From signals to music: a bottom-up approach to the structure of neuronal activity.

Introduction: The search for the "neural code" has been a fundamental quest in neuroscience, concerned with the way neurons and neuronal systems process and transmit information. However, the term "code" has been mostly used as a metaphor, seldom acknowledging the formal definitions introduced by information theory, and the contributions of linguistics and semiotics not at all. The heuristic potential of the latter was suggested by structuralism, which turned the methods and findings of linguistics to other fields of knowledge. For the study of complex communication systems, such as human language and music, the necessity of an approach that considers multilayered, nested, structured organization of symbols becomes evident. We work under the hypothesis that the neural code might be as complex as these human-made codes. To test this, we propose a bottom-up approach, constructing a symbolic logic in order to translate neuronal signals into music scores. Methods: We recorded single cells' activity from the rat's globus pallidus pars interna under conditions of full alertness, blindfoldedness and environmental silence. We analyzed the signals with statistical, spectral, and complex methods, including Fast Fourier Transform, Hurst exponent and recurrence plot analysis. Results: The results indicated complex behavior and recurrence graphs consistent with fractality, and a Hurst exponent >0.5, evidencing temporal persistence. On the whole, these features point toward a complex behavior of the time series analyzed, also present in classical music, which upholds the hypothesis of structural similarities between music and neuronal activity. Furthermore, through our experiment we performed a comparison between music and raw neuronal activity. Our results point to the same conclusion, showing the structures of music and neuronal activity to be homologous. The scores were not only spontaneously tonal, but they exhibited structure and features normally present in human-made musical creations. Discussion: The hypothesis of a structural homology between the neural code and the code of music holds, suggesting that some of the insights introduced by linguistic and semiotic theory might be a useful methodological resource to go beyond the limits set by metaphoric notions of "code."

Predictive processing: Is the future just a memory?

Predictive processing seems like a radical departure from traditional theories of information processing in the brain, but a broader view of predictions highlights many similarities with standard frameworks. Predictive processing is memory and competitive bias in a new outlook-and we should use this correspondence to advance research on both fronts.

Hippocampal place cells are topographically organized, but physical space has nothing to do with it.

Topographical organization can be found in many areas of the cerebral cortex, although its presence in higher order cortices is debated. Some studies evaluated whether this pattern of organization is present in the hippocampus, trying to determine whether hippocampal place cells are organized around a topographical map of space. Those studies indicated that the topographical organization of hippocampal place cells is either very limited or simply nonexistent. In this paper, we argue for a different interpretation of available evidence and suggest that there is a topographical organization in hippocampal place cells, but the topographical map formed is not a map of the physical space. Although place cell firing is correlated with the animal's position and is important to spatial navigation, place cells encode much more information than just location. Thus, we should not expect the topographical map to be organized around physical space, but around an abstract, multidimensional space containing the receptive fields of place cells. We show that this conclusion is supported by two of the main theories of hippocampal function-cognitive map theory and index theory-which, when carefully analyzed, make exactly the same predictions about hippocampal topography. Such abstract topographical map would be extremely hard to find using the methods commonly employed in the literature, but there are some approaches that may, in the future, make possible to characterize the topographical organization in the hippocampus and other high-order brain regions.

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.

Information-Theoretical Analysis of the Neural Code in the Rodent Temporal Lobe.

In the study of the neural code, information-theoretical methods have the advantage of making no assumptions about the probabilistic mapping between stimuli and responses. In the sensory domain, several methods have been developed to quantify the amount of information encoded in neural activity, without necessarily identifying the specific stimulus or response features that instantiate the code. As a proof of concept, here we extend those methods to the encoding of kinematic information in a navigating rodent. We estimate the information encoded in two well-characterized codes, mediated by the firing rate of neurons, and by the phase-of-firing with respect to the theta-filtered local field potential. In addition, we also consider a novel code, mediated by the delta-filtered local field potential. We find that all three codes transmit significant amounts of kinematic information, and informative neurons tend to employ a combination of codes. Cells tend to encode conjunctions of kinematic features, so that most of the informative neurons fall outside the traditional cell types employed to classify spatially-selective units. We conclude that a broad perspective on the candidate stimulus and response features expands the repertoire of strategies with which kinematic information is encoded.

Superlinear Summation of Information in Premotor Neuron Pairs.

Whether premotor/motor neurons encode information in terms of spiking frequency or by their relative time of firing, which may display synchronization, is still undetermined. To address this issue, we used an information theory approach to analyze neuronal responses recorded in the premotor (area F5) and primary motor (area F1) cortices of macaque monkeys under four different conditions of visual feedback during hand grasping. To evaluate the sensitivity of spike timing correlation between single neurons, we investigated the stimulus dependent synchronization in our population of pairs. We first investigated the degree of correlation of trial-to-trial fluctuations in response strength between neighboring neurons for each condition, and second estimated the stimulus dependent synchronization by means of an information theoretical approach. We compared the information conveyed by pairs of simultaneously recorded neurons with the sum of information provided by the respective individual cells. The information transmission across pairs of cells in the primary motor cortex seems largely independent, whereas information transmission across pairs of premotor neurons is summed superlinearly. The brain could take advantage of both the accuracy provided by the independency of F1 and the synergy allowed by the superlinear information population coding in F5, distinguishing thus the generalizing role of F5.

Time and category information in pattern-based codes.

Sensory stimuli are usually composed of different features (the what) appearing at irregular times (the when). Neural responses often use spike patterns to represent sensory information. The what is hypothesized to be encoded in the identity of the elicited patterns (the pattern categories), and the when, in the time positions of patterns (the pattern timing). However, this standard view is oversimplified. In the real world, the what and the when might not be separable concepts, for instance, if they are correlated in the stimulus. In addition, neuronal dynamics can condition the pattern timing to be correlated with the pattern categories. Hence, timing and categories of patterns may not constitute independent channels of information. In this paper, we assess the role of spike patterns in the neural code, irrespective of the nature of the patterns. We first define information-theoretical quantities that allow us to quantify the information encoded by different aspects of the neural response. We also introduce the notion of synergy/redundancy between time positions and categories of patterns. We subsequently establish the relation between the what and the when in the stimulus with the timing and the categories of patterns. To that aim, we quantify the mutual information between different aspects of the stimulus and different aspects of the response. This formal framework allows us to determine the precise conditions under which the standard view holds, as well as the departures from this simple case. Finally, we study the capability of different response aspects to represent the what and the when in the neural response.