arXiv subjects

Gustavo Menesse

Publications and source records attributed to Gustavo Menesse.

5 recordsLinked to original sources

arXiv2025

Astrocyte-Mediated Higher-Order Control of Synaptic Plasticity

The dynamics of higher-order topological signals are increasingly recognized as a key aspect of the activity of complex systems. A paradigmatic example are synaptic dynamics: synaptic efficacy changes over time driven by different mechanisms. Beyond traditional node-driven short-term plasticity mechanisms, the role of astrocyte modulation through higher-order interactions, in the so-called tripartite synapse, is increasingly recognized. However, the competition and interplay between node-driven and higher-order mechanisms have yet to be considered. Here, we introduce a simple higher-order model of the tripartite synapse accounting for astrocyte-synapse-neuron interactions in short-term plasticity. In the model, astrocyte gliotransmission and pre-synaptic intrinsic facilitation mechanisms jointly modulate the probability of neurotransmitter release at the synapse, generalizing previous short-term plasticity models. We investigate the implications of such mechanisms in a minimal recurrent motif -- a directed ring of three excitatory leaky integrate-and-fire neurons -- where one neuron receives external stimulation that propagates through the circuit. Due to its strong recurrence, the circuit is highly prone to self-sustained activity, which can make it insensitive to external input. By introducing higher-order interactions among different synapses through astrocyte modulation, we show that higher-order modulation robustly stabilizes circuit dynamics and expands the parameter space that supports stimulus-driven activity. Our findings highlight a plausible mechanism by which astrocytes can reshape effective connectivity and enhance information processing through higher-order structural interactions -- even in the simplest recurrent circuits.

q-bio.NC↗

arXiv2024

Integrated Information Decomposition Unveils Major Structural Traits of $In$ $Silico$ and $In$ $Vitro$ Neuronal Networks

The properties of complex networked systems arise from the interplay between the dynamics of their elements and the underlying topology. Thus, to understand their behaviour, it is crucial to convene as much information as possible about their topological organization. However, in a large systems such as neuronal networks, the reconstruction of such topology is usually carried out from the information encoded in the dynamics on the network, such as spike train time series, and by measuring the Transfer Entropy between system elements. The topological information recovered by these methods does not necessarily capture the connectivity layout, but rather the causal flow of information between elements. New theoretical frameworks, such as Integrated Information Decomposition ($\Phi$-ID), allow to explore the modes in which information can flow between parts of a system, opening a rich landscape of interactions between network topology, dynamics and information. Here, we apply $\Phi$-ID on $in$ $silico$ and $in$ $vitro$ data to decompose the usual Transfer Entropy measure into different modes of information transfer, namely synergistic, redundant or unique. We demonstrate that the unique information transfer is the most relevant measure to uncover structural topological details from network activity data, while redundant information only introduces residual information for this application. Although the retrieved network connectivity is still functional, it captures more details of the underlying structural topology by avoiding to take into account emergent high-order interactions and information redundancy between elements, which are important for the functional behavior, but mask the detection of direct simple interactions between elements constituted by the structural network topology.

q-bio.NC↗

arXiv2023

Information dynamics of $in\; silico$ EEG Brain Waves: Insights into oscillations and functions

The relation between EEG rhythms, brain functions, and behavioral correlates is well-established. Some mechanisms underlying rhythm generation are understood, enabling the replication of brain rhythms $in\; silico$. This allows to explore relations between neural oscillations and specific neuronal circuits, helping to decipher the functional properties of brain waves. Integrated information Decomposition ($\Phi$-ID) framework relates dynamical regimes with informational properties, providing deeper insights into neuronal dynamic functions. Here, we investigate wave emergence in an excitatory/inhibitory (E/I) balanced network of IF neurons with short-term synaptic plasticity producing a diverse range of EEG-like rhythms, from low $\delta$ waves to high-frequency oscillations. Through $\Phi$-ID, we analyze the network's information dynamics elucidating the system's suitability for robust information transfer, storage, and parallel operation. Our study identifies also regimes that may resemble pathological states due to poor informational properties and high randomness. We found that $in\; silico$ $\beta$ and $\delta$ waves are associated with maximum information transfer in inhibitory and excitatory neuron populations, and the coexistence of excitatory $\theta$, $\alpha$, and $\beta$ waves associated to information storage. Also, high-frequency oscillations can exhibit either high or poor informational properties, shedding light on discussions regarding physiological versus pathological high-frequency oscillations. Our study demonstrates that dynamical regimes with similar oscillations may exhibit different information dynamics. Finally, our findings suggest that the use of information dynamics in both model and experimental data analysis, could help discriminate between oscillations associated with cognitive functions and those linked to neuronal disorders.

q-bio.NC↗

arXiv2023

Less is different: why sparse networks with inhibition differ from complete graphs

In neuronal systems, inhibition contributes to stabilizing dynamics and regulating pattern formation. Through developing mean field theories of neuronal models, using complete graph networks, inhibition is commonly viewed as one ``control parameter'' of the system, promoting an absorbing phase transition. Here, we show that for low connectivity sparse networks, inhibition weight is not a control parameter of the transition. We present analytical and simulation results using generic stochastic integrate-and-fire neurons that, under specific restrictions, become other simpler stochastic neuron models common in literature, which allow us to show that our results are valid for those models as well. We also give a simple explanation about why the inhibition role depends on topology, even when the topology has a dimensionality greater than the critical one. The absorbing transition independence of the inhibitory weight may be an important feature of a sparse network, as it will allow the network to maintain a near-critical regime, self-tuning average excitation, but at the same time, have the freedom to adjust inhibitory weights for computation, learning, and memory, exploiting the benefits of criticality.

cond-mat.dis-nn↗

arXiv2022

Homeostatic Criticality in Neuronal Networks

In self-organized criticality (SOC) models, as well as in standard phase transitions, criticality is only present for vanishing external fields $h \to 0$. Considering that this is rarely the case for natural systems, such a restriction poses a challenge to the explanatory power of these models. Besides that, in models of dissipative systems like earthquakes, forest fires, and neuronal networks, there is no true critical behavior, as expressed in clean power laws obeying finite-size scaling, but a scenario called "dirty" criticality or self-organized quasi-criticality (SOqC). Here, we propose simple homeostatic mechanisms which promote self-organization of coupling strengths, gains, and firing thresholds in neuronal networks. We show that with an adequate separation of the timescales for the coupling strength and firing threshold dynamics, near criticality (SOqC) can be reached and sustained even in the presence of significant external input. The firing thresholds adapt to and cancel the inputs ($h$ decreases towards zero). Similar mechanisms can be proposed for the couplings and local thresholds in spin systems and cellular automata, which could lead to applications in earthquake, forest fire, stellar flare, voting, and epidemic modeling.

nlin.AO↗