Comprendiendo el código neuronal: un análisis de cuatro perspectivas / Understanding the neural code: a four-perspective analysis

El objetivo de este artículo de revisión es dar a conocer diferentes perspectivas que han contribuido al estudio del Código Neuronal, un concepto que proviene de la Neurociencia y que explica el funcionamiento del cerebro a través de conexiones de neuronas. Se entregan cuatro ideas relacionadas con el análisis de este funcionamiento. En primer lugar, la propuesta de Convergencia Jerárquica, que ofrece una explicación asociada a un correlato neuronal específico para una conducta determinada. En segundo lugar, se aborda la idea del Código de Poblaciones, que explica el trabajo de un grupo de neuronas que representan un determinado estado. Posteriormente se expone la propuesta de Correlación Temporal, que plantea la presencia de poblaciones neuronales activas que se diferencian entre sí en base a patrones temporales de descarga para, finalmente, llegar al concepto de redes neuronales y sus diferentes modelos explicativos que han actuado como cimientos para el desarrollo de la Neurociencia moderna y que han sido desarrollados gracias a los aportes de la Biología, la Física, las Matemáticas, entre otras disciplinas, y que han generado las bases para la comprensión del funcionamiento del cerebro a través de neuronas interconectadas para lograr la expresión de los diferentes procesos cognitivos. El presente artículo pretende que el lector desarrolle una visión panorámica y general de cómo opera el flujo de la información que procesa el sistema nervioso central y el impacto que este fenómeno genera en el proceso de integración sensorial como parte de la emoción y la cognición en el cerebro humano.

The objective of this review article is to present different perspectives that have contributed to the study of the Neural Code, a concept that comes from Neuroscience and that explains the functioning of the brain through neuron connections. Four ideas related to the analysis of this functioning are presented. Firstly, the proposal of Hierarchical Convergence, which offers an explanation associated with a specific neuronal correlate for a specific behavior. Secondly, the idea of the Population Code is discussed, which explains the work of a group of neurons that represent a certain state. Subsequently, the proposal of Temporal Correlation is addressed, which proposes the presence of active neuronal populations that differentiate each other based on temporal discharge patterns, finally arriving at the concept of neural networks and their different explanatory models. The latter have acted as foundations for the development of modern Neuroscience and have been developed thanks to the contributions of Biology, Physics, Mathematics, among other disciplines, and have generated the basis for understanding the functioning of the brain through interconnected neurons to achieve the expression of the different cognitive processes. The paper aims to develop a panoramic and general view of how the flow of information processed by the central nervous system operates and the impact that this phenomenon generates in the process of sensory integration as part of emotion and cognition in the human brain.

Research of Functional Connectivity Based on Independent Component Analysis and Temporal Correlation Analysis / 中国医学物理学杂志

Objective: Combining spatial independent component analysis (sICA) with temporal correlation analysis to investigate the functional connectivity of human brain using resting state fMRI. Methods: First, activated area was localized by performing sICA on the data from block design run, then one of the activated brain areas was chosen as a region of interest (ROI)and low frequency correlations between ROI and other regions were calculated in resting state to detect the functional connectivity networks. To validate the method, neural connectivity to primary motor cortex was assessed using this method during a resting state. Results: Functional connectivity network of motor cortex was detected, including primary motor cortex (M1), supplementary motor area (SMA), primary sensory cortex (S1), dorsal premotor cortex (PMd) and posterior parietal somatosensory association area (PSAAp). The connectivity implied by the resting state correlation was far more similar to the connectivity established by non-imaging methods. Conclusion: Functional connectivity of human motor primary cortex was investigated by combining sICA with temporal correlation using resting fMRI data. It provided a simple and noninvasive method for the research of brain functional connectivity.