Biology subjects

Mato, G.

Publications and source records attributed to Mato, G..

5 recordsLinked to original sources

bioRxiv2025

Statistics of correlations in nonlinear recurrent neural networks

The statistics of correlations are central quantities characterizing the collective dynamics of recurrent neural networks. We derive exact expressions for the statistics of correlations of nonlinear recurrent networks in the limit of a large number N of neurons, including systematic 1/N corrections. Our approach uses a path-integral representation of the networks stochastic dynamics, which reduces the description to a few collective variables and enables efficient computation. This generalizes previous results on linear networks to include a wide family of nonlinear activation functions, which enter as interaction terms in the path integral. These interactions can resolve the instability of the linear theory and yield a strictly positive participation dimension. We present explicit results for power-law activations, revealing scaling behavior controlled by the network coupling. In addition, we introduce a class of activation functions based on Pade approximants and provide analytic predictions for their correlation statistics. Numerical simulations confirm our theoretical results with excellent agreement.

neuroscience↗

bioRxiv2024

Chaotic synchronization in adaptive networks of pulse-coupled oscillators

Ensembles of phase-oscillators are known to exhibit a variety of collective regimes. Here, we show that a simple mean-field model involving two heterogenous populations of pulse-coupled oscillators, exhibits, in the strong-coupling limit, a robust irregular macroscopic dynamics. The resulting, strongly synchronized, regime is sustained by a homeostatic mechanism induced by the shape of the phase-response curve combined with adaptive coupling strength, included to account for energy dissipated by the pulse emission. The proposed setup mimicks a neural network composed of excitatory and inhibitory neurons.

neuroscience↗

bioRxiv2020

On the complex interplay between spectral harmonicity and different types of cross frequency couplings in non linear oscillators and biologically plausible neural network models

BackgroundCross-frequency coupling (CFC) refers to the non linear interaction between oscillations in different frequency bands, and it is a rather ubiquitous phenomenon that has been observed in a variety of physical and biophysical systems. In particular, the coupling between the phase of slow oscillations and the amplitude of fast oscillations, referred as phase-amplitude coupling (PAC), has been intensively explored in the brain activity recorded from animals and humans. However, the interpretation of these CFC patterns remains challenging since harmonic spectral correlations characterizing non sinusoidal oscillatory dynamics can act as a confounding factor. MethodsSpecialized signal processing techniques are proposed to address the complex interplay between spectral harmonicity and different types of CFC, not restricted only to PAC. For this, we provide an in-depth characterization of the Time Locked Index (TLI) as a novel tool aimed to efficiently quantify the harmonic content of noisy time series. It is shown that the proposed TLI measure is more robust and outperform traditional phase coherence metrics (e.g. Phase Locking Value, Pairwise Phase Consistency) in several aspects. ResultsWe found that a non linear oscillator under the effect of additive noise can produce spurious CFC with low spectral harmonic content. On the other hand, two coupled oscillatory dynamics with independent fundamental frequencies can produce true CFC with high spectral harmonic content via a rectification mechanism or other post-interaction nonlinear processing mechanisms. These results reveal a complex interplay between CFC and harmonicity emerging in the dynamics of biologically plausible neural network models and more generic non linear and parametric oscillators. ConclusionsWe show that, contrary to what is usually assumed in the literature, the high harmonic content observed in non sinusoidal oscillatory dynamics, is neither sufficient nor necessary condition to interpret the associated CFC patterns as epiphenomenal. There is mounting evidence suggesting that the combination of multimodal recordings, specialized signal processing techniques and theoretical modeling is becoming a required step to completely understand CFC patterns observed in oscillatory rich dynamics of physical and biophysical systems. HighlightsO_LITime locked index efficiently quantifies the harmonic content of noisy time series. C_LIO_LIA non linear oscillator under the effect of additive noise can produce spurious cross frequency couplings (CFC) with low spectral harmonic content. C_LIO_LITwo coupled oscillatory dynamics with independent fundamental frequencies can produce true CFC with high spectral harmonic content via rectification mechanisms or other post-interaction nonlinear processing mechanisms. C_LIO_LIA non sinusoidal oscillatory dynamics with high harmonic content is neither sufficient nor necessary condition for spurious CFC. C_LIO_LIA complex interplay between CFC and harmonicity emerges from the dynamics of nonlinear, parametric and biologically plausible oscillators. C_LI

neuroscience↗

bioRxiv2020

Spectral harmonicity distinguishes two types of ictal phase-amplitude cross frequency couplings in patients candidate to epilepsy surgery

ObjectiveSpectral harmonicity of the ictal activity was analyzed regarding two clinically relevant aspects, (1) as a confounding factor producing spurious phase-amplitude couplings (PAC) which may lead to wrong conclusions about the underlying ictal mechanisms, and (2) its role in how good PAC is in correspondence to the seizure onset zone (SOZ) classification performed by the epileptologists. MethodsPAC patterns observed in intracerebral electroencephalography (iEEG) recordings were retrospectively studied during seizures of seven patients with pharmacoresistant focal epilepsy. The time locked index (TLI) measure was introduced to quantify the degree of harmonicity between frequency bands associated to the emergence of PAC during epileptic seizures. Results(1) Harmonic and non harmonic PAC patterns coexist during the seizure dynamics in iEEG recordings with macroelectrodes. (2) Harmonic PAC patterns are an emergent property of the periodic non sinusoidal waveform constituting the epileptiform activity. (3) The TLI metric allows to distinguish the non harmonic PAC pattern, which has been previously associated with the ictal core through the paroxysmal depolarizing shifts mechanism of seizure propagation. ConclusionsOur results suggest that the spectral harmonicity of the ictal activity plays a relevant role in the visual analysis of the iEEG recordings performed by the epileptologists to define the SOZ, and that it should be considered for the proper interpretation of ictal mechanisms. SignificanceThe proposed harmonicity analysis can be used to improve the delineation of the SOZ by reliably identifying non harmonic PAC patterns emerging from fully recruited cortical and subcortical areas. HighlightsO_LIHarmonic and non harmonic phase-amplitude couplings coexist in epileptiform local field potentials. C_LIO_LIThe time locked index quantitatively differentiates harmonic and non harmonic ictal PAC patterns. C_LIO_LISpectral harmonicity plays a relevant role in the interpretation of underlying ictal mechanisms. C_LI

neuroscience↗

bioRxiv2018

Emergent orientation selectivity from random networks in mouse visual cortex

We study the connectivity principles underlying the emergence of orientation selectivity in primary visual cortex (V1) of mammals lacking an orientation map. We present a computational model in which random connectivity gives rise to orientation selectivity that matches experimental observations. It predicts that mouse V1 neurons should exhibit intricate receptive fields in the two-dimensional frequency domain, causing shift in orientation preferences with spatial frequency. We find evidence for these features in mouse V1 using calcium imaging and intracellular whole cell recordings.

neuroscience↗