Biology subjects

Cataldo, E.

Publications and source records attributed to Cataldo, E..

4 recordsLinked to original sources

bioRxiv2025

Gamma oscillations in basal ganglia stem from the interplay between local inhibition and beta synchronization

Basal ganglia rhythms have mainly been studied in the beta band (12-30 Hz), a hallmark of Parkinsons disease (PD), while gamma oscillations (30-100 Hz) in the subthalamic nucleus (STN) have emerged as alternative markers for guiding adaptive deep brain stimulation. However, their underlying mechanisms remains unclear. Using a spiking network model of the basal ganglia, we identified two distinct gamma rhythms: a high-frequency gamma in pallidal (GPe-TI) neurons and a slower gamma in D2 medium spiny neurons (MSNs), both generated through self-inhibition. Under simulated parkinsonian condition, GPe-TI gamma intensity remained stable. In contrast, D2 MSN gamma emerged only in pathological conditions and was strongly modulated by beta activity in both intensity and frequency. Although STN did not generate gamma oscillations directly, gamma activity from GPe-TI population was reflected in simulated STN local field potentials. These results clarify the circuit origins of gamma rhythms and their modulation in PD.

neuroscience↗

bioRxiv2025

A spiking LIF model captures the role of Somatostatin and Parvalbumin neurons in generating oscillations in V1

PurposeOscillations in the primary visual cortex of the mammalian brain have been demonstrated to arise from the balance between excitatory and inhibitory activity. Experimental studies suggest that different inhibitory neuron populations might make specific contributions to such oscillations, but the underlying mechanism has not yet been assessed. MethodsWe modified a standard excitatory-inhibitory spiking neuron model of layer 4 of the primary visual cortex and we investigated the effects on oscillations of the differentiation of inhibitory neurons in somatostatin and parvalbumin neurons. ResultsOur model reproduced the hypothesis that somatostatin and parvalbumin neurons are responsible for beta (15-25)Hz and gamma (40-70)Hz band oscillations, respectively. ConclusionTo date, this is the simplest model accounting for this phenomenon and could therefore be suited to study pathologies in which the two populations have specific roles, such as migraine.

neuroscience↗

bioRxiv2021

Euler method can outperform more complex ODE solvers in the numerical implementation of the Izhikevich artificial Spiking Neuron Model given the allocated FLOPS

The Izhikevich artificial spiking neuron model is among the most employed models in neuromorphic engineering and computational neuroscience, due to the affordable computational effort to discretize it and its biological plausibility. It has been adopted also for applications with limited computational resources in embedded systems. It is important therefore to realize a compromise between error and computational expense to solve numerically the models equations. Here we investigate the effects of discretization and we study the solver that realizes the best compromise between accuracy and computational cost, given an available amount of Floating Point Operations per Second (FLOPS). We considered three fixed-step solvers for Ordinary Differential Equations (ODE), commonly used in computational neuroscience: Euler method, the Runge-Kutta 2 method and the Runge-Kutta 4 method. To quantify the error produced by the solvers, we used the Victor Purpura spike train Distance from an ideal solution of the ODE. Counterintuitively, we found that simple methods such as Euler and Runge Kutta 2 can outperform more complex ones (i.e. Runge Kutta 4) in the numerical solution of the Izhikevich model if the same FLOPS are allocated in the comparison. Moreover, we quantified the neuron rest time (with input under threshold resulting in no output spikes) necessary for the numerical solution to converge to the ideal solution and therefore to cancel the error accumulated during the spike train; in this analysis we found that the required rest time is independent from the firing rate and the spike train duration. Our results can generalize in a straightforward manner to other spiking neuron models and provide a systematic analysis of fixed step neural ODE solvers towards an accuracy-computational cost tradeoff.

bioengineering↗

bioRxiv2020

Thalamocortical spectral transmission relies on balanced input strengths

The thalamus is a key element of sensory transmission in the brain, as all sensory information is processed by the thalamus before reaching the cortex. The thalamus is known to gate and select sensory streams through a modulation of its internal activity in which spindle oscillations play a preponderant role, but the mechanism underlying this process is not completely understood. In particular, how do thalamocortical connections convey stimulus-driven information selectively over the background of thalamic internally generated activity (such as spindle oscillations)? Here we investigate this issue with a spiking network model of connectivity between thalamus and primary sensory cortex reproducing the local field potential of both areas. We found two features of the thalamocortical dynamics that filter out spindle oscillations: i) spindle oscillations are weaker in neurons projecting to the cortex, ii) the resonance dynamics of cortical networks selectively blocks frequency in the range encompassing spindle oscillations. This latter mechanism depends on the balance of the strength of thalamocortical connections toward excitatory and inhibitory neurons in the cortex. Our results pave the way toward an integrated understanding of the sensory streams traveling between the periphery and the cortex.

neuroscience↗