Neurons and Networks: An Introduction to Behavioral Neuroscience by John E. DowlingHarvard University Press is proud to announce the second edition of a widely admired introductory textbook. When first published, Neurons and Networks filled the need for an introductory neuroscience text that is lucid, accessible, authoritative, logically organized, and concise. Avoiding the encyclopedic coverage that makes most neuroscience texts overwhelming, Neurons and Networks focused instead on building the solid foundation of understanding and knowledge required for further study.
The new edition retains the features that made the first edition so attractive: consistent emphasis on results and concepts that have stood the test of time; abundant high-quality illustrations; exceptionally clear explanations of technical terms. Completely revised and enlarged with six new chapters, the second edition of Neurons and Networks is an introduction not just to neurobiology, but to all of behavioral neuroscience. It is an ideal text for first- or second-year college students with minimal college science exposure. It is also an invaluable resource for students in biology, psychology, anthropology, and computer science who seek an accessible guide to a discipline that will be a critically important area of research in the twenty-first century.
Network neuroscience is the emerging discipline concerned with investigating the complex patterns of interconnections found in neural systems, and identifying principles with which to understand them. Within this discipline, one particularly powerful approach is network generative modelling, in which wiring rules are algorithmically implemented to produce synthetic network architectures with the same properties as observed in empirical network data. Successful models can highlight the principles by which a network is organized and potentially uncover the mechanisms by which it grows and develops. Here, we review the prospects and promise of generative models for network neuroscience. We begin with a primer on network generative models, with a discussion of compressibility and predictability, and utility in intuiting mechanisms, followed by a short history on their use in network science, broadly. We then discuss generative models in practice and application, paying particular attention to the critical need for cross-validation.
The online version of the course is completely free to take, and those students who successfully complete the course are eligible to receive a certificate of completion from edX. The course serves as an introductory survey of topics in neuroscience, ranging from the function of ion channels in the neuronal membrane, to the activity of individual neurons and small groups of neurons, to the function of the brain and its subsystems. By utilizing fun artistic presentations, on-location fieldtrips, do-it-yourself at-home experiments and rich online interactive experiences, we have tried to make our course a totally new kind of online educational experience. We will walk you through the process of building up a neuron, piece by piece, allowing you to dynamically explore the function of the nervous system. While lectures are tied to the physical environment lecture hall, the internet has no such limitations. With the internet, we can bring you into the laboratory, or to a museum, or to a doctor's office.
Despite substantial recent progress, our understanding of the principles and mechanisms underlying complex brain function and cognition remains incomplete. Network neuroscience proposes to tackle these enduring challenges. Approaching brain structure and function from an explicitly integrative perspective, network neuroscience pursues new ways to map, record, analyze and model the elements and interactions of neurobiological systems. Two parallel trends drive the approach: the availability of new empirical tools to create comprehensive maps and record dynamic patterns among molecules, neurons, brain areas and social systems; and the theoretical framework and computational tools of modern network science. The convergence of empirical and computational advances opens new frontiers of scientific inquiry, including network dynamics, manipulation and control of brain networks, and integration of network processes across spatiotemporal domains. We review emerging trends in network neuroscience and attempt to chart a path toward a better understanding of the brain as a multiscale networked system.
This course provides an introduction to basic computational methods for understanding what nervous systems do and for determining how they function. We will explore the computational principles governing various aspects of vision, sensory-motor control, learning, and memory.
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John H. Byrne, Ph. Its phenomenal features would not be possible without the hundreds of billions of neurons that make it up, and, importantly, the connections between those neurons. Fortunately, much is known about the properties of individual neurons and simple neuronal networks, and aspects of complex neuronal networks are beginning to be unraveled. This chapter will begin with a discussion of the neuron, the elementary node or element of the brain, and then move to a discussion of the ways in which individual neurons communicate with each other. What makes the nervous system such a fantastic device and distinguishes the brain from other organs of the body is not that it has billion neurons, but that nerve cells are capable of communicating with each other in such a highly structured manner as to form neuronal networks.