); Time Zone: Pacific Time
Session LL09: V: Neuroscience and Behavior |
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Sponsoring Units: DBIO Chair: Sarah Marzen, Scripps, Pitzer & CMC Room: Virtual Room 9 |
Tuesday, March 21, 2023 5:00AM - 5:12AM |
LL09.00001: Identifying temporal neural firing from images Dominika Lyzwa In order to better understand information processing of neural systems and the neural interactions, the simultaneous activity of its individual neurons needs to be known. Optical tools allow imaging the activity of neural groups in order to study their collective response. For this, the temporal activity of each neuron needs to be identified from the images. The algorithms presented here enable robust and comprehensive identification of the temporal individual activity. |
Tuesday, March 21, 2023 5:12AM - 5:24AM |
LL09.00002: Photonic Probing of Structural Alteration in DNA Molecular Spatial Mass Density Fluctuations in the Nuclei of Human Brain Cells in Progressive Alzheimer's disease Fatemah Alharthi, Ishmael Apachigawo, Prabhakar Pradhan, Mohammad Moshahid Khan Alzheimer's disease (AD) is one of the most common neurodegenerative disorders worldwide and a leading cause of death among the elderly. It is characterized by the accumulation of amyloid protein plaques between nerve cells in the brain. In the early stages of AD, a loss of cognitive function is one of the most noticeable symptoms. It is believed that Alzheimer's may begin decades before symptoms start. Although a person may not show symptoms during this period, the damage occurs in the brain as tau proteins tangle and amyloid plaques build up. This may influence the brain cells/tissue structures at the nanoscale level which the present histopathological procedures can’t detect. Molecular specific photonics localization technique, the inverse participation ratio (IPR), is a promising technique to study the nanoscale structural alterations due to AD in brain cells/tissues using molecular-specific confocal imaging. We quantify nanoscale spatial structural alterations in DNA molecular spatial mass densities in brain cell nuclei. The results show an increase in the degree of spatial structural disorder in DNA modification with AD progression. An increase in spatial disorder in DNA molecular mass density may suggest DNA damage or spatial rearrangement caused by the deposition of amyloid beta protein plaques and neurofibrillary tangles inside and between the neurons. |
Tuesday, March 21, 2023 5:24AM - 5:36AM |
LL09.00003: Synaptic reshaping of plastic neuronal networks by periodic multichannel stimulation with single-pulse and burst stimuli Justus A Kromer, Peter A Tass Synaptic dysfunction is associated with several brain disorders, e.g., Parkinson's disease. Utilizing synaptic plasticity, brain stimulation is capable of reshaping synaptic connectivity. Novel stimulation techniques that counteract pathological synaptic connectivity may induce long-lasting therapeutic effects. We study synaptic reshaping by periodic multichannel stimulation (PMCS). During PMCS, phase-shifted stimuli are delivered to segregated neuronal subpopulations. Harnessing STDP, PMCS changes the synaptic network structure. We compare theoretical approximations of the stimulation-induced network structure to simulations of PMCS of networks of leaky integrate-and-fire neurons with spike-timing-dependent plasticity. PMCS-induced synaptic reshaping depends on the phase lags between stimuli and the stimulus type. Single-pulse stimuli and burst stimuli with low intraburst frequency down-regulate, whereas burst stimuli with high intraburst frequency up-regulate synaptic connections between simultaneously stimulated neurons. Our results may impact parameter adjustment procedures for clinical deep brain stimulation (DBS), which, so far, focus on acute effects. They further provide testable hypotheses on the effect of the stimulus type on the long-lasting outcome of DBS. |
Tuesday, March 21, 2023 5:36AM - 5:48AM |
LL09.00004: Extrinsic vs Intrinsic Criticality In Systems With Many Components Vudtiwat Ngampruetikorn, Ilya M Nemenman, David J Schwab Some biological systems with many components exhibit seemingly critical behaviors, causes of which are unclear. Here we argue that although signatures of criticality are indicative of atypically large fluctuations coupled to observable degrees of freedom, they are agnostic about the origins of such fluctuations. We provide a definition of intrinsic and extrinsic criticalities, based on whether large critical fluctuations originate from within the systems, and offer a unifying way of describing the mechanism behind both types of criticalities. We study how the fluctuation variance, system size, subsampled system size and sample size affect the observability of Zipf's law—oft-used empirical evidence of criticality based on a power-law rank-frequency distribution of the states of the systems. We argue that intrinsic fluctuations are usually too small to explain empirically observed Zipf behaviors and extrinsically driven criticality is a more robust explanation. Finally we explore how specially designed models may induce intrinsic fluctuations large enough to generate Zipf-like distributions that extend adequately far into the observable range of rank-frequency plots. |
Tuesday, March 21, 2023 5:48AM - 6:00AM |
LL09.00005: Mechanics of memory of an information engine Tamoghna Das, Tsvi Tlusty Single particle heat engines are the essential building blocks to understand the information processing in biological systems. As a biological process typically involves transition among multiple states spanning over widely different timescales, the cumulative outcome is naturally expected to depend crucially on the memory of the dominant previous states. We attempt to model such intrinsic memory by introducing a two-step fast transition where the two steps are separated by a waiting time. The system then slowly relaxes to its final state. A complementary reverse protocol is considered to ensure the return of the system to its initial state. Note that such a cycle is fundamentally different from those performing under the feedback due to delayed measurements. Contrary to the latter case, the case of intrinsic memory has got very little attention so far. Primary focus of this study is to elucidate the mechanical aspects of the intrinsic memory processes in terms of the space-time correlation functions. The operational space and states of such an information engine is also explored as a function of the waiting time for the memory formation. |
Tuesday, March 21, 2023 6:00AM - 6:12AM |
LL09.00006: A universal theory of the sensory response requiring no free parameters Willy Wong A theoretical approach governing the rate response of peripheral neurons is described. This approach bears similarities to Boltzmann’s statistical physics. It is a non-mechanistic approach which is important because while Hodgkin-Huxley-like models can provide a universal description, they are unable to capture the simplicity of the response of an entire neuron. Nor are simplified models often grounded theoretically, and the universality of these models have yet to be demonstrated. |
Tuesday, March 21, 2023 6:12AM - 6:24AM |
LL09.00007: A framework for solving time-delayed Markov Decision Processes Sarah Marzen, Yorgo Sawaya, George Issa Reinforcement learning has revolutionized our understanding of evolved systems and our ability to engineer systems based on a theoretical framework for understanding how to maximize expected reward. However, time delays between the observation and action are estimated to be roughly ~150 ms for humans, and this should affect reinforcement learning algorithms. We reformulate the Markov Decision Process framework to include time delays in action, first deriving a new Bellman equation in a way that unifies previous attempts and then implementing the corresponding SARSA-like algorithm. The main ramification-- potentially useful for both evolved and engineered systems-- is that, when the size of the state space is lower than that of the action space, the modified reinforcement learning algorithms will prefer to operate on sequences of states rather than just the present state with the length of the sequence equal to one plus the time delay. |
Tuesday, March 21, 2023 6:24AM - 6:36AM |
LL09.00008: How toe spacing affects impact dynamics during passive "foot" intrusions into granular media Simon J Thill Although efficient locomotion on granular media is challenging for animals and robots, some "sand-specialist" lizards have evolved to run rapidly on sand. Preliminary results from a study of subsurface foot movement revealed that these species space their toes approximately 3-5 particle diameters (pd) apart during a step, whereas slower, non-sand-specialist lizards often use spacings outside of this range. This is especially interesting because simulations and experiments have found that two horizontal, parallel cylinders intruded into a granular medium experience maximum force when spaced approximately 3 pd apart. To understand how toe spacing influences the intrusion dynamics of lizard feet, we studied 3D printed models of a simplified 3-toed "foot" dropped vertically into poppy seeds. Models with toes spaced 1, 3, 5, and 7 pd apart were released so as to achieve 3 intrusion speeds in the range used by running lizards (n = 5 trials/condition). High speed (1069 fps) video and 2-axis impact force measurements allowed us to measure how the force, torque, and work exerted by the granular medium during intrusion depends on toe spacing and speed. We relate these findings to lessons for the design of both robotic and prosthetic feet intended to navigate complex terrain. |
Tuesday, March 21, 2023 6:36AM - 6:48AM Author not Attending |
LL09.00009: Noise and local interactions in milling whirligig beetles Vishwesha Guttal, Shashi Thutupalli, Jitesh Jhawar A plethora of spectacular patterns of collective behaviour in animals exist in nature. Many studies analyse high-resolution data of collective motion of animal groups and infer the nature of local interactions that explain the observed behaviour. However, these studies most often ignore the role of noise. Here, we quantify the stochasticity in the time series dynamics of milling swarms of whirligig beetles (Gyrinidae dineutes) and show that it reveals insights on the local interactions that organisms follow. We apply the approach of data-driven discovery of dynamical equations to high-resolution time series data of milling beetle swarms, and obtain a stochastic differential equation (SDE) that governs the underlying dynamics. Analysis of the terms of SDE, together with analyses of agent-based models, reveal that beetles are likely changing their local interaction rules with group size. Specifically, we infer that beetles predominantly follow the simple rule of copying a randomly chosen individual at small group sizes; in larger groups, however, beetles follow a higher-order interaction rule. Furthermore, we find evidence for a simple negative interaction -- where a pair of beetles occasionally split. Our study shows the surprising possibility of inferring underlying modes of interactions between individuals via analysis of noise. |
Tuesday, March 21, 2023 6:48AM - 7:00AM |
LL09.00010: Pattern dynamics and stochasticity of the brain rhythms Yuri A Dabaghian, Clarissa Hoffman, Daoyn Ji, Jingheng Cheng We adopt recently introduced mathematical concepts of pattern stochasticity to quantifying transient behavior of extracellular fields at "temporal mesoscale." Currently, the mean extracellular fields (or any biological signals for that matter) are analyzed based either on their instantaneous parameters, agnostic of protracted behaviors, or time-averaged characteristics, which highlight mean trends. What remains unexplored, is the actual structure of waves—their shapes and patterns over finite timescales. We offer a methodology that is sensitive to individual, time localized features and yet allows describing a given waveform as a single entity, without defeaturing, putting each pattern, as a whole, into a statistical perspective. This approach allows attributing precise meaning to commonly used intuitive notions such as a brain wave's "regularity," "typicality," or "orderliness," and affords distinguishing statistically mundane wave patterns from atypical ones (e.g., atypically periodic or excessively time-cluttering) as well as capturing transitions between them. Applying these analyses to local field potentials recorded in mice hippocampi and correlating wave-pattern dynamics with changes in the animals' motor activity, we demonstrate motion-modulated changes of the wave's cadence, an antiphase relationship between orderliness and acceleration, spatial selectiveness of patterns, coupling to the animals' attention and other behavioral parameters. These results offer a complementary—morphological—description of the brain waves' structure, dynamics, and functionality, providing a novel perspective om neuronal circuits' dynamics and functionality. |
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