RESUMO
The sensorimotor gating is a nervous system function that modulates the acoustic startle response (ASR). Prepulse inhibition (PPI) phenomenon is an operational measure of sensorimotor gating, defined as the reduction of ASR when a high intensity sound (pulse) is preceded in milliseconds by a weaker stimulus (prepulse). Brainstem nuclei are associated with the mediation of ASR and PPI, whereas cortical and subcortical regions are associated with their modulation. However, it is still unclear how the modulatory units can influence PPI. In the present work, we developed a computational model of a neural circuit involved in the mediation (brainstem units) and modulation (cortical and subcortical units) of ASR and PPI. The activities of all units were modeled by the leaky-integrator formalism for neural population. The model reproduces basic features of PPI observed in experiments, such as the effects of changes in interstimulus interval, prepulse intensity, and habituation of ASR. The simulation of GABAergic and dopaminergic drugs impaired PPI by their effects over subcortical units activity. The results show that subcortical units constitute a central hub for PPI modulation. The presented computational model offers a valuable tool to investigate the neurobiology associated with disorder-related impairments in PPI.
RESUMO
Training neural networks to perform different tasks is relevant across various disciplines. In particular, Recurrent Neural Networks (RNNs) are of great interest in Computational Neuroscience. Open-source frameworks dedicated to Machine Learning, such as Tensorflow and Keras have produced significant changes in the development of technologies that we currently use. This work contributes by comprehensively investigating and describing the application of RNNs for temporal processing through a study of a 3-bit Flip Flop memory implementation. We delve into the entire modeling process, encompassing equations, task parametrization, and software development. The obtained networks are meticulously analyzed to elucidate dynamics, aided by an array of visualization and analysis tools. Moreover, the provided code is versatile enough to facilitate the modeling of diverse tasks and systems. Furthermore, we present how memory states can be efficiently stored in the vertices of a cube in the dimensionally reduced space, supplementing previous results with a distinct approach.
RESUMO
The functioning of the brain has been a complex and enigmatic phenomenon. From the first approaches made by Descartes about this organism as the vehicle of the mind to contemporary studies that consider the brain as an organism with emergent activities of primary and higher order, this organism has been the object of continuous exploration. It has been possible to develop a more profound study of brain functions through imaging techniques, the implementation of digital platforms or simulators through different programming languages and the use of multiple processors to emulate the speed at which synaptic processes are executed in the brain. The use of various computational architectures raises innumerable questions about the possible scope of disciplines such as computational neurosciences in the study of the brain and the possibility of deep knowledge into different devices with the support that information technology (IT) brings. One of the main interests of cognitive science is the opportunity to develop human intelligence in a system or mechanism. This paper takes the principal articles of three databases oriented to computational sciences (EbscoHost Web, IEEE Xplore and Compendex Engineering Village) to understand the current objectives of neural networks in studying the brain. The possible use of this kind of technology is to develop artificial intelligence (AI) systems that can replicate more complex human brain tasks (such as those involving consciousness). The results show the principal findings in research and topics in developing studies about neural networks in computational neurosciences. One of the principal developments is the use of neural networks as the basis of much computational architecture using multiple techniques such as computational neuromorphic chips, MRI images and brain-computer interfaces (BCI) to enhance the capacity to simulate brain activities. This article aims to review and analyze those studies carried out on the development of different computational architectures that focus on affecting various brain activities through neural networks. The aim is to determine the orientation and the main lines of research on this topic and work in routes that allow interdisciplinary collaboration.
RESUMO
Objective.The diagnosis of nerve disorders in humans has relied heavily on the measurement of electrical signals from nerves or muscles in response to electrical stimuli applied at appropriate locations on the body surface. The present study investigated the demyelinating subtype of Guillain-Barré syndrome using multiscale computational model simulations to verify how demyelination of peripheral axons may affect plantar flexion torque as well as the ongoing electromyogram (EMG) during voluntary isometric or isotonic contractions.Approach.Changes in axonal conduction velocities, mimicking those found in patients with the disease at different stages, were imposed on a multiscale computational neuromusculoskeletal model to simulate subjects performing unipodal plantar flexion force and position tasks.Main results.The simulated results indicated changes in the torque signal during the early phase of the disease while performing isotonic tasks, as well as in torque variability after partial conduction block while performing both isometric and isotonic tasks. Our results also indicated changes in the root mean square values and in the power spectrum of the soleus EMG signal as well as changes in the synchronization index computed from the firing times of the active motor units. All these quantitative changes in functional indicators suggest that the adoption of such additional measurements, such as torques and ongoing EMG, could be used with advantage in the diagnosis and be relevant in providing extra information for the neurologist about the level of the disease.Significance.Our findings enrich the knowledge of the possible ways demyelination affects force generation and position control during plantarflexion. Moreover, this work extends computational neuroscience to computational neurology and shows the potential of biologically compatible neuromuscular computational models in providing relevant quantitative signs that may be useful for diagnosis in the clinic, complementing the tools traditionally used in neurological electrodiagnosis.
Assuntos
Síndrome de Guillain-Barré , Axônios/fisiologia , Simulação por Computador , Eletrodiagnóstico , Síndrome de Guillain-Barré/diagnóstico , Humanos , Condução Nervosa/fisiologia , TorqueRESUMO
The analysis of behavior in animal models is an important objective in many research fields, including neuroscience, psychology, toxicology, and neuropsychopharmacology. Animal models have been used for many years, and several behavioral paradigms, such as locomotor activity, social interactions, and cognitive behavior, have been studied in animal models to correlate the behaviors with pharmacological or environmental interventions and with molecular, biochemical, and physiological findings. We reviewed the literature looking for open-source, freely available software to analyze animal behavior and found 12 freely available programs: ToxTrack, EthoWatcher, Mouse Behavior Tracker, Mouse Move, JAABA, wrMTrck, AnimalTracker, idTracker, Ctrax, Mousetracker, VideoHacking, and Cowlog, which were developed with different programs, work on different platforms, and have particular types of inputs and outputs and analysis capabilities. We reviewed some examples of their use, tested some of them, and provided several recommendations for the future development of programs for the automated analysis of behavior in animal models. In conclusion, we show freely available software for the automated analysis of behavior in animal models such as adult zebrafish and provide information for researchers and students looking for quick, easy-to-implement, and inexpensive behavior analysis alternatives.
Assuntos
Comportamento Animal , Etologia/métodos , Ciência dos Animais de Laboratório/métodos , Software , Peixe-Zebra , Animais , Etologia/instrumentação , Ciência dos Animais de Laboratório/instrumentaçãoRESUMO
Abstract Introduction The understanding of the neurophysiological mechanisms underlying movement control can be much furthered using computational models of the neuromusculoskeletal system. Biologically based multi-scale neuromusculoskeletal models have a great potential to provide new theories and explanations related to mechanisms behind muscle force generation at the molecular, cellular, synaptic, and systems levels. Albeit some efforts have been made to investigate how neurodegenerative diseases alter the dynamics of individual elements of the neuromuscular system, such diseases have not been analyzed from a systems viewpoint using multi-scale models. Overview and Perspectives This perspective article synthesizes what has been done in terms of multi-scale neuromuscular development and points to a few directions where such models could be extended so that they can be useful in the future to discover early predictors of neurodegenerative diseases, as well as to propose new quantitative clinical neurophysiology approaches to follow the course of improvements associated with different therapies (drugs or others). Concluding Remarks Therefore, this article will present how existing biologically based multi-scale models of the neuromusculoskeletal system could be expanded and adapted for clinical applications. It will point to mechanisms operating at different levels that would be relevant to be considered during model development, along with implications for interpreting experimental results from neurological patients.
RESUMO
BACKGROUND: Recent research suggests that the CA3 subregion of the hippocampus has properties of both autoassociative network, due to its ability to complete partial cues, tolerate noise, and store associations between memories, and heteroassociative one, due to its ability to store and retrieve sequences of patterns. Although there are several computational models of the CA3 as an autoassociative network, more detailed evaluations of its heteroassociative properties are missing. METHODS: We developed a model of the CA3 subregion containing 10,000 integrate-and-fire neurons with both recurrent excitatory and inhibitory connections, and which exhibits coupled oscillations in the gamma and theta ranges. We stored thousands of pattern sequences using a heteroassociative learning rule with competitive synaptic scaling. RESULTS: We showed that a purely heteroassociative network model can (i) retrieve pattern sequences from partial cues with external noise and incomplete connectivity, (ii) achieve homeostasis regarding the number of connections per neuron when many patterns are stored when using synaptic scaling, (iii) continuously update the set of retrievable patterns, guaranteeing that the last stored patterns can be retrieved and older ones can be forgotten. DISCUSSION: Heteroassociative networks with synaptic scaling rules seem sufficient to achieve many desirable features regarding connectivity homeostasis, pattern sequence retrieval, noise tolerance and updating of the set of retrievable patterns.
RESUMO
Resumo Nas últimas décadas o estudo de processos cognitivos vem sendo influenciado por duas tendências: a legitimação de diversas formas e níveis de estudo e a tentativa de integração multidisciplinar. A primeira teve grande importância na segunda metade do século XX, quando linhas de pesquisa na psicologia cognitiva e nas neurociências fortaleceram-se. Nesse sentido, destacam-se os três níveis de Marr (computacional, algorítmico e implementacional) como forma de estruturar o estudo dos processos cognitivos. A segunda tendência é mais recente e busca, apoiada na primeira, aprofundar o entendimento dos processos cognitivos em suas diversas escalas e integrar diversos paradigmas de estudos, buscando consiliência teórica. O intento deste artigo é apresentar a neurociência computacional e suas possíveis contribuições para a psicologia cognitiva, articulando, por meio dos três níveis de Marr, uma base teórica que explicite o papel de cada uma das disciplinas e as suas possíveis interações.
Résumé Au long des dernières décennies, l'étude des processus cognitifs se voit influencé par deux tendances : la légitimation de plusieurs formes et niveaux d'études et l'essai d'intégration multidisciplinaire. La première a eu une grande importance pendant la deuxième moitié du XXe siècle, quand des lignes de recherche en psychologie cognitive et en neurosciences ont gagné force. Dans ce sens, on peut souligner les trois niveaux de Marr (computationnel, algorithmique et implémentationnel) comme moyens de structurer l'étude des procédés cognitifs. La deuxième tendance est plus récente et cherche, avec l'aide de la première, à approfondir la connaissance des procédés cognitifs et ses différentes échelles et à intégrer plusieurs modèles d'études, en cherchant des convergences théoriques. Le but de cet article est donc de présenter la neuroscience computationnelle et ses possibles contributions pour la psychologie cognitive en articulant, par les trois niveaux de Marr, une base théorique qui puisse expliciter le rôle de chacune des disciplines et de ses possibles interactions.
Resumen En las últimas décadas, el estudio de procesos cognitivos se ha visto influenciado por dos tendencias: la legitimación de diversas formas y niveles de estudio, y el intento de integración multidisciplinar. La primera tuvo gran importancia en la segunda mitad del siglo XX, cuando varias líneas de investigación en la psicología cognitiva y en las neurociencias se fortalecieron. En ese sentido, destacan los tres niveles de Marr (computacional, algorítmico e implementacional) como una manera de estructurar el estudio de los procesos cognitivos. La segunda tendencia es más reciente y busca, apoyada en la primera, profundizar la comprensión de los procesos cognitivos en sus diversas escalas e integrar diversos paradigmas de estudios, buscando consiliencia teórica. En este artículo, se intenta presentar la neurociencia computacional y sus posibles contribuciones para la psicología cognitiva, articulando, a través de los tres niveles de Marr, una base teórica que ponga de manifiesto el papel de cada una de las disciplinas y sus posibles interacciones.
Abstract In recent decades the study of cognitive processes has been influenced by two tendencies: legitimation of several forms and levels of study and the attempt of multidisciplinary integration. The first had great importance in the second half of the 20th century, when research lines in cognitive psychology and neuroscience were strengthened. In this sense, Marr's three levels of analysis (computational, algorithmic, and implementation) are one way to structure the study of cognitive processes. The second tendency is more recent and, supported by the first one, seeks to deepen the understanding of cognitive processes in their different scales and to integrate several paradigms of studies in order to reach theoretical consilience. This article aims to introduce computational neuroscience and its possible contributions to cognitive psychology, articulating, through Marr's three levels, a theoretical basis that explains the role of each of the disciplines and their possible interactions.
Assuntos
Humanos , Visão Ocular , Cognição , Metodologias Computacionais , Neurociência Cognitiva/tendênciasRESUMO
We consider the maximum entropy Markov chain inference approach to characterize the collective statistics of neuronal spike trains, focusing on the statistical properties of the inferred model. To find the maximum entropy Markov chain, we use the thermodynamic formalism, which provides insightful connections with statistical physics and thermodynamics from which large deviations properties arise naturally. We provide an accessible introduction to the maximum entropy Markov chain inference problem and large deviations theory to the community of computational neuroscience, avoiding some technicalities while preserving the core ideas and intuitions. We review large deviations techniques useful in spike train statistics to describe properties of accuracy and convergence in terms of sampling size. We use these results to study the statistical fluctuation of correlations, distinguishability, and irreversibility of maximum entropy Markov chains. We illustrate these applications using simple examples where the large deviation rate function is explicitly obtained for maximum entropy models of relevance in this field.
RESUMO
CONTEXTO: A desatenção no transtorno de déficit de atenção e hiperatividade (TDAH) é principalmente associada à hipoatividade dopaminérgica mesocortical. Contudo, variações dopaminérgicas mesotalâmicas também afetam o controle da atenção e, possivelmente, originam alterações atencionais no TDAH. OBJETIVO: Elaboração de um modelo neurocomputacional a partir do conhecimento do funcionamento bioquímico dos sistemas dopaminérgicos mesocortical e mesotalâmico, a fim de investigar a influência dos níveis de dopamina na via mesotalâmica sobre o circuito tálamo-cortical e suas implicações nos sintomas de desatenção do TDAH. MÉTODO: Através de um conjunto de equações modelamos propriedades fisiológicas de neurônios talâmicos. A seguir, simulamos computacionalmente o comportamento do circuito tálamo-cortical variando os níveis de dopamina nas vias mesotalâmica e mesocortical. RESULTADOS: Em relação à via mesotalâmica, a hipoatividade dopaminérgica dificulta o deslocamento do foco de atenção, e a hiperatividade dopaminérgica acarreta desfocalização atencional. Quando tais situações são acompanhadas de hipoatividade dopaminérgica mesocortical, surge uma incapacidade em perceber estímulos, devido à competição sem vencedores entre regiões talâmicas pouco ativadas. A desatenção no TDAH também se origina em desequilíbrios dopaminérgicos na via mesotalâmica, que levam à focalização excessiva ou à desfocalização da atenção. CONCLUSÃO: O nosso experimento in silico sugere que no TDAH a desatenção relaciona-se com alterações dopaminérgicas, que não se restringem à via mesocortical.
BAKGROUND: Inattention symptoms observed in patients with attention deficit hyperactivity disorder (ADHD) are mostly related to a hipoactivity in the mesocortical dopaminergic pathway. However, mesothalamic dopaminergic variations also affect the attentional control, and possibly lead to attention alterations in ADHD. PURPOSE: Elaborating a neurocomputational model from biochemical knowledge of mesocortical and mesotalamic dopamine systems, to investigate how different levels of mesothalamic dopamine influence the thalamocortical loop, leading to some attention deficits observed in ADHD. METHOD: First, we model physiological properties of thalamic neurons with a set of mathematical equations. Next, we simulate computationally the modeled thalamocortical loop under different levels of mesothalamic dopamine, and also the mesocortical dopaminergic decrease. RESULTS: Low levels of mesothalamic dopamine hinders the attentional shift and, high levels of such neuromodulator lead to distraction. When such alterations occur together with a decrease in the mesocortical dopamine level, the attention deficit turns into incapacity of perceiving environmental stimuli, due to a no winner competition between low activated thalamic areas. Inattention in ADHD also has its origins in dopaminergic disturbs throughout the mesothalamic pathway, which enhance a high focusing or do not allow the attention focus consolidation. CONCLUSION: In ADHD, the inattention is related to dopaminergic alterations that are not restricted to the mesocortical system.