J. Chevallier, A. Duarte, E. Löcherbach and G. Ost
We consider spatially extended systems of interacting nonlinear Hawkes processes modeling large systems of neurons placed in and study the associated mean field limits. As the total number of neurons tends to infinity, we prove that the evolution of a typical neuron, attached to a given spatial position, can be described by a nonlinear limit differential equation driven by a Poisson random measure. The limit process is described by a neural field equation. As a consequence, we provide a rigorous derivation of the neural field equation based on a thorough mean field analysis.
Ceballos C. C., Roque A. C. and Leão R. M.
Neuronal subthreshold voltage-dependent currents determine membrane properties such as the input resistance (Rin) and the membrane time constant (τm) in the subthreshold range. In contrast with classical cable theory predictions, the persistent sodium current (INaP), a non-inactivating mode of the voltage-dependent sodium current, paradoxically increases Rin and τm when activated. Furthermore, this current amplifies and prolongs synaptic currents in the subthreshold range. Here, using a computational neuronal model, we showed that the creation of a region of negative slope conductance by INaP activation is responsible for these effects and the ability of the negative slope conductance to amplify and prolong Rin and τm relies on the fast activation of INaP. Using dynamic clamp in hippocampal CA1 pyramidal neurons in brain slices, we showed that the effects of INaP on Rin and τm can be recovered by applying an artificial INaP after blocking endogenous INaP with tetrodotoxin. Furthermore, we showed that injection of a pure negative conductance is enough to reproduce the effects of INaP on Rin and τm and is also able to prolong artificial excitatory post synaptic currents. Since both the negative slope conductance and the almost instantaneous activation are critical for producing these effects, the INaP is an ideal current for boosting the amplitude and duration of excitatory post synaptic currents near the action potential threshold.
The whole paper is available here.
Natália B. Mota, Renata Callipo, Lígia Leite, Ana R. Torres, Janaína Weissheimer, Silvia A. Bunge, Mauro Copelli and Sidarta Ribeiro
Formal thought organization obtained from free speech, a key feature for psychiatric evaluations, has been poorly investigated during typical development. Computational tools such as speech graph connectedness (LSC) currently allow for an accurate quantification in naturalistic settings. LSC's typical development is better predicted by years of education than by age. Among beginning readers, the LSC of stories composed of short‐term memory predicted reading independently from IQ. Here we set out to test a longitudinal sample (6–8 years old, n = 45, followed for 2 years) to verify whether the LSC is predictive of various memory measures, and whether such relations can explain the correlation with reading. The LSC was specifically correlated with verbal short‐term memory performance. The results support the notion that the short‐term storage of verbal information is necessary to plan a story. Given the limited sample size, the relationship of this interaction with reading remains inconclusive.
The whole paper is available here.
Zacharias L. R., Peres A. S. C., Souza V. H., Conforto A. B. and Baffa O.
Background
Small variations in TMS parameters, such as pulse frequency and amplitude may elicit distinct neurophysiological responses. Assessing the mismatch between nominal and experimental parameters of TMS stimulators is essential for safe application and comparisons of results across studies.
Pierre Hodara and Ioannis Papageorgiou
We aim to prove Poincaré inequalities for a class of pure jump Markov processes inspired by the model introduced by Galves and Löcherbach to describe the behavior of interacting brain neurons. In particular, we consider neurons with degenerate jumps, i.e., which lose their memory when they spike, while the probability of a spike depends on the actual position and thus the past of the whole neural system. The process studied by Galves and Löcherbach is a point process counting the spike events of the system and is therefore non-Markovian. In this work, we consider a process describing the membrane potential of each neuron that contains the relevant information of the past. This allows us to work in a Markovian framework.
