Bulletin of the American Physical Society
APS March Meeting 2021
Volume 66, Number 1
Monday–Friday, March 15–19, 2021; Virtual; Time Zone: Central Daylight Time, USA
Session S12: Physics of Neural Systems ILive
|
Hide Abstracts |
Sponsoring Units: DBIO Chair: Andrew Leifer, Princeton University; John Beggs, Indiana Univ - Bloomington |
Thursday, March 18, 2021 11:30AM - 11:42AM Live |
S12.00001: Synchronization on the random graph with non-linear interaction: application to breathing. Valentin Slepukhin, Sufyan Ashhad, Jack Feldman, Alex Levine We consider a system of leaky integrate-and-fire neurons interacting through a lognormal distribution of synaptic weights on random, directed graphs. This system is a good approximation of the preBötzinger Complex, the central pattern generator that sets the inspiratory rhythm in mammals by periodic bursting. We validate the model by comparing its prediction with the results of external burst initiation by the stimulation of a small subset of neurons in the preBötzinger Complex, which leads to synchronized spiking in the network that preceeds the burst of inspiratory activity. Comparing the probability of a burst and the time delay between that stimulation and the burst as the function of number of stimulated neurons, we obtain the quantitative fit to the experimental data with no free parameters. We then explore the process of the burst initiation in this model by asking the question: what features of the network and the stimulated neurons determine whether the burst will occur or not? Using the graph theory and a simplified version of the model, we suggest a list of quantities that we expect to be good predictors of the burst and evaluate the performance of these quantities using machine learning. |
Thursday, March 18, 2021 11:42AM - 11:54AM Live |
S12.00002: Stimulus information in collective response of tree networks of excitable elements Kanishk Chauhan, Ali Khaledi-Nasab, Peter A. Tass, Alexander B Neiman Tree-like branched myelinated dendrites characterize the morphology of certain sensory neurons with excitable nodes of Ranvier at every branch point and at leaves. Such a morphology can be modeled as tree networks of diffusively coupled excitable elements. Only the leaf nodes (the nodes at the branch endings) receive the stimulus and other random inputs from the surroundings and engender action potentials that propagate through the tree and reach the central node. We quantify the collective response generated by the central node by mutual information (MI) between the stimulus and the response. We show that in the strong coupling limit, the MI is determined by the number of nodes and leaves. At the same time, it is insensitive to the particular connectivity and the distribution of the stimulus among the leaf nodes. In an intermediate coupling regime, however, MI may strongly depend on the distribution of the stimulus. We identify a mechanism behind the competition between the leaf nodes and show that the background firing of unstimulated branches can occlude a localized stimulus. Furthermore, we show that the MI can be enhanced by proper stimulus localization and tuning of coupling. |
Thursday, March 18, 2021 11:54AM - 12:06PM Live |
S12.00003: Information tradeoffs in receptor arrays Caroline Holmes, William S Bialek Organisms sense the world through arrays of receptor cells, such as photoreceptors on the retina. In cases such as the compound eyes of insects, these arrays are nearly crystalline, while in others, such as the human retina, sampling is much less regular. Ordered arrays gather more information, but this comes at the cost of specifying the positions of all the cells. We explore this tradeoff between bits of sensory information and bits of positional information. This problem maps to an equilibrium statistical mechanics problem for the positions of the receptor cells, with interactions that depend on the correlation structure of the input signal. We find limits where the cost of disorder is small and where it is larger, and we try to relate these results to the diversity of structures that we find across different organisms. |
Thursday, March 18, 2021 12:06PM - 12:18PM Live |
S12.00004: Controlling phase-locked functional connectivity states with local perturbations to multi-regional brain circuits Evangelia Papadopoulos, Demian Battaglia, Danielle Bassett Oscillatory synchrony is hypothesized to support information flow between brain regions, with different phase-locked patterns enabling flexible “functional connectivity”. Past work has proposed multistable phase-locking as a way for fixed networks to yield multiple functional states, without the need to rewire anatomical links. It is thus important to understand how state selection could be controlled to achieve on-demand reconfiguration of functional connectivity. Here, we investigate functional state control in small model networks of coupled oscillatory neural masses. In particular, starting with a deterministic system that exhibits multistability, we study the network response to external signals targeting only a single area. We identify conditions under which control signals (i) are ineffective, (ii) slightly modulate the current functional state (“state morphing”), or (iii) trigger transitions to topologically different functional connectivity motifs (“state switching”). We then show that these control strategies can also extend to a stochastic regime where oscillations are more irregular and where phase-locking is transient. These results add to a growing literature highlighting that control of dynamical multistability may provide a basis for flexible network operation. |
Thursday, March 18, 2021 12:18PM - 12:30PM Live |
S12.00005: Stimulation-induced long-lasting desynchronization of plastic neuronal networks Justus Kromer, Peter A. Tass Excessive neuronal synchrony is a hallmark of several brain disorders, such as Parkinson’s disease. Brain stimulation may counteract abnormal synchrony and suppress symptoms. Exploiting synaptic plasticity, modern theory-based approaches deliver spatio-temporal stimulus patterns that cause a reshaping of synaptic connectivity. By driving neuronal networks into an attractor of a stable desynchronized state, this may cause desynchronization effects that outlast stimulation. Corresponding long-lasting desynchronization effects were demonstrated in animal and clinical studies. |
Thursday, March 18, 2021 12:30PM - 12:42PM Live |
S12.00006: Decoding locomotion from population neural activity in moving C. elegans Kelsey Hallinen, Ross Dempsey, Monika Scholz, Xinwei Yu, Ashley Linder, Francesco Randi, Anuj Sharma, Joshua Shaevitz, Andrew M Leifer We investigate neural representations of locomotion by recording calcium activity from the majority of neurons in the compact brain of the nematode C. elegans as it crawls freely. We find neurons tuned to features of the animal’s spontaneous locomotion, such as its velocity and gross body curvature. We developed a population decoder using linear ridge regression that predicts properties of the animal’s current locomotion from its population neural activity. We find that our population decoder outperforms best single neuron models for both velocity and curvature and captures a wider range of behavioral outputs. We also labeled the AVA neuron pair and investigated its role in the population. Finally we studied differences between population neural activity in the same worm when it is moving compared to immobilized. We find an increase in the magnitude of the correlation of the neurons during immobilization and recover previously reported stereotyped neural trajectories. |
Thursday, March 18, 2021 12:42PM - 12:54PM Live |
S12.00007: Experimental Evidence of Quasicriticality in the Brain Leandro Fosque, Rashid Williams-Garcia, Gerardo Ortiz, John Beggs
|
Thursday, March 18, 2021 12:54PM - 1:06PM Live |
S12.00008: Scaling Theory in Axons and Dendrites Paheli Desai-Chowdhry, Alex Brummer, Van Savage Neurons, the fundamental cellular units of the nervous system, have complex branching processes that connect to one another. A neuronal network, much like the cardiovascular system, is a resource distribution network subject to biophysical constraints. Previous work has focused on deriving power laws that illustrate the relationship between geometric scaling of cardiovascular vessel length and radius and fluid flow, space-filling, and whole organism metabolism. Here, we explore the possibility of extending this theoretical framework to analyze structural properties of neurons, aiming to understand the relationship between neuron structure and function. We test theoretical predictions against scaling ratios extracted from image data. We find that the distributions of scaling ratios in neuron processes across brain regions and species are nearly identical to those observed in cardiovascular networks, supporting the notion that a unifying framework underlies these diverse biological systems. |
Thursday, March 18, 2021 1:06PM - 1:18PM Live |
S12.00009: Predictive capacity of a dynamical system Kamesh Krishnamurthy, William S Bialek, Anna Frishman, Xiaowen Chen
|
Thursday, March 18, 2021 1:18PM - 1:30PM Live |
S12.00010: Associative memory by generalized holography Ernest Bergmann, W Fowler The concept of holography is generalized from idealized thick emulsion optical holography: to finite numbers of pixels, to other linear transforms than only the wave equation (using rectangular matrices), and to other non-linear recording functions. Using multiple exposures, two related computer simulations displaying auto-associative memory are demonstrated. These simulations consist of large, randomly connected, feed forward neural networks. This work is motivated by possible functioning of the brain’s memory and recognition. These simulations succeed in producing such associative memory based upon the statistics of large numbers and redundancy. They support arbitrarily large redundancies and fault tolerances. All memory is highly distributed. These simulations do not rely upon free-space assumptions, nor upon a wave equation. Information is assumed to be transmitted in a base-band manner, not through a carrier based manner (in contrast to optical holography). Some future directions are suggested. |
Thursday, March 18, 2021 1:30PM - 1:42PM Live |
S12.00011: Conservation of underlying stereotypy in olfaction. Or Alus, Marcelo Magnasco, Matt Wachowiak The first stage of any sensory processing begins with the transformation from the external signal to the abstract space defined by receptor activity. For olfaction it is defined by the olfactory receptors (OR) located on the membrane of Olfactory Sensory Neurons (OSN) in the olfactory epithelium. Unlike other senses there is not yet a coherent definition of the abstract space defining the external signal (Ligands) within reach. While each OSN expresses a single OR gene, all OSN expressing the same gene send projections to the same place in the olfactory bulb. Therefore calcium imaging signal from the olfactory bulb is a good measure of the amount of receptors activated. Given data of mice glomeruli responses to a large number of chemicals we show conservation across animals of the underlying activity and therefore of the encoding transformation carried out by the receptors. The role of concentration effects is also discussed. Furthermore we assign corresponding pairs based on the similarity of the responses between glomeruli of different animals. We demonstrate that the geometrical locations of the glomeruli are therefore conserved. |
Thursday, March 18, 2021 1:42PM - 1:54PM Live |
S12.00012: Impact of Sodium Channel Distribution in the Axon Initial Segment on the Initiation and Backpropagation of Action Potentials Benjamin M Barlow, Bela Joos, Andre Longtin We are interested in the biophysics of forward and backward propagation of action potentials (APs), as they are both important for learning. The axon initial segment (AIS) initiates APs in a variety of neurons. Pyramidal cells contain two types of voltage-gated sodium channel: Nav1.2 (high threshold) and Nav1.6 (low threshold). These channels are not uniformly distributed in the AIS. The density of Nav1.2 is greatest near the soma, and Nav1.6 density peaks further down the AIS, away from the soma. While this distribution is observed, its purpose remains unclear. Counterintuitively, published simulations suggest that concentration of high threshold channels near the soma lowers the threshold for backpropagation. We find that this is true when stimulating at the axon. However, the opposite is true when stimulating at the soma. We propose an intuitive explanation: the cell becomes more excitable —including backpropagation— when the Nav distribution places more low-threshold channels closer to the site of stimulation. Our results suggest that the observed distribution increases the backpropagation threshold. We use the time-evolution of integrated Nav current to support this description, and discuss the effect of parameters such as AIS geometry. |
Thursday, March 18, 2021 1:54PM - 2:06PM Live |
S12.00013: Automatic Neuron Correspondence Prediction In C.elegans With Deep Learning Xinwei Yu, Matthew S Creamer, Andrew M Leifer The neurons in C. elegans are well characterized, named, and have stereotyped locations. The ability to find corresponding neurons across animals is needed to compare neural signals, study variability, or collect statistics. Variability in neural position across animals makes it hard to find this correspondence. We present a deep learning method based on the transformer architecture for finding neural correspondence and apply it to the brain of C. elegans. The model learns to extract features that capture relative spatial organization among pairs of neurons within and across individuals. The model is trained exclusively on empirically derived synthetic data and it is used to predict correspondence between real animals via transfer learning. When compared against held-out human-annotated ground-truth Neuropal data, the model finds the correct correspondence for 65.8% of labeled neurons. With added genetically encoded color labeling, the model finds correspondence for 78.1% of labeled neurons. Unlike previous methods, this approach requires no human annotation, straightening, or prepossessing of the animal's pose. The model is parallelable and much faster than previous methods. |
Thursday, March 18, 2021 2:06PM - 2:18PM Not Participating |
S12.00014: A mathematical model of reward-mediated learning in drug addiction Davide Maestrini, Maria D'Orsogna, Tom Chou We propose a mathematical model combining the so-called opponent |
Thursday, March 18, 2021 2:18PM - 2:30PM Live |
S12.00015: Statistical Motor Biomarkers Characterizing age-dependence in Neurodevelopmental Disorders Khoshrav Doctor, Di Wu, Aditya Phadnis, John Nurnberger, Martin Plawecki, Jorge Jose We studied the statistical properties of motor kinematics of individuals with Neurodevelopmental Disorders (NDD). Humans show random arm kinematic trajectories that appear smooth to the naked eye. By using high definition motion capture sensors attached to moving arms we found millisecond time fluctuations leading to a statistical biomarker [1]. We broadened our previous studies of subjects with Autism Spectrum Disorder (ASD), to subjects with Attention Deficit Hyperactivity Disorder (ADHD), Comorbid ASD and ADHD. The subjects moved their arm towards touch screen targets appearing randomly in the monitor. We measured the statistical properties of the angular velocity, acceleration, and jerk of upper extremity movements. We introduce a new Fano-like Factor (FF) biomarker of the distribution of fluctuations in the kinematic variables. Implementing a Support Vector Machine Learning tool allowed us a separation in the cognitive conditions of the NDD subjects studied. Subjects with ASD+ADHD show an age dependent change as well as a small sample of ADHD subjects. This is consistent with clinical studies that record improvement in clinical ADHD severity and impairment with age. |
Follow Us |
Engage
Become an APS Member |
My APS
Renew Membership |
Information for |
About APSThe American Physical Society (APS) is a non-profit membership organization working to advance the knowledge of physics. |
© 2024 American Physical Society
| All rights reserved | Terms of Use
| Contact Us
Headquarters
1 Physics Ellipse, College Park, MD 20740-3844
(301) 209-3200
Editorial Office
100 Motor Pkwy, Suite 110, Hauppauge, NY 11788
(631) 591-4000
Office of Public Affairs
529 14th St NW, Suite 1050, Washington, D.C. 20045-2001
(202) 662-8700