Bulletin of the American Physical Society
APS March Meeting 2022
Volume 67, Number 3
Monday–Friday, March 14–18, 2022; Chicago
Session G03: Neural Systems IIFocus Recordings Available
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Sponsoring Units: DBIO Chair: Philipp Fleig, University of Pennsylvania Room: McCormick Place W-176A |
Tuesday, March 15, 2022 11:30AM - 12:06PM |
G03.00001: Sequential and efficient neural-population coding of complex task information Invited Speaker: Sue Ann Koay Recent work has highlighted that many types of variables are represented in each neocortical area. How can these many neural representations be organized together without interference, and coherently maintained/updated through time? We recorded from excitatory neural populations in posterior cortices as mice performed a complex, dynamic task involving multiple interrelated variables. The neural encoding implied that highly-correlated task variables were represented by less-correlated neural-population modes, while pairs of neurons exhibited a spectrum of signal correlations. This finding relates to principles of efficient coding, but notably utilizes neural-population modes as the encoding unit, and suggests partial whitening of task-specific information where different variables are represented with different signal-to-noise levels. Remarkably, this encoding function was multiplexed with sequential neural dynamics yet reliably followed changes in task-variable correlations throughout the trial. We suggest that neural circuits can implement time-dependent encodings in a simple way using random sequential dynamics as a temporal scaffold. |
Tuesday, March 15, 2022 12:06PM - 12:42PM |
G03.00002: Effect of Geometric Complexity on Intuitive Model Selection Eugenio Piasini, Vijay Balasubramanian, Joshua I Gold Occam's razor is the principle stating that, all else being equal, simpler explanations for a set of observations are to be preferred to more complex ones. This idea can be made precise in the context of statistical inference, where a geometrical characterization of statistical model complexity emerges naturally from an approach based on Bayesian model selection. The broad applicability of this formulation suggests a normative reference point for decision making under uncertainty. However, little is known about if and how humans intuitively quantify the complexity of competing interpretations of noisy data. In this work, first, we extend the geometrical characterization of model complexity to apply to models with bounded parameters, and second, we measure the sensitivity of naive human subjects to statistical model complexity. Our data show that human subjects bias their decisions in favor of simple explanations based not only on the dimensionality of the alternatives (number of model parameters), but also on finer-grained aspects of their geometry, such as volume, curvature, and presence of prominent boundaries. Our results imply that principled notions of statistical model complexity have direct quantitative relevance to human decision making. |
Tuesday, March 15, 2022 12:42PM - 12:54PM |
G03.00003: Functional imaging and quantification of multi-neuronal olfactory responses in C. elegans Albert Lin For many animals, chemosensation is the primary sensory modality through which they perceive the world. To detect and identify a wide range of chemical compounds, animals employ a large number of chemosensory neurons, making olfactory responses inherently collective responses. Thus, multi-neuronal imaging methods are ideal for understanding the neuronal basis of odor coding. Here, we studied the ensemble-level representation of odor identity in the nematode C. elegans. We recorded from all chemosensory neurons in the animal simultaneously while presenting a broad panel of olfactory stimuli in a highly controlled manner using a microfluidics device. Observing the dynamics of these neurons has allowed us to build a quantitative and comprehensive picture of the way the olfactory system in C. elegans consolidates and represents high-dimensional sensory information. We found that collectively, the sensory neurons encode odor identity and intensity. We also described the roles of each of the individual sensory neurons in olfactory coding, finding diverse dose response and tuning properties across neuron classes. |
Tuesday, March 15, 2022 12:54PM - 1:06PM |
G03.00004: Biomechanical and Ionic Excitability Within Developing Brain Cell Networks Sylvester J Gates, Wolfgang Losert Within the brain, multiple cell types work together to allow for complex processes like memory, learning, and cognition. During development neural progenitor cells (NPC) give rise to two main brain cell types, neurons and astrocytes. While a large body research studies the communication of neurons, there is less studied about the network communication of neurons and astrocytes, especially in the context of networks developing from NPCs. To understand the biomechanical excitable system, the key cellular scaffolding protein actin is fluorescently labeled and dynamics are analyzed using a computer algorithm called optical flow. We examine these actin dynamics in conjugation with electrical/ionic communication monitored via calcium dynamics throughout the development of biological neural networks of both neurons and astrocytes from human NPCs in parallel. This work shows that pharmacologically perturbing actin impacts the functional communication as measured by calcium in the developing neural networks – suggesting new avenues to understanding the impact of biomechanical elements in neural communication. |
Tuesday, March 15, 2022 1:06PM - 1:18PM |
G03.00005: Functional connectivity in the C. elegans brain Francesco Randi, Anuj K Sharma, Sophie Dvali, Andrew M Leifer The nematode C. elegans plays an important role in the study of nervous systems. Thanks to its unique properties, we have an exquisitely detailed knowledge of its brain, including its complete anatomical connectome, that describes the map of the connections between all the neurons in its brain. While the connectome has been instrumental in elucidating specific circuits, it has not allowed the community to fully understand whole-brain neural dynamics. Neural dynamics is governed by functional connectivity, the properties of the interactions between neurons, which include their strength, sign, direction, and temporal properties. In contrast, the connectome only tells us which neurons interact with which others. Measuring functional connectivity is therefore fundamental to understanding the brain. |
Tuesday, March 15, 2022 1:18PM - 1:30PM |
G03.00006: Recurrent activity in neuronal avalanches Tyler M Salners A new statistical analysis of large neuronal avalanches observed in mouse and rat brain tissues reveals a substantial degree of recurrent activity and cyclic patterns of activation not seen in smaller avalanches. To explain these observations, we adapted a model of structural weakening in materials. In this model, dynamical weakening of neuron spiking thresholds closely replicates experimental avalanche size distributions, spiking number distributions, and patterns of cyclic activity. This agreement between model and data suggests that a mechanism like dynamical weakening plays a key role in recurrent activity found in large neuronal avalanches. We expect these results to illuminate the causes and dynamics of large avalanches, like those seen in seizures. |
Tuesday, March 15, 2022 1:30PM - 1:42PM |
G03.00007: Evidence of quasicritical dynamics across the life span: A MEG study Leandro J Fosque, John M Beggs, Gerardo Ortiz, Marzieh Zare, Abolfazl Alipour, Rashid Williams-García, Mahdi Sarikhani
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Tuesday, March 15, 2022 1:42PM - 1:54PM |
G03.00008: Simpler models of neuronal activity via compression of interactions Luisa f Ramirez, William S Bialek New experimental methods make it possible to record the simultaneous activity of thousands of neurons in the brain. Theoretical models that capture the behavior of these large populations, though, continue to be a challenge, often leading to an explosion of complexity that needs simplification. In condensed matter physics, simple models often work, quantitatively, but simplifications rest in part in the pairwise and local microscopic interactions. Here we explore the idea that the influence of the whole system on one degree of freedom can be compressed, in the sense of information theory. We study populations of neurons in the salamander retina and the mouse hippocampus, and focus on interactions between a single neuron and groups of eight other cells, a scale for which data allow complete sampling. We find that we can capture the influence of the group on one cell with only ten states, significantly less than the 256 possible states. Inspired by the renormalization group, we iterate this strategy and find, surprisingly, that the number of states needed to describe the interactions grows linearly with the number of neurons included. Compared with the expected exponential growth, compression of interactions seems to provide a path to build simpler models of neuronal activity. |
Tuesday, March 15, 2022 1:54PM - 2:06PM |
G03.00009: Universal scaling in neural activity: a renormalization-group perspective Guillermo Barrios Morales, Miguel A Muñoz, Serena di Santo The brain is in a state of perpetual reverberating neural activity, even in the absence of specific tasks or stimuli. Shedding light onto the origin and functional meaning of such an activity is essential to understand how the brain transmits, processes and stores information. An inspiring yet still controversial conjecture proposes that some statistical features of empirically-observed neural activity can be understood by assuming that brain networks operate in a dynamical regime close to the edge of a phase transition, and that the resulting critical behavior, with its concomitant scale invariance, provides neuronal networks with crucial functional advantages. To shed further light on these issues, here we analyze activity recordings of thousands of individual neurons across regions in the mouse brain. We employ a variety of state-of-the-art theoretical approaches, including a recently-proposed phenomenological renormalization group approach, as well as methods that allow us to infer the overall dynamical state of a neural population from measurements of pairwise covariances. By synergetically combining these ideas, we find strong signatures of scale-invariance which turns out to be quite robust or "quasi-universal" across brain regions, providing evidence that these areas may operate ---to a greater or a lesser extent--- at the edge of instability. |
Tuesday, March 15, 2022 2:06PM - 2:18PM |
G03.00010: A novel modeling approach to identify groups of coordinated neurons which accounts for high-order neural correlations Clelia de Mulatier, Jean-Hugues Lestang, Songhan Zhang, Lalitta Suriya-Arunroj, Yale Cohen, Vijay Balasubramanian Perception and behavior are mediated by interconnected neurons that form neural circuits. Traditionally, groups of neurons exhibiting coordinated activity within these circuits have been detected through the use of dimensionality reduction techniques. Here, we proposed a novel approach that is based on modeling groups of coordinated neurons with minimally complex spin models. These models are maximum entropy models with interactions of arbitrary order that are grouped into "communities", with no interactions between the communities and all possible interactions within each community. With these models, we can perform exact Bayesian model selection. Besides, the novelty of our approach is that it accounts for high-order neural activity patterns (i.e. multi-neuronal activity motifs) in the detection of groups of correlated neurons. Additionally, we show that this technique provides robust predictions, without the need for statistical thresholding. We discuss the strengths and weaknesses of both the traditional approach and our approach, and illustrate our findings on artificial data and on real neural data obtained from the auditory cortex. Our new approach is a promising preliminary direction in the search for functional connectivity structures going beyond pairwise interactions. |
Tuesday, March 15, 2022 2:18PM - 2:30PM |
G03.00011: Full-stack inference of low-dimensional effective models Alexandre René, Andre Longtin Key to understanding the dynamics of large neural populations is the use of low-dimensional models, which must attempt to capture a rich variety of observed behaviors while maintaining interpretability and analytical tractability. One approach is to keep a simple functional form, but adjust its parameters to capture the effect of neglected interactions. We are interested in finding good such effective parameters for population models of interacting neurons, which cannot feasibly be derived from a more detailed theory. |
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