Ricerc@Sapienza

Limb apraxia (LA) is a high-order motor disorder linked to left-hemisphere damage. It is characterized by defective execution of purposeful actions upon delayed imitation, or verbal command when the actions are performed in isolated, non-naturalistic, conditions. Whether interpersonal interactions provide social affordances that activate neural resources different from those requested by individual action execution, which may improve LA performance, is unknown.

During decisions, animals balance goal achievement and effort management. Despite physical exercise and fatigue significantly affecting the levels of effort that an animal exerts to obtain a reward, their role in effort-based choice and the underlying neurochemistry are incompletely known. In particular, it is unclear whether fatigue influences decision (cost-benefit) strategies flexibly or only post-decision action execution and learning.

In this article, we explore the neural correlates of the general and specific factors assessed by the Life Orientation Test-Revised. These factors have been shown to assess general optimism (GO) and a form of self-enhancement akin to unrealistic optimism (SP). Toward our aim, we used a standardized low-resolution brain electromagnetic tomography (sLORETA), which provides electroencephalographic (EEG) localization measures that are independent of recording reference. Resting-EEG and self-report measures of GO and SP were collected from 51 female undergraduates.

Our brain continuously receives information over multiple timescales that are differently processed across areas. In this study, we investigated the intrinsic timescale of neurons in the dorsal premotor cortex (PMd) of two rhesus macaques while performing a non-match-to-goal task. The task rule was to reject the previously chosen target and select the alternative one. We defined the intrinsic timescale as the decay constant of the autocorrelation structure computed during a baseline period of the task.

Based on their pharmacological properties, psychoactive drugs are supposed to take control of the natural reward system to finally drive compulsory drug seeking and consumption. However, psychoactive drugs are not used in an arbitrary way as pure pharmacological reinforcement would suggest, but rather in a highly specific manner depending on non-pharmacological factors. While pharmacological effects of psychoactive drugs are well studied, neurobiological mechanisms of non-pharmacological factors are less well understood.

We have previously shown that in seven drug-resistant epilepsy patients, both reachinggrasping
of objects and the mere observation of those actions did desynchronize
subdural electrocorticographic (ECoG) alpha (8–13 Hz) and beta (14–30) rhythms as
a sign of cortical activation in primary somatosensory-motor, lateral premotor and
ventral prefrontal areas (Babiloni et al., 2016a). Furthermore, that desynchronization was
greater during action execution than during its observation. In the present exploratory

Simultaneous resting state functional magnetic resonance imaging (rsfMRI)-resting state electroencephalography (rsEEG) studies in healthy adults showed robust positive associations of signal power in the alpha band with BOLD signal in the thalamus, and more heterogeneous associations in cortical default mode network (DMN) regions. Negative associations were found in occipital regions. In Alzheimer's disease (AD), rsfMRI studies revealed a disruption of the DMN, while rsEEG studies consistently reported a reduced power within the alpha band.

Echo state networks (ESNs), belonging to the wider family of reservoir computing methods, are a powerful tool for the analysis of dynamic data. In an ESN, the input signal is fed to a fixed (possibly large) pool of interconnected neurons, whose state is then read by an adaptable layer to provide the output. This last layer is generally trained via a regularized linear least-squares procedure.

In this paper, we address the challenging task of simultaneously optimizing (i) the weights of a neural network, (ii) the number of neurons for each hidden layer, and (iii) the subset of active input features (i.e., feature selection). While these problems are traditionally dealt with separately, we propose an efficient regularized formulation enabling their simultaneous parallel execution, using standard optimization routines.

The aim of this paper is to develop a general framework for training neural networks (NNs) in a distributed environment, where