Rich in detail, Hippocampal Microcircuits: A Computational Modeler’s Resource Book provides succinct and focused reviews of experimental results. It is an unparalleled resource of data and methodology that will be invaluable to anyone wishing to develop computational models of the microcircuits of the hippocampus. The editors have divided the material into two thematic areas. Covering the subject’s experimental background, leading neuroscientists discuss the morphological, physiological and molecular characteristics as well as the connectivity and synaptic properties of the various cell types found in the hippocampus. Here, ensemble activity, related to behavior, on the part of morphologically identified neurons in anesthetized and freely moving animals, lead to insights into the functions of hippocampal areas. In the second section, on computational analysis, computational neuroscientists present models of hippocampal microcircuits at various levels of detail, including single-cell and network levels. A full chapter is devoted to the single-neuron and network simulation environments currently used by computational neuroscientists in developing their models. In addition to the above, the chapters also identify outstanding questions and areas in need of further clarification that will guide future research by computational neuroscientists.

This is the 2nd edition of a very well received and popular book that reflects the current state-of-the-art of the ongoing research avenues concerning the hippocampus and processing units bridging the gap between single cell activity, network activity and global brain function. It aims to provide a methodology to anyone interested in developing microcircuit level models of the hippocampus. The book is divided into two thematic areas: (I) Experimental background and (II) Computational analysis. In part I, leading experimental neuroscientists discuss the morphological, physiological and molecular characteristics as well as the connectivity and synaptic properties of the various cell types found in the hippocampus. Behaviour-related ensemble activity patterns of morphologically identified neurons in anesthetized and freely moving animals provide insights on the function of the hippocampal areas. In part II, computational neuroscientists present models of the hippocampal microcircuits at various levels of detail (e.g. single cell level, network level, etc.). Synaptomics and connectomics models of hippocampal structures are initially discussed. Then, network models of memory, rhythm generation and spatial navigation are presented, followed by abstract and biophysical models of synaptic plasticity. Network models of hippocampal implicated disorders (epilepsy and schizophrenia) are then detailed and how their network topologies, connectivities and activities change in these diseases. Finally, two chapters are dedicated to describing simulator environments of single neurons and networks currently used by computational neuroscientists in developing their models and modelling tools to parametrically constrain them. This engaging volume is invaluable to experimental and computational neuroscientists, electrical engineers, physicists, mathematicians and others interested in developing microcircuit models of the hippocampus. Graduate level students and trainees in all of these fields can find this book a significant source of information.

The hippocampus mediates several higher brain functions, such as learning, memory, and spatial coding. The input region of the hippocampus, the dentate gyrus, plays a critical role in these processes. Several lines of evidence suggest that the dentate gyrus acts as a preprocessor of incoming information, preparing it for subsequent processing in CA3. For example, the dentate gyrus converts input from the entorhinal cortex, where cells have multiple spatial fields, into the spatially more specific place cell activity characteristic of the CA3 region. Furthermore, the dentate gyrus is involved in pattern separation, transforming relatively similar input patterns into substantially different output patterns. Finally, the dentate gyrus produces a very sparse coding scheme in which only a very small fraction of neurons are active at any one time. How are these unique functions implemented at the level of cells and synapses? Dentate gyrus granule cells receive excitatory neuron input from the entorhinal cortex and send excitatory output to the hippocampal CA3 region via the mossy fibers. Furthermore, several types of GABAergic interneurons are present in this region, providing inhibitory control over granule cell activity via feedback and feedforward inhibition. Additionally, hilar mossy cells mediate an excitatory loop, receiving powerful input from a small number of granule cells and providing highly distributed excitatory output to a large number of granule cells. Finally, the dentate gyrus is one of the few brain regions exhibiting adult neurogenesis. Thus, new neurons are generated and functionally integrated throughout life. How these specific cellular and synaptic properties contribute to higher brain functions remains unclear. One way to understand these properties of the dentate gyrus is to try to integrate experimental data into models, following the famous Hopfield quote: “Build it, and you understand it.” However, when trying this, one faces two major challenges. First, hard quantitative data about cellular properties, structural connectivity, and functional properties of synapses are lacking. Second, the number of individual neurons and synapses to be represented in the model is huge. For example, the dentate gyrus contains ~1 million granule cells in rodents, and ~10 million in humans. Thus, full scale models will be complex and computationally demanding. In this Frontiers Research Topic, we collect important information about cells, synapses, and microcircuit elements of the dentate gyrus. We have put together a combination of original research articles, review articles, and a methods article. We hope that the collected information will be useful for both experimentalists and modelers. We also hope that the papers will be interesting beyond the small world of “dentology,” i.e., for scientists working on other brain areas. Ideally, the dentate gyrus may serve as a blueprint, helping neuroscientists to define strategies to analyze network organization of other brain regions.

During social interactions, humans and social animals can distinguish not only familiar and novel conspecifics (social recognition) but also between multiple familiar individuals (social specification). Recent studies have implicated hippocampal sub-region dorsal CA2 (dCA2) in social recognition and identified social recognition memory engram in downstream ventral CA1 (vCA1). However, the anatomical site for the storage of social specification memory and its underlying neuroscientific mechanisms are poorly known. Here, we report that social specification memory engrams are stored in vCA1 while social information encoded in dCA2 becomes sharpened as it travels from dCA2 to vCA1 microcircuits within CA2, thereby acquiring a progressive increase in specification through repeating motifs of feed-forward inhibition. Both the inhibition of GABAergic inhibitory neurons in CA2 and reduced activity of excitatory neurons by ablation of oxytocin receptors in the dCA2 to vCA1 microcircuits impair social memory specification. These results suggest that the vCA1 and the multiple feed-forward inhibition motifs in the dCA2 to vCA1 microcircuits are crucial for social memory specification.

In order to focus on principles, each chapter in this work is brief, organized around 1-3 wiring diagrams of the key circuits, with several pages of text that distil the functional significance of each microcircuit

A comprehensive Introduction to the world of brain and behavior computational models This book provides a broad collection of articles covering different aspects of computational modeling efforts in psychology and neuroscience. Specifically, it discusses models that span different brain regions (hippocampus, amygdala, basal ganglia, visual cortex), different species (humans, rats, fruit flies), and different modeling methods (neural network, Bayesian, reinforcement learning, data fitting, and Hodgkin-Huxley models, among others). Computational Models of Brain and Behavior is divided into four sections: (a) Models of brain disorders; (b) Neural models of behavioral processes; (c) Models of neural processes, brain regions and neurotransmitters, and (d) Neural modeling approaches. It provides in-depth coverage of models of psychiatric disorders, including depression, posttraumatic stress disorder (PTSD), schizophrenia, and dyslexia; models of neurological disorders, including Alzheimer’s disease, Parkinson’s disease, and epilepsy; early sensory and perceptual processes; models of olfaction; higher/systems level models and low-level models; Pavlovian and instrumental conditioning; linking information theory to neurobiology; and more. Covers computational approximations to intellectual disability in down syndrome Discusses computational models of pharmacological and immunological treatment in Alzheimer's disease Examines neural circuit models of serotonergic system (from microcircuits to cognition) Educates on information theory, memory, prediction, and timing in associative learning Computational Models of Brain and Behavior is written for advanced undergraduate, Master's and PhD-level students—as well as researchers involved in computational neuroscience modeling research.

This three volume set LNCS 6352, LNCS 6353, and LNCS 6354 constitutes the refereed proceedings of the 20th International Conference on Artificial Neural Networks, ICANN 2010, held in Thessaloniki, Greece, in September 2010. The 102 revised full papers, 68 short papers and 29 posters presented were carefully reviewed and selected from 241 submissions. The first volume is divided in topical sections on ANN applications, Bayesian ANN, bio inspired – spiking ANN, biomedical ANN, computational neuroscience, feature selection/parameter identification and dimensionality reduction, filtering, genetic – evolutionary algorithms, and image – video and audio processing.

The way you perceive the world, plan, make decisions and communicate your thoughts and feelings depends on the function and hierarchical arrangement of cortical modules. The ability to both provide adaptive responses to our ever-changing environment and to pursue a useful role in society is the most important problem faced by present day neuroscientists. In essence, the workings of cortical modules define the nature of our soul, making each of us who we are. This book provides a breath-taking view of different perspectives by world renowned authorities as to the workings of these cortical modules both in the normal state and in mental disorders.

Featuring research from the 2017 research symposium of the Association for Women in Mathematics, this volume presents recent findings in pure mathematics and a range of advances and novel applications in fields such as engineering, biology, and medicine. Featured topics include geometric group theory, generalized iterated wreath products of cyclic groups and symmetric groups, Conway-Coxeter friezes and mutation, and classroom experiments in teaching collegiate mathematics. A review of DNA topology and a computational study of learning-induced sequence reactivation during sharp-wave ripples are also included in this volume. Numerous illustrations and tables convey key results throughout the book. This volume highlights research from women working in academia, industry, and government. It is a helpful resource for researchers and graduate students interested in an overview of the latest research in mathematics.