Maturation of pyramidal cells in anterior piriform cortex may be sufficient to explain the end of early olfactory learning in rats

Studies have shown that neonate rodents exhibit high ability to learn a preference for novel odors associated with thermotactile stimuli that mimics maternal care. Artificial odors paired with vigorous strokes in rat pups younger than 10 postnatal days (P), but not older, rapidly induce an orientation-approximation behavior toward the conditioned odor in a two-choice preference test. The olfactory bulb (OB) and the anterior olfactory cortex (aPC), both modulated by norepinephrine (NE), have been identified as part of a neural circuit supporting this transitory olfactory learning. One possible explanation at the neuronal level for why the odor-stroke pairing induces consistent orientation-approximation behavior in P10, is the coincident activation of prior existent neurons in the aPC mediating this behavior. Specifically, odorstroke conditioning in P10 pups, promoting orientation-approximation behavior in the former but not in the latter. In order to test this hypothesis, we performed in vitro patch-clamp recordings of the aPC pyramidal neurons from rat pups from two age groups (P5–P8 and P14–P17) and built computational models for the OB-aPC neural circuit based on this physiological data. We conditioned the P5–P8 OB-aPC artificial circuit to an odor associated with NE activation (representing the process of maternal odor learning during mother–infant interactions inside the nest) and then evaluated the response of the OB-aPC circuit to the presentation of the conditioned odor. The results show that the number of responsive aPC neurons to the presentation of the conditioned odor in the P14–P17 OB-aPC circuit was lower than in the P5–P8 circuit, suggesting that at P14–P17, the reduced number of responsive neurons to the conditioned (maternal) odor might not be coincident with the responsive neurons for a second conditioned odor

The maturational characteristics of the GABA input in the anterior piriform cortex may also contribute to the rapid learning of the maternal odor during the sensitive period

During the first ten postnatal days (P), infant rodents can learn olfactory preferences for novel odors if they are paired with thermo-tactile stimuli that mimic components of maternal care. After P10, the thermo-tactile pairing becomes ineffective for conditioning. The current explanation for this change in associative learning is the alteration in the norepinephrine (NE) inputs from the locus coeruleus (LC) to the olfactory bulb (OB) and the anterior piriform cortex (aPC). By combining patchclamp electrophysiology and computational simulations, we showed in a recent work that a transitory high responsiveness of the OB-aPC circuit to the maternal odor is an alternative mechanism that could also explain early olfactory preference learning and its cessation after P10. That result relied solely on the maturational properties of the aPC pyramidal cells. However, the GABAergic system undergoes important changes during the same period. To address the importance of the maturation of the GABAergic system for early olfactory learning, we incorporated data from the GABA inputs, obtained from in vitro patch-clamp experiment in the aPC of rat pups aged P5–P7 reported here, to the model proposed in our previous publication. In the younger than P10 OB-aPC circuit with GABA synaptic input, the number of responsive aPC pyramidal cells to the conditioned maternal odor was amplified in 30% compared to the circuit without GABAergic input. When compared with the circuit with other younger than P10 OB-aPC circuit with adult GABAergic input profile, this amplification was 88%. Together, our results suggest that during the olfactory preference learning in younger than P10, the GABAergic synaptic input presumably acts by depolarizing the aPC pyramidal neurons in such a way that it leads to the amplification of the pyramidal neurons response to the conditioned maternal odor. Furthermore, our results suggest that during this developmental period, the aPC pyramidal cells themselves seem to resolve the apparent lack of GABAergic synaptic inhibition by a strong firing adaptation in response to increased depolarizing inputs

Effective recommendations towards healthy routines to preserve mental health during the COVID-19 pandemic

Objective: To assess the adherence to a set of evidence-based recommendations to support mental health during the coronavirus disease 2019 (COVID-19) pandemic and its association with depressive and anxiety symptoms. Methods: A team of health workers and researchers prepared the recommendations, formatted into three volumes (1: COVID-19 prevention; 2: Healthy habits; 3: Biological clock and sleep). Participants were randomized to receive only Volume 1 (control), Volumes 1 and 2, Volumes 1 and 3, or all volumes. We used a convenience sample of Portuguese-speaking participants over age 18 years. An online survey consisting of sociodemographic and behavioral questionnaires and mental health instruments (Patient Health Questionnaire-9 [PHQ-9] and Generalized Anxiety Disorder-7 [GAD-7]) was administered. At 14 and 28 days later, participants were invited to complete follow-up surveys, which also included questions regarding adherence to the recommendations. A total of 409 participants completed the study – mostly young adult women holding university degrees. Results: The set of recommendations contained in Volumes 2 and 3 was effective in protecting mental health, as suggested by significant associations of adherence with PHQ-9 and GAD-7 scores (reflecting anxiety and depression symptoms, respectively). Conclusion: The recommendations developed in this study could be useful to prevent negative mental health effects in the context of the pandemic and beyond

Redes de neurônios com interações sinápticas hierárquicas

Na primeira etapa investigamos detalhadamente as propriedades de armazenamento de um modelo de redes de neurônios que apresenta uma organização em aglomerados semelhante àquela existente no Modelo Hierárquico de Dyson para ferromagnetismo. As memórias, neste modelo, são armazenadas através da Regra de Aprendizagem de Hebb, superposta à estrutura hieráquica. No caso de um número finito de padrões, mostramos que, junto com os padrões originais ou "ancestrais", o sistema é capaz de recuperar uma hierarquia de padrões "descendentes". Estes padrões diferem dos "ancestrais" nos sinais relativos das magnetizações nos diferentes blocos, e o número destas soluções cresce exponencialmente com o número de blocos, n(t) e0451, para grande. Para um número extensivo de padrões armazenados p = aN , onde N é o tamanho do sistema, nós investigamos a capacidade crítica de armazenamento do modelo tanto para "ancestrais" como para "descendentes". Usando dois métodos distintos, uma formulação de mecânica estatística de equilíbrio (campo médio) e uma análise de sinal-ruído, nós obtemos uma sucessão de capacidades de armazenamento que são sempre menores que o valor correspondente ao modelo de Hopfield. Usamos as razões entre estas capacidades e a do modelo de Hopfield para comparar os resultados dos dois métodos. Nós apresentamos o diagrama de fases no plano a — T para o caso especial de dois blocos e um único "descendente". A segunda parte é relacionada com o estudo do espaço de interações dos modelos de redes de neurônios. Neste caso nós buscamos determinar a máxima capacidade crítica de armazenamento para modelos que apresentem, de alguma forma, a mesma estrutura de blocos que discutimos antes. Para ter em conta esta estrutura foi preciso modificar o problema de Gardner. Para o armazenamento de padrões "ancestrais", obtivemos um valor de anc i". sempre menor que 2, o qual corresponde ao caso de modelos sem estrutura predefinida. Este resultado combina com aquele que encontramos anteriormente na análise de sinal-ruído. Por outro lado, mostramos que no caso especial de dois blocos esta estrutura pouco afeta a armazenagem conjunta de "ancestrais" e "descendentes".In the first part we perform a detailed investigation of the storage properties of a model for neural networks that exhibits the same organization into clusters as Dyson's hierarchical model, for ferromagnetism, combined with Hebb's learning algorithm for p stored patterns. In the case of finite p, we show that together with the original stored patterns or "ancestors" the system retrieves also a hierarchy of "descendants". The "descendants" differ from the "ancestor" in the signs of the cluster overlaps, and the number of this solutions increases exponentially with the cluster number, n(t) ti e"", for large values of t. For an extensive number of stored patterns p = aN , where N is the size of the network, we investigate the criticai storage capacity of the model to both "ancestor" and "descendant" patterns. By using two different methods, a statistical mechanics formulation (mean-field) and a signal-to-noise analysis, we obtain a succession of criticai storage capacities that are below the corresponding value for Hopfield's model. We use the ratio of this criticai storage capacities to the same quantity as evaluated in Hopfield's model to compare the results in both methods. We present the phase diagram in the a — T plane for the particular case of two clusters and one descendant. The second part is related with the study of the space of interactions in neural network models. In this case we search for the maximal criticai storage capacity of models that present, in some sense, the same cluster struture that we discussed before. In order to take this structure in account we redefine the Gardner Program. Concerning the storage of "ancestors" we obtain a value of arx. always lower than 2, which corresponds to the original case of models without structure. This result agrees with that we found before in the signal-to-noise analysis. On the other side we show in the special case of two clusters that this structure barely affects the storage of "ancestors" and "descendants" together

Redes de neurônios com interações sinápticas hierárquicas

Na primeira etapa investigamos detalhadamente as propriedades de armazenamento de um modelo de redes de neurônios que apresenta uma organização em aglomerados semelhante àquela existente no Modelo Hierárquico de Dyson para ferromagnetismo. As memórias, neste modelo, são armazenadas através da Regra de Aprendizagem de Hebb, superposta à estrutura hieráquica. No caso de um número finito de padrões, mostramos que, junto com os padrões originais ou "ancestrais", o sistema é capaz de recuperar uma hierarquia de padrões "descendentes". Estes padrões diferem dos "ancestrais" nos sinais relativos das magnetizações nos diferentes blocos, e o número destas soluções cresce exponencialmente com o número de blocos, n(t) e0451, para grande. Para um número extensivo de padrões armazenados p = aN , onde N é o tamanho do sistema, nós investigamos a capacidade crítica de armazenamento do modelo tanto para "ancestrais" como para "descendentes". Usando dois métodos distintos, uma formulação de mecânica estatística de equilíbrio (campo médio) e uma análise de sinal-ruído, nós obtemos uma sucessão de capacidades de armazenamento que são sempre menores que o valor correspondente ao modelo de Hopfield. Usamos as razões entre estas capacidades e a do modelo de Hopfield para comparar os resultados dos dois métodos. Nós apresentamos o diagrama de fases no plano a — T para o caso especial de dois blocos e um único "descendente". A segunda parte é relacionada com o estudo do espaço de interações dos modelos de redes de neurônios. Neste caso nós buscamos determinar a máxima capacidade crítica de armazenamento para modelos que apresentem, de alguma forma, a mesma estrutura de blocos que discutimos antes. Para ter em conta esta estrutura foi preciso modificar o problema de Gardner. Para o armazenamento de padrões "ancestrais", obtivemos um valor de anc i". sempre menor que 2, o qual corresponde ao caso de modelos sem estrutura predefinida. Este resultado combina com aquele que encontramos anteriormente na análise de sinal-ruído. Por outro lado, mostramos que no caso especial de dois blocos esta estrutura pouco afeta a armazenagem conjunta de "ancestrais" e "descendentes".In the first part we perform a detailed investigation of the storage properties of a model for neural networks that exhibits the same organization into clusters as Dyson's hierarchical model, for ferromagnetism, combined with Hebb's learning algorithm for p stored patterns. In the case of finite p, we show that together with the original stored patterns or "ancestors" the system retrieves also a hierarchy of "descendants". The "descendants" differ from the "ancestor" in the signs of the cluster overlaps, and the number of this solutions increases exponentially with the cluster number, n(t) ti e"", for large values of t. For an extensive number of stored patterns p = aN , where N is the size of the network, we investigate the criticai storage capacity of the model to both "ancestor" and "descendant" patterns. By using two different methods, a statistical mechanics formulation (mean-field) and a signal-to-noise analysis, we obtain a succession of criticai storage capacities that are below the corresponding value for Hopfield's model. We use the ratio of this criticai storage capacities to the same quantity as evaluated in Hopfield's model to compare the results in both methods. We present the phase diagram in the a — T plane for the particular case of two clusters and one descendant. The second part is related with the study of the space of interactions in neural network models. In this case we search for the maximal criticai storage capacity of models that present, in some sense, the same cluster struture that we discussed before. In order to take this structure in account we redefine the Gardner Program. Concerning the storage of "ancestors" we obtain a value of arx. always lower than 2, which corresponds to the original case of models without structure. This result agrees with that we found before in the signal-to-noise analysis. On the other side we show in the special case of two clusters that this structure barely affects the storage of "ancestors" and "descendants" together

Campo magnético crítico superior de um supercondutor impuro

O campo magnético crítico superior e a temperatura crítica são calculadas para supercondutores com impurezas. Na avaliação das funções de Green do problema são usadas as autofunções exatas de um elétron em um campo magnético constante, ao invés da aproximação semi-clássica comumente utilizada.The upper critical field and the Critical Temperature of a dirty superconductor are calculated. To evaluate the Green Functions we avoid the semi-classical aproximation by using the exact eletrons eigenstates in a magnetic field

Navegação de robôs usando redes neurais

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Estudo da dinâmica de ordenamento celular

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