Synapses represent a unique neuronal compartment specialized in communication. The morpho-functional investigation of the synapse has always been hindered by its tiny size and high density of molecular elements, but specific technological and methodological advances helped us to reduce these limitations. As the matter of fact, in recent years, we have witnessed the continuous development of new methods allowing measuring and controlling synaptic activation both in vitro and in vivo. These include new genetically encoded sensors of synaptic exo-endocytosis, but also engineered synaptic proteins able to inhibit the release of neurotransmitters. Furthermore, new promising tools allow changing the functional properties of synapses in a plasticity-like manner. All these methodological advances are likely to generate unprecedented knowledge about the dynamics of synaptic transmission and plasticity at several levels of the nervous system. Nevertheless, in most cases, the implementation of these new methods remains technically demanding, likely due to the high complexity of their operating principle. Hence, it would be worth refining and potentiating these tools in order to extend the range of experimental settings for their application.

Spiking Neural Networks (SNN) closely imitate biological networks. Information processing occurs in both spatial and temporal manner, making SNN extremely interesting for the pertinent mimicking of the biological brain. Biological brains code and transmit the sensory information in the form of spikes that capture the spatial and temporal information of the environment with amazing precision. This information is processed in an asynchronous way by the neural layer performing recognition of complex spatio-temporal patterns with sub-milliseconds delay and at with a power budget in the order of 20W. The efficient spike coding mechanism and the asynchronous and sparse processing and communication of spikes seems to be key in the energy efficiency and high-speed computation capabilities of biological brains. SNN low-power and event-based computation make them more attractive when compared to other artificial neural networks (ANN).

Synaptic transmission is the basis of neuronal communication and is thus the most important element in brain functions, ranging from sensory input to information processing. Changes in synaptic transmission can result in the formation or dissolution of memories, and can equally lead to neurological and psychiatric disorders. The proteins composing the synapse, and their respective functions, are getting increasingly known. One aspect that has become evident in the last years is that most synaptic functions are performed not by single proteins, but by highly organized multi-protein machineries, which interact dynamically to provide responses optimally suited to the needs of the neuronal network. To decipher synaptic and neuronal function, it is essential to understand the organisational, morphological and functional aspects of the molecular nanomachines that operate at the synapse. We discuss these aspects in 11 different chapters, focusing on the structure and function of the active zone, on the functional anatomy of the synaptic vesicle, and on some of the best known soluble protein complexes from the synapse, including those involved in endocytosis and vesicle recycling.

The Mammalian Spinal Cord provides a comprehensive account of the anatomy and histology of the spinal cord. The text covers the cytoarchitecture, chemoarchitecture, motor neuron distribution, long tracts, autonomic outflow, and gene expression in the spinal cord. A feature of the book is the inclusion of segment-by-segment atlases of the spinal cords of rat, mouse, newborn mouse, marmoset, rhesus monkey, and human. This book is an essential reference for researchers studying the spinal cord.

This book sets out to build bridges between the domains of photonic device physics and neural networks, providing a comprehensive overview of the emerging field of "neuromorphic photonics." It includes a thorough discussion of evolution of neuromorphic photonics from the advent of fiber-optic neurons to today’s state-of-the-art integrated laser neurons, which are a current focus of international research. Neuromorphic Photonics explores candidate interconnection architectures and devices for integrated neuromorphic networks, along with key functionality such as learning. It is written at a level accessible to graduate students, while also intending to serve as a comprehensive reference for experts in the field.

Development of new imaging technologies in recent years has transformed neuroscience in profound ways. Following on the heels of the revolution based on the Green Fluorescent Protein, refined genetically-encoded fluorescent reporters and genetic targeting strategies now enable optical recording of synaptic transmission in defined neuronal populations at speeds approaching the enviable temporal resolution of electrophysiology. Super-resolution light microscopy permits observation of synapses and their molecular machinery at sub-diffraction resolution. At the ultrastructural level, automated forms of electron microscopy, improvements in specimen fixation methods, and recent efforts to correlate data from light and electron micrographs now make the reconstruction of functional neural circuits a reality. Finally, the use of optogenetic actuators, such as channelrhodopsins, allows precise temporal and spatial manipulation of neuronal activity and is revealing profound insights into the organization of neural circuits and their roles in behavior. This research topic highlights recent advances in both light and electron microscopy, with a specific focus on approaches that combine innovations from several different fields to obtain novel information about synapse structure and function. We are confident that this collection of articles - three original research papers, six reviews, one methods paper and one perspective article - will enable neuroscientists to achieve the next generation of experiments aimed at cracking the neural code.