Vascular Dynamics Aid a Coupled Neurovascular Network Learn Sparse Independent Features: A Computational Model

Cerebral vascular dynamics are generally thought to be controlled by neural activity in a unidirectional fashion. However, both computational modeling and experimental evidence point to the feedback effects of vascular dynamics on neural activity. Vascular feedback in the form of glucose and oxygen controls neuronal ATP, either directly or via the agency of astrocytes, which in turn modulates neural firing. Recently, a detailed model of the neuron-astrocyte-vessel system has shown how vasomotion can modulate neural firing. Similarly, arguing from known cerebrovascular physiology, an approach known as “hemoneural hypothesis” postulates functional modulation of neural activity by vascular feedback. To instantiate this perspective, we present a computational model in which a network of “vascular units” supplies energy to a neural network. The complex dynamics of the vascular network, modeled by a network of oscillators, turns neurons ON and OFF randomly. The informational consequence of such dynamics is explored in the context of an auto-encoder network. In the proposed model, each vascular unit supplies energy to a subset of hidden neurons of an autoencoder network, which constitutes its “projective field.” Neurons that receive adequate energy in a given trial have reduced threshold, and thus are prone to fire. Dynamics of the vascular network are governed by changes in the reconstruction error of the auto-encoder network, interpreted as the neuronal demand. Vascular feedback causes random inactivation of a subset of hidden neurons in every trial. We observe that, under conditions of desynchronized vascular dynamics, the output reconstruction error is low and the feature vectors learnt are sparse and independent. Our earlier modeling study highlighted the link between desynchronized vascular dynamics and efficient energy delivery in skeletal muscle. We now show that desynchronized vascular dynamics leads to efficient training in an auto-encoder neural network

Caracterización de gestos faciales y oculares mediante EEG utilizando SVM

Este documento tiene como fin describir el desarrollo implementado para la caracterización de gestos faciales y oculares mediante el uso del método de electroencefalograma, usando una diadema Emotiv EPOC+. Esta caracterización fue desarrollada a través de grabaciones de datos brutos (EEG) con distintos sujetos variantes en edad y sexo, analizando cada dato obtenido mediante procesos estadísticos y procesamiento de señales digitales, comprobando sus diferentes respuestas mediante una clasificación por máquinas de soporte vectorial, con el fin de evaluar si la diadema podría ser una opción de uso y aplicación en personas con discapacidades motoras en sus extremidades

A MULTIPLE REPRESENTATIONS MODEL OF THE HUMAN MIRROR NEURON SYSTEM FOR LEARNED ACTION IMITATION

The human mirror neuron system (MNS) is a fundamental sensorimotor system that plays a critical role in action observation and imitation. Despite a large body of experimental and theoretical MNS studies, the visuospatial transformation between the observed and the imitated actions has received very limited attention. Therefore, this work proposes a neurobiologically plausible MNS model, which examines the dynamics between the fronto-parietal mirror system and the parietal visuospatial transformation system during action observation and imitation. The fronto-parietal network is composed of the inferior frontal gyrus (IFG) and the inferior parietal lobule (IPL), which are postulated to generate the neural commands and the predictions for its sensorimotor consequences, respectively. The parietal regions identified as the superior parietal lobule (SPL) and the intraparietal sulcus (IPS) are postulated to encode the visuospatial transformation for enabling view-independent representations of the observed action. The middle temporal region is postulated to provide the view-dependent representations such as direction and velocity of the observed action. In this study, the SPL/IPS, IFG, and IPL are modeled with artificial neural networks to simulate the neural mechanisms underlying action imitation. The results reveal that this neural model can replicate relevant behavioral and neurophysiological findings obtained from previous action imitation studies. Specifically, the imitator can replicate the observed actions independently of the spatial relationships with the demonstrator while generating similar synthetic functional magnetic resonance imaging blood oxygenation level-dependent responses in the IFG for both action observation and execution. Moreover, the SPL/IPS can provide view-independent visual representations through mental transformation for which the response time monotonically increases as the rotation angle augments. Furthermore, the simulated neural activities reveal the emergence of both view-independent and view-dependent neural populations in the IFG. As a whole, this work suggests computational mechanisms by which visuospatial transformation processes would subserve the MNS for action observation and imitation independently of the differences in anthropometry, distance, and viewpoint between the demonstrator and the imitator

Functional disconnection and social cognition in schizophrenia

Introduction Social and emotional functions play a key role in schizophrenia. Both positive symptoms, such as hallucinations and persecutory delusions, as well as negative symptoms such as social withdrawal, and flattened affect impact socioemotional function. These functions involve distributed brain networks. The ‘Disconnection Hypothesis’, a plausible unifying theory of schizophrenia, proposes connectivity within such networks as a core pathological feature of schizophrenia. Connectivity is also related to specific genetic risk factors. Therefore the present project addresses the hypothesis that individuals with schizophrenia might show disconnection within socio-emotional brain networks, and examines the effects of a functional polymorphism of the BDNF gene on connectivity within these networks. Methods Here I examined the brain activation and connectivity for implicit emotional reaction and social judgment in schizophrenia, as well as with variation in the val66met polymorphism of BDNF. Brain activation was examined with functional magnetic resonance imaging, and effective connectivity was estimated using psycho-physiological interactions, from the bilateral amygdala to the whole brain (using a facial image paradigm for explicit approachability judgement and implicit fear response respectively). Results Individuals with schizophrenia showed reduced activation in the right lingual gyrus, right superior temporal gyrus and left amygdala during fear processing, as well as reduced connectivity from the left amygdala to the right temporo-parietal junction and precuneus. During approachability judgments, patients overactivated the right inferior frontal gyrus and right precuneus and showed reduced connectivity from the bilateral amygdala to the right inferior frontal gyrus. Met allele carriers of the BDNF val66met polymorphism showed overactivation in the medial anterior cingulate cortex, and bilateral insula, as well as reduced connectivity between the anterior cingulate cortex and hippocampus. For approachability judgment, met carriers overactivated the middle occipital gyrus, and showed reduced connectivity from the left amygdala to the right parahippocampal gyrus and medial frontal gyrus, as well as the left posterior cingulate gyrus, pre and post central gyrus, middle temporal gyrus and cerebellum. Conclusion In conclusion, connectivity between the amygdala and brain regions associated with a range of socially relevant functions were found to be reduced in both patients, and met allele carriers of the BDNF val66met SNP. Given the key role of the amygdala in affective processing this diffuse disconnection in networks for socio-emotional functions might mediate the aberrant emotional and social behavior seen in individuals with schizophrenia