Statistical Physics ePosters

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].…

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…

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…

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…

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…

Different stimuli elicit different transient neural responses in the brain. How is the information represented in the parallel neuronal activity of a neuronal population? We investigate this question in Neuropixel recordings of awake behaving mice by inferring the stimulus class (e.g., visual or tactile) from the…

Since their introduction as a theoretical concept in the 70es by Amari, ring attractor networks have been a popular tool to explain orientation and space dependence of neural activity. Only recently, there has been an experimental proof that they are actually implemented in the fly's brain (Kim et al. 2017). While…

Brain rhythms in the alpha frequency range [8Hz-12Hz] are present in idling conditions and sleep. They have also an important role in cognition, e.g. they modulate activity of task-irrelevant neural populations, and enhances selective attention among others. A prominent occipital alpha wave emerges in mammals when the…

The effects of a global inhibition are studied on different models of neural networks, from the Wilson-Cowan model to networks of leaky integrate-and-fire neurons. These systems typically display a region of parameter space where two different states are possible, one of low and another of high activity with a…

Cortical neurons in vivo exhibit significant temporal variability, the source of which is crucial for understanding its computational and behavioural implications [1,2]. However, to match experimental observations, single-cell activity must fluctuate over much longer time scales than typical neural and synaptic time…

The spatio-temporal structure of brain dynamics at the macroscopic level has been hypothesized to reflect neuronal communication for cognitive processes. In particular, recent studies have focused on time-irreversibility and its relationship with consciousness [1,2]. Beyond this example of high-level cognitive…

In order to analyse complex networks in the cerebral cortex, different approaches are used in computational neuroscience: microscopic network models that consider every single neuron in simulations aiming for a realistic biological description as well as heuristic firing rate or neural-mass models which deliver a…

Activity dependent synaptic plasticity is widely believed to play the major role in learning and memory. Moreover, the robustness of formed memories is dependent on the stability of synapses. However, recent studies have shown that synapses undergo a significant volatility which are activity independent [1]. This…

For more than 20 years, a growing body of evidence indicates that the brain functions near a critical point, where neural activity is balanced between damping and amplification. One of the appealing benefits of criticality is its optimal information processing [1, 2]. In addition, deviations from criticality are…

Our goal is to extend the open thermodynamic model of cortical neurodynamic Freeman hypothesis using the theoretical Carnot and then Rankine Cycles to define biomarkers for mean field energy versus entropy change and relevancy to temperature. We are using the Rankine cycle to incorporate criticality which is at null…

Neuronal computation is mediated by spikes, yet it is unclear what the benefits of discrete spiking dynamics over continuous firing rates are. Many theoretical and computational studies treat single neurons as continuous units and their output as an effective firing rate. These works view spiking dynamics as a…