Bulletin of the American Physical Society
APS March Meeting 2020
Volume 65, Number 1
Monday–Friday, March 2–6, 2020; Denver, Colorado
Session P23: Physics of the Brain: Structure and Dynamics IIFocus
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Sponsoring Units: DBIO GSNP Chair: Marek Cieplak, Institute of Physics, Polish Academy of Sciences Room: 304 |
Wednesday, March 4, 2020 2:30PM - 3:06PM |
P23.00001: A Quantitative Kinematic Movement Biomarker Characterizing Neurodevelopmental Disorders Invited Speaker: Jorge Jose When we make reach-directed movements to an object, like a cup, there is an infinite number of possible trajectories that an arm could take to go from its initial position to the final target. These are not given by solving Newton’s equations but are controlled by the brain. Humans show highly stereotyped random trajectories with hand velocity profiles that appear smooth to the naked eye. However, by using high definition motion capture MEM sensors we have found that at finer millisecond time scales, away from naked eye detection, the kinematic variables have clear random peak fluctuations that were unknown previously. We have been able to directly connect, in an individualized way, these random fluctuations to the nature of the Neurodevelopmental Disorders (NDD) of children with autism or attention hyperactive disorders. These random peak fluctuations are very different, though, in healthy individuals. In this lecture I will describe the nature of experiments we carry out, the importance of fully filtering out the external electronic noise from neuronal noise. Me measure, angular velocity, linear acceleration, and the time derivative of the acceleration, known as jerk of importance in human kinematics. Based on the data measured over thousands of arm movement trials we developed a detailed statistical analysis that allowed us to uncover quantitative NDD biomarkers that a posteriori agreed with the clinical diagnoses for each individual studied. We further hypothesize that the brain tries to minimize the associated stochastic kinematic variable fluctuations describing the motions. The identified biomarkers quantitatively determine the illness severity, that could be used as a diagnostic tool by clinicians. |
Wednesday, March 4, 2020 3:06PM - 3:18PM |
P23.00002: Latent fields lead to emergence of scaling in simulated neurons Mia Morrell, Audrey Sederberg, Ilya M Nemenman Understanding activity of large populations of neurons is difficult due to the combinatorial complexity of possible cell-cell interactions. A remedy is to note that many systems may be macroscopically described with models simpler than the system's microscopic behavior. This has been probed via a coarse-graining procedure on experimental neural recordings, which shows over two decades of scaling in free energy, variance, eigenvalue spectra, and correlation time [1], hinting that a mouse hippocampus operates in a critical regime. We investigated whether this scaling behavior could be explained as a result of coupling the neural population to latent dynamic stimuli. We conducted simulations of conditionally independent binary neurons coupled to a small number of long-timescale stochastic fields with and without periodic spatial stimuli (depicting neural place cells) and replicated the coarse-graining shown in [1]. In a biologically relevant regime, we find that much of the observed scaling [1] may be recreated by this model. This suggests that aspects of the scaling may be explained by coupling to hidden dynamic processes, a ubiquitous trait of neural systems. |
Wednesday, March 4, 2020 3:18PM - 3:30PM |
P23.00003: Architectural Principles and Predictive Modeling of the Mammalian Connectome Zoltan Toroczkai, Ferenc Molnar, Ana-Rita Ribeiro Gomes, Maria Ercsey-Ravasz, Kenneth Knoblauch, Henry Kennedy Although mammalian brains show a massive, 5 orders of magnitude variation in weight, they present common processing features, implying that cortical computing is built on a scalable architecture. We hypothesize the existence of network architectural organizational principles in the mammalian brain, critical for efficient and hierarchically modular information processing. Extending our empirical, consistent tract-tracing network databases, we confirm the validity of the Exponential Distance Rule (EDR) in the macaque cortex, showing that the EDR is an architectural network invariant. We have also developed and cross-validated novel, machine learning based imputation algorithms, suitable for dense interareal networks, exploiting the weighted, directed and the spatially embedded nature of these networks. As we show, these algorithms can efficiently be used to guide further tract-tracing experiments, for example by identifying potential injection targets that would generate the largest information gain, after every new injection. |
Wednesday, March 4, 2020 3:30PM - 3:42PM |
P23.00004: A 2D Stochastic Lattice Model Describing the Self-Assembly of Synaptic Membrane Protein Domains Everest Law The regulation of neurotransmitter receptor domains on the postsynaptic membrane plays an important rule in signal transduction across synapses. Inspired by the interactions between glycine receptors and gephyrin scaffolds, we present a stochastic lattice reaction-diffusion model explaining receptor domain formation. Using reaction and diffusion rates consistent with experimental observations, our model reproduces the receptor/scaffold copy numbers and domain areas observed in nanoscopy experiments. The Turing instability in our model intuitively explains the pattern formation observed, without assuming the existence of patterns a priori. The present work is an extension of previous work where the same model is only studied in 1D. |
Wednesday, March 4, 2020 3:42PM - 4:18PM |
P23.00005: Dynamics of the intrinsically disordered proteins and neurodegeneration Invited Speaker: Marek Cieplak The equilibrium dynamics of the intrinsically disordered proteins is thought to consist of transitions between many basins in the free energy landscape whereas structured proteins stay in the vicinity of one native basin. We demonstrate this picture explicitly by studying networks defined on the discretized plane: conformational end-to-end distances vs. radii of gyration. The bin sizes are defined by time scales that span orders of magnitude. The networks, derived from all-atom and coarse-grained molecular dynamics simulations, are nearly scale invariant. The bin representation also provides insights into the folding process of the structured proteins and identifies regions that hinder folding. Intrinsically disordered proteins often lead to neurodegenerative diseases through a variety of mechanisms. Here, we focus on one such mechanism: jamming of the proteasomal protein degradation by transient conformations that contain knots. We show that such conformations arise at least for sufficiently long polyglutamine tracts and α-synuclein. The polyglutamine tracts are fused within huntingtin protein that is associated with the Huntington neurodegenerative disease. We show that the presence of knots in the tracts obstructs translocation through a model proteasome, especially when the knots are deep. The knots in polyglutamine may form in tracts that exceed about 40 residues. This fact explains the existence of a similarly sized length threshold above which there is an experimentally observed toxicity at the monomeric level. We also discuss emergence of knots in α-synuclein – the protein associated with the Parkinson disease. We show that these knots are either shallow or deep and last for about 3 – 5 µs, as inferred from an all-atom explicit-solvent 20 and 30 µs trajectories. We argue that their presence enhances aggregation of α-synucleins. In collaboration with: M. Chwastyk, L. Mioduszewski, and B. de Aquino. |
Wednesday, March 4, 2020 4:18PM - 4:30PM |
P23.00006: Alpha rhythm shapes the correlation landscape of avalanche dynamics across resting wakefulness Fabrizio Lombardi, Lucilla De Arcangelis, Hans Jurgen Herrmann, Oren Shriki Brain rhythms and neuronal avalanches form a fascinating dichotomy in the universe of emergent brain dynamics. The former have characteristic times and amplitudes, whereas the latter are scale-free, i.e. they lack characteristic spatio-temporal scales. Yet, they coexist and can be independently measured across different physiologic states, such as sleep and resting wake. However, underlying dynamics and functional role of such emergent dichotomy, as well as the dynamic relationship between brain rhythms and avalanches, remain not understood. In this talk I will show that, while the scale-free properties are universal and preserved across sleep and wake, avalanche dynamics is intimately connected to the dominant brain rhythm characterizing each of those physiologic states, and shows unique features in the resting awake state. Our results indicate that the alpha rhythm induces a dynamic transition in the functional organization of neuronal avalanches, and crucially determines the relationship between consecutive avalanches to balance the spatio-temporal dynamics of spontaneous brain activity. |
Wednesday, March 4, 2020 4:30PM - 4:42PM |
P23.00007: Non-perturbative renormalization group analysis of strongly-coupled spiking networks Braden Brinkman To fully explain how neural dynamics transmit information and perform computations, we need to understand the structure of the coordinated activity of neurons and their responses to external inputs. Given a model of neural dynamics and their synaptic connections, we would in principle achieve this goal by calculating the statistical and response functions of the network---a notoriously intractable task for all but the simplest models. While diagrammatic series have been successfully used to correct mean field predictions in weakly-coupled network models, they break down in networks with strong nonlinear behavior, demanding new approximation methods. |
Wednesday, March 4, 2020 4:42PM - 4:54PM |
P23.00008: Representation of nearby and infinitely far reference frames in the brain Shonali Dhingra, Mina Shahi, Mayank Mehta While navigating the world, our brain needs to keep track of the space around us. A brain region called the Hippocampus plays a significant role in our perception of space. Our work shows that hippocampal cells recognize space as a vector! Hippocampal Place Cells have been shown to keep track of the subject's absolute position. Using GLM, we show that hippocampal cells also keep track of the direction of the rat's position with respect to various frames of reference. These frames include infinitely far frames, thus keeping track of the canonical Head Direction[1]. We also show that these cells utilize various frames of reference at finite distances from the rat's current position. We call the origin of these reference frames as Anchor Points. We see that these cells code for such anchor points in coherent and stable ways, and we call such tuning of these cells as Anchor Fields. We observe that visual cues are capable of affecting various properties of these anchor tunings, including position of Anchor Points, size of Anchor Fields, percentage of cells tuned, and the direction of tuning with respect to these anchor points. We posit that hippocampal cells utilize these tunings to create a vectorial map of space in our brains. |
Wednesday, March 4, 2020 4:54PM - 5:30PM |
P23.00009: How behavioral and evolutionary constraints sculpt early visual processing Invited Speaker: Stephanie Palmer While efficient coding has been a successful organizational principle in visual neuroscience, to make a more general theory behavioral, mechanistic, and even evolutionary constraints need to be added to this framework. In our work, we use a mix of known computational goals and detailed behavioral measurements to add constraints to the notion of 'optimality' in vision. Accurate visual prediction is one such constraint. Prediction is essential for interacting fluidly and accurately with our environment because of the delays inherent to all brain circuits. In our work, we explore how our visual system makes these predictions, starting as early as the eye. We use techniques from statistical physics and information processing to assess how efficient, predictive vision emerges from these imperfect component parts. To test whether the visual system performs optimal predictive compression and computation, we compute the past and future stimulus information in populations of retinal ganglion cells, and in the vertical motion sensing system of the fly. In the fly, we anchor our calculations with published measurements of fast evasive flight maneuvers. This survival-critical behavior requires fast and accurate control of flight, which we show can be achieved by visual prediction via a specific wiring motif involving gap junction coupling. Developing a general theory of the evolution of computation is also a current research direction in our group. We use the repeated evolution of tetra-chromatic color vision in butterflies to test hypotheses about whether extant neural computations contain shadows of the evolutionary history of the organism. |
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