Integrated Information Coefficient Estimated from Neuronal Activity in Hippocampus-Amygdala Complex of Rats as a Measure of Learning Success.

BACKGROUND: The goal of the brain is to provide right on time a suitable earlier-acquired model for the future behavior. How a complex structure of neuronal activity underlying a suitable model is selected or fixated is not well understood. Here we propose the integrated information Φ as a possible metric for such complexity of neuronal groups. It quantifies the degree of information integration between different parts of the brain and is lowered when there is a lack of connectivity between different subsets in a system. METHODS: We calculated integrated information coefficient (Φ) for activity of hippocampal and amygdala neurons in rats during acquisition of two tasks: spatial task followed by spatial aversive task. An Autoregressive Φ algorithm was used for time-series spike data. RESULTS: We showed that integrated information coefficient Φ is positively correlated with a metric of learning success (a relative number of rewards). Φ for hippocampal neurons was positively correlated with Φ for amygdalar neurons during the learning requiring the cooperative work of hippocampus and amygdala. CONCLUSIONS: This result suggests that integrated information coefficient Φ may be used as a prediction tool for the suitable level of complexity of neuronal activity and the future success in learning and adaptation and a tool for estimation of interactions between different brain regions during learning.

Different Approximation Methods for Calculation of Integrated Information Coefficient in the Brain during Instrumental Learning.

The amount of integrated information, Φ, proposed in an integrated information theory (IIT) is useful to describe the degree of brain adaptation to the environment. However, its computation cannot be precisely performed for a reasonable time for time-series spike data collected from a large count of neurons.. Therefore, Φ was only used to describe averaged activity of a big group of neurons, and the behavior of small non-brain systems. In this study, we reported on ways for fast and precise Φ calculation using different approximation methods for Φ calculation in neural spike data, and checked the capability of Φ to describe a degree of adaptation in brain neural networks. We show that during instrumental learning sessions, all applied approximation methods reflect temporal trends of Φ in the rat hippocampus. The value of Φ is positively correlated with the number of successful acts performed by a rat. We also show that only one subgroup of neurons modulates their Φ during learning. The obtained results pave the way for application of Φ to investigate plasticity in the brain during the acquisition of new tasks.

Regression II. Development through regression.

As shown in our previous paper ('Regression I. Experimental approaches to regression', JAP, 65, 2, 345-65), the common mechanism of regression can be described as reversible dedifferentiation, which is understood as a relative increase of the proportion of low-differentiated (older) systems in actualized experience. Experimental data show that regression following disease (chronic tension headache) is followed by adaptation and an increase in system differentiation in that experience domain which contains systems responsible for that adaptation. The results of mathematical modelling support the idea that reversible dedifferentiation can be one of the mechanisms for increasing the effectiveness of adaptation through learning. Reversible dedifferentiation, which is phenomenologically described as regression, is a general mechanism for restructuring the organism-environment interactions in situations where behaviours that were effective in the past become ineffective. Reversible dedifferentiation has evolved as a component of adaptation when new behaviours are formed and large-scale modifications in the existing behaviours are required in the face of changes in the external and/or internal environment. Thus, the authors believe that this article provides evidence for Jung's view that regression is not only a 'return' to past forms of thinking, affects and behaviour, but that regressive processes provide a significant impetus for psychological growth and development.

Comme nous l'avons montré dans notre article précédent (« Régression I. Les approches expérimentales de la régression ¼), le mécanisme propre à la régression peut être décrit en tant que dé-différentiation réversible, ce que l'on peut comprendre comme une hausse relative de la proportion de systèmes peu différentiés (plus vieux) dans l'expérience actualisée. Les données expérimentales montrent que la régression suite à une maladie (mal de tête de tension chronique) est suivie par une adaptation et un accroissement dans la différentiation des systèmes dans le domaine d'expérience qui contient les systèmes responsables de cette adaptation. Les résultats de la modélisation mathématique soutiennent l'idée que la dé-différentiation réversible peut être l'un des mécanismes pour accroitre l'efficacité de l'adaptation par l'apprentissage. La dé-différentiation réversible, qui est décrite phénoménologiquement comme régression, est un mécanisme général pour restructurer les interactions organisme-environnement dans des situations où les comportements qui fonctionnaient par le passé sont devenus inefficaces. La dé-différentiation réversible a évolué comme un élément de l'adaptation quand de nouveaux comportements se développent et que des changements dans l'environnement extérieur ou intérieur requièrent des modifications à grande échelle dans les comportements existants. Ainsi, les auteurs pensent que cet article apporte un soutien à la perspective de Jung selon laquelle la régression n'est pas seulement un « retour ¼ à des formes anciennes de fonctionnement, d'affects et de comportement, mais que les processus régressifs fournissent un élan significatif pour la croissance et le développement psychologiques.

Como hemos mostrado en nuestros trabajos previos ('Regresión I. Abordajes experimentales hacia la regresión'), el mecanismo común de la regresión puede ser descripto como desdiferenciación reversible, el cual es comprendido como un relativo incremento en la proporción de sistemas de baja-diferenciación en la experiencia actual. Data experimental muestra que la regresión luego de una enfermedad (tensión de cabeza crónica) es seguida por la adaptación y por un incremento en la diferenciación de sistemas en aquel dominio de la experiencia, que contiene sistemas responsables para tal adaptación. Los resultados del modelo matemático sostienen la idea de que la desdiferenciación reversible puede ser uno de los mecanismos para incrementar la efectividad de la adaptación a través del aprendizaje. La desdiferenciación reversible, la cual fenomenológicamente se describe como regresión, es un mecanismo general para restructurar las interacciones entre el organismo y el medio ambiente, en situaciones en las que las conductas que eran efectivas en el pasado se vuelven ineficaces. La desdiferenciación reversible ha evolucionado como un componente de la adaptación cuando se forman nuevas conductas y se requieren modificaciones a gran escala en las conductas existentes frente a los cambios en el medio ambiente externo y/o interno. Así, los autores consideran que el artículo proporciona evidencia a la perspectiva de Jung sobre la regresión, no solamente como un 'retorno' a formas de pensar, sentir y actuar del pasado sino que los procesos regresivos proveen un estímulo significativo para el desarrollo y el crecimiento psicológico.

Regression I. Experimental approaches to regression.

The concept of regression is considered with an emphasis on the differences between the positions of Freud and Jung regarding its significance. The paper discusses the results of experimental analyses of individual experience dynamics (from gene expression changes and impulse neuronal activity in animals to prosocial behaviour in healthy humans at different ages, and humans in chronic pain) in those situations where regression occurs: stress, disease, learning, highly emotional states and alcohol intoxication. Common mechanisms of regression in all these situations are proposed. The mechanisms of regression can be described as reversible dedifferentiation, which is understood as a relative increase of the representation of low-differentiated (older) systems in the actualized experience. In all of the cases of dedifferentiation mentioned above, the complexity of the systemic organization of behaviour significantly decreases.

Le concept de régression est étudié en mettant l'accent sur les différences entre les positions de Freud et celles de Jung concernant sa portée. L'article discute les résultats des analyses expérimentales de dynamiques de l'expérience individuelle (de changements dans l'expression des gènes et de l'activité des réflexes neuronaux chez les animaux aux comportements pro-sociaux chez des humains en bonne santé et à des âges de vie divers, et chez des humains en situation de souffrance chronique) dans ces situations où se produit la régression: le stress, la maladie, l'apprentissage, les états hautement émotionnels et l'intoxication par l'alcool. Les mécanismes communs de régression dans toutes ces situations sont présentés. Les mécanismes de régression peuvent être décrits en tant que dé-différentiation réversible, ce qui est interprété comme un accroissement relatif de la représentation de systèmes peu-différenciés (plus vieux) dans l'expérience actualisée. Dans tous les cas de dé-différentiation mentionnés plus haut, la complexité de l'organisation systémique du comportement décroit de manière significative.

Se considera el concepto de regresión, con énfasis en las diferencias entre Freud y Jung en lo que concierne a su significado. El trabajo desarrolla los resultados de análisis experimentales sobre dinámicas experienciales individuales (desde cambios en la expresión genética y actividad neuronal en animales a conductas prosociales en humanos saludables en diferentes edades, y humanos en dolor crónico) en aquellas situaciones donde la regresión ocurre: estrés, enfermedad, aprendizaje, estados altamente emocionales e intoxicación alcohólica. Se proponen mecanismos comunes de regresión en todas estas situaciones. Los mecanismos de regresión pueden describirse como de-diferenciación reversible, la cual es comprendida como un incremento relativo de la representación de sistemas de baja-diferenciación (antiguos) en la experiencia actualizada. En todos los casos de de-diferenciación mencionados anteriormente, la complejidad de la organización sistemática de conducta decrece significativamente.

Neuronal Bases of Systemic Organization of Behavior.

Despite the years of studies in the field of systems neuroscience, functions of neural circuits and behavior-related systems are still not entirely clear. The systems description of brain activity has recently been associated with cognitive concepts, e.g. a cognitive map, reconstructed via place-cell activity analysis and the like, and a cognitive schema, modeled in consolidation research. The issue we find of importance is that a cognitive unit reconstructed in neuroscience research is mainly formulated in terms of environment. In other words, the individual experience is considered as a model or reflection of the outside world and usually lacks a biological meaning, such as describing a given part of the world for the individual. In this chapter, we present the idea of a cognitive component that serves as a model of behavioral interaction with environment, rather than a model of the environment itself. This intangible difference entails the need in substantial revision of several well-known phenomena, including the long-term potentiation.The principal questions developed here are how the cognitive units appear and change upon learning and performance, and how the links between them create the whole structure of individual experience. We argue that a clear distinction between processes that provide the emergence of new components and those underlying the retrieval and/or changes in the existing ones is necessary in learning and memory research. We then describe a view on learning and corresponding neuronal activity analysis that may help set this distinction.

Expression of c-Fos in the rat retrosplenial cortex during instrumental re-learning of appetitive bar-pressing depends on the number of stages of previous training.

Learning is known to be accompanied by induction of c-Fos expression in cortical neurons. However, not all neurons are involved in this process. What the c-Fos expression pattern depends on is still unknown. In the present work we studied whether and to what degree previous animal experience about Task 1 (the first phase of an instrumental learning) influenced neuronal c-Fos expression in the retrosplenial cortex during acquisition of Task 2 (the second phase of an instrumental learning). Animals were progressively shaped across days to bar-press for food at the left side of the experimental chamber (Task 1). This appetitive bar-pressing behavior was shaped by nine stages ("9 stages" group), five stages ("5 stages" group) or one intermediate stage ("1 stage" group). After all animals acquired the first skill and practiced it for five days, the bar and feeder on the left, familiar side of the chamber were inactivated, and the animals were allowed to learn a similar instrumental task at the opposite side of the chamber using another pair of a bar and a feeder (Task 2). The highest number of c-Fos positive neurons was found in the retrosplenial cortex of "1 stage" animals as compared to the other groups. The number of c-Fos positive neurons in "5 stages" group animals was significantly lower than in "1 stage" animals and significantly higher than in "9 stages" animals. The number of c-Fos positive neurons in the cortex of "9 stages" animals was significantly higher than in home caged control animals. At the same time, there were no significant differences between groups in such behavioral variables as the number of entrees into the feeder or bar zones during Task 2 learning. Our results suggest that c-Fos expression in the retrosplenial cortex during Task 2 acquisition was influenced by the previous learning history.