Physics ePosters

Higher brain regions are thought to use the computational advantages of sitting closer to a critical point to support more complex cognitive functions. Finding evidence for this hypothesis from neural data is challenging, requiring robust and accurate measures of the distance to criticality (DTC). Many existing…

Reconciling theoretical models with experimental data is a major challenge in neuroscience. We investigate how neurons that exhibit disorderly properties individually can produce orderly collective dynamics. Focusing on continuous-attractor networks, which model representations of continuous variables such as head…

The critical brain hypothesis proposes that neural networks operate near criticality to reap the computational benefits of accessing a wide range of timescales [1]. Proponents of this viewpoint highlight the presence of heavy-tailed (power-law) spatiotemporal correlations as markers of criticality in the brain [2].…

Information theory provides a powerful theoretical framework for a quantitative understanding of the structure and function of neural circuits. Despite this, the application of these ideas to neuroscience and machine learning has been hampered by the fact that the computation and maximization of mutual information is…

Understanding the neuromechanical principles of collective motor behaviors is a longstanding question at the intersection between neuroscience, motor control and fluid mechanics. Striking examples of these behaviors include the patterns formed by fishes swimming or birds flying in schooling formations. What is the…

Computational models have been seminal in expanding our understanding of how neural, biomechanical, or physical elements can give rise to behavior. With the forthcoming emergence of nearly complete connectomes of the central nervous system, musculature, and biomechanics of Drosophila, it is only now possible to…

Neural circuits display both input-driven activity that is necessary for the real-time control of behavior and internally generated activity that is necessary for memory, planning, and other cognitive processes. A key mediator between these intrinsic and evoked dynamics is arousal, an internal state variable that…

Despite wide recognition of the modular and hierarchical organization of neural circuits in the brain, our understanding of its influence on neural dynamics and information processing remains incomplete. To address this gap, we introduced a model of randomly connected neural populations (modules) and studied its…

Understanding the functional consequences of synaptic heterogeneity within neuronal populations is a critical challenge in neuroscience. Recent connectomic reconstructions, such as the complete fly brain connectome, provide key insights into the structural constraints on neural networks. One prominent observation from…

Healthy cerebral cortex function requires complex neuronal population activity with a diverse repertoire of timescales, from fleeting response to sensory input ($\sim$10-100 ms), to execution of complex body movements ($\sim 100-1000 ms$), to even longer timescales of working memory ($\sim 1-10 s$). How does cortex…

Mean-field models of neuronal populations in the brain have proven extremely useful to understand network dynamics and response to stimuli, but these models generally lack a faithful description of the fluctuations in the biologically relevant case of finite network size and connection probabilities $p<1$ (non-full…

Healthy brains exhibit a rich dynamical repertoire with flexible and varied spatiotemporal patterns replays on both microscopic and large scales. Neurodegenerative diseases reduce this functional repertoire. We hypothesize that microscopic dynamics must operate in or near a critical regime for the functional…

The vast majority of neurons in the vertebrate nervous systems transmits information in a digital manner via action potentials. Yet, a detailed understanding of the advantages of digital communication in the brain is still lacking. In our study, we investigate synaptic transmission in terms of an optimization problem,…

Expanding use of large-scale neural recordings has led to paradigm shifts in how neural computation is understood [1]. Technologies used for collecting these datasets face fundamental limitations ranging from power dissipation, penetration depth, and resolution [2] while facing additional practical challenges related…

The interplay of the calcium concentration and the anatomical structure at the presynaptic zone of presynaptic boutons is an important challenge of the relationship of form and function in (computational) neuroscience. A simple model system to study such basic effects is the neuromuscular junction of the Drosophila…

A key function of the brain is to process noisy sensory stimuli to make fast and accurate decisions. Canonical studies manipulate average noise to study stimulus effects on decision parameters, but how processing of exact stochastic fluctuations impacts the decision outcome, has remained largely unexplored. We…

We introduce OpenEyeSim 2.1, an extension of the OpenEyeSim platform that includes simulation of accommodation control and chromatic aberrations. It is suitable for developing models of the joint learning of visual representations, eye movements, and accommodation control in the perception-action cycle. The simulated…

Slow brain rhythms, observed during slow-wave sleep or seizures, often coincide with oscillations in extracellular potassium levels. These rhythms have a lower frequency compared to a typical neuron’s tonic firing. Mechanisms organizing these rhythms are believed to rely on synaptic interactions within the network or…

Brains are energy-hungry and evolution has driven them to work in an energy-efficient manner. In food restriction experiments, Padamsey et al. [1] have recently observed that individual neurons trade off energy consumption and information transmission. Specifically, neurons appear to reduce energy consumption by…

Understanding the neural basis of animal behavior is a central challenge in biology. In this work, we establish a connection between the spatio-temporal decision-making processes of animals and the ring attractor dynamics of neurons, which determine the organism's heading direction. We formulate a neural field model…

Biological neural network models with synaptic plasticity pose challenges for both theoretical and numerical analyses. The numerical bottleneck arises from the $N^2$ scaling of synapses. Additionally, the intricate coupling between neuron and synapse dynamics, along with plasticity-induced heterogeneity, hinders the…

Modeling non-invasive brain stimulation, particularly transcranial magnetic stimulation (TMS) of the primary motor cortex (M1), has been previously explored through simulation methods at different scales. However, the coupling of TMS induced electric fields to neural mass models is still largely unexplored, while…

Memory and learning are significantly influenced by long-term changes in synaptic strength, though the exact molecular mechanisms remain unclear. The postsynaptic density (PSD) is a critical component for storing and organizing synaptic long-term changes. The PSD itself is organized into complex nano-clusters, which…

Low-intensity ultrasound neuromodulation is a rapidly emerging technology due to its non-invasiveness and millimeter-scale precision. Despite numerous experimental settings demonstrating that ultrasonic waves can modulate the neural activity in different brain areas, the interaction between the ultrasonic source and…