Gilad Silberberg

Bio: Gilad Silberberg is an academic researcher from Karolinska Institutet. The author has contributed to research in topics: Striatum & Neocortex. The author has an hindex of 35, co-authored 72 publications receiving 8335 citations. Previous affiliations of Gilad Silberberg include Weizmann Institute of Science & École Polytechnique Fédérale de Lausanne.

Interneurons of the neocortical inhibitory system.

Abstract: Mammals adapt to a rapidly changing world because of the sophisticated cognitive functions that are supported by the neocortex. The neocortex, which forms almost 80% of the human brain, seems to have arisen from repeated duplication of a stereotypical microcircuit template with subtle specializations for different brain regions and species. The quest to unravel the blueprint of this template started more than a century ago and has revealed an immensely intricate design. The largest obstacle is the daunting variety of inhibitory interneurons that are found in the circuit. This review focuses on the organizing principles that govern the diversity of inhibitory interneurons and their circuits.

Anatomical, physiological and molecular properties of Martinotti cells in the somatosensory cortex of the juvenile rat

Abstract: Whole-cell patch-clamp recordings followed by histochemical staining and single-cell RT-PCR were obtained from 180 Martinotti interneurones located in layers II to VI of the somatosensory cortex of Wistar rats (P13-P16) in order to examine their anatomical, electrophysiological and molecular properties. Martinotti cells (MCs) mostly displayed ovoid-shaped somata, bitufted dendritic morphologies, and axons with characteristic spiny boutons projecting to layer I and spreading horizontally across neighbouring columns more than 1 mm. Electron microscopic examination of MC boutons revealed that all synapses were symmetrical and most synapses (71%) were formed onto dendritic shafts. MCs were found to contact tuft, apical and basal dendrites in multiple neocortical layers: layer II/III MCs targeted mostly layer I and to a lesser degree layer II/III; layer IV MCs targeted mostly layer IV and to a lesser degree layer I; layer V and VI MCs targeted mostly layer IV and layer I and to a lesser degree the layer in which their somata was located. MCs typically displayed spike train accommodation (90%; n = 127) in response to depolarizing somatic current injections, but some displayed non-accommodating (8%) and a few displayed irregular spiking responses (2%). Some accommodating and irregular spiking MCs also responded initially with bursts (17%). Accommodating responses were found in all layers, non-accommodating mostly in upper layers and bursting mostly in layer V. Single-cell multiplex RT-PCR performed on 63 MCs located throughout layers II-VI, revealed that all MCs were somatostatin (SOM) positive, and negative for parvalbumin (PV) as well as vasoactive intestinal peptide (VIP). Calbindin (CB), calretinin (CR), neuropeptide Y (NPY) and cholecystokinin (CCK) were co- expressed with SOM in some MCs. Some layer-specific trends seem to exist. Finally, 24 accommodating MCs were examined for the expression of 26 ion channel genes. The ion channels with the highest expression in these MCs were (from highest to lowest); Cabeta1, Kv3.3, HCN4, Cabeta4, Kv3.2, Kv3.1, Kv2.1, HCN3, Caalpha1G, Kv3.4, Kv4.2, Kv1.1 and HCN2. In summary, this study provides the first detailed analysis of the anatomical, electrophysiological and molecular properties of Martinotti cells located in different neocortical layers. It is proposed that MCs are crucial interneurones for feedback inhibition in and between neocortical layers and columns.

Complex brain networks: graph theoretical analysis of structural and functional systems

Abstract: Recent developments in the quantitative analysis of complex networks, based largely on graph theory, have been rapidly translated to studies of brain network organization. The brain's structural and functional systems have features of complex networks--such as small-world topology, highly connected hubs and modularity--both at the whole-brain scale of human neuroimaging and at a cellular scale in non-human animals. In this article, we review studies investigating complex brain networks in diverse experimental modalities (including structural and functional MRI, diffusion tensor imaging, magnetoencephalography and electroencephalography in humans) and provide an accessible introduction to the basic principles of graph theory. We also highlight some of the technical challenges and key questions to be addressed by future developments in this rapidly moving field.

Genome-wide atlas of gene expression in the adult mouse brain

Abstract: Molecular approaches to understanding the functional circuitry of the nervous system promise new insights into the relationship between genes, brain and behaviour. The cellular diversity of the brain necessitates a cellular resolution approach towards understanding the functional genomics of the nervous system. We describe here an anatomically comprehensive digital atlas containing the expression patterns of approximately 20,000 genes in the adult mouse brain. Data were generated using automated high-throughput procedures for in situ hybridization and data acquisition, and are publicly accessible online. Newly developed image-based informatics tools allow global genome-scale structural analysis and cross-correlation, as well as identification of regionally enriched genes. Unbiased fine-resolution analysis has identified highly specific cellular markers as well as extensive evidence of cellular heterogeneity not evident in classical neuroanatomical atlases. This highly standardized atlas provides an open, primary data resource for a wide variety of further studies concerning brain organization and function.

Rhythms of the brain

Abstract: Prelude. Cycle 1. Introduction. Cycle 2. Structure defines function. Cycle 3. Diversity of cortical functions is provided by inhibition. Cycle 4. Windows on the brain. Cycle 5. A system of rhythms: from simple to complex dynamics. Cycle 6. Synchronization by oscillation. Cycle 7. The brain's default state: self-organized oscillations in rest and sleep. Cycle 8. Perturbation of the default patterns by experience. Cycle 9. The gamma buzz: gluing by oscillations in the waking brain. Cycle 10. Perceptions and actions are brain state-dependent. Cycle 11. Oscillations in the "other cortex:" navigation in real and memory space. Cycle 12. Coupling of systems by oscillations. Cycle 13. The tough problem. References.

Dynamical Systems in Neuroscience

Abstract: This book explains the relationship of electrophysiology, nonlinear dynamics, and the computational properties of neurons, with each concept presented in terms of both neuroscience and mathematics and illustrated using geometrical intuition In order to model neuronal behavior or to interpret the results of modeling studies, neuroscientists must call upon methods of nonlinear dynamics This book offers an introduction to nonlinear dynamical systems theory for researchers and graduate students in neuroscience It also provides an overview of neuroscience for mathematicians who want to learn the basic facts of electrophysiology "Dynamical Systems in Neuroscience" presents a systematic study of the relationship of electrophysiology, nonlinear dynamics, and computational properties of neurons It emphasizes that information processing in the brain depends not only on the electrophysiological properties of neurons but also on their dynamical properties The book introduces dynamical systems starting with one- and two-dimensional Hodgkin-Huxley-type models and continuing to a description of bursting systems Each chapter proceeds from the simple to the complex, and provides sample problems at the end The book explains all necessary mathematical concepts using geometrical intuition; it includes many figures and few equations, making it especially suitable for non-mathematicians Each concept is presented in terms of both neuroscience and mathematics, providing a link between the two disciplines Nonlinear dynamical systems theory is at the core of computational neuroscience research, but it is not a standard part of the graduate neuroscience curriculum - or taught by math or physics department in a way that is suitable for students of biology This book offers neuroscience students and researchers a comprehensive account of concepts and methods increasingly used in computational neuroscience

What we can do and what we cannot do with fMRI

Abstract: Functional magnetic resonance imaging (fMRI) is currently the mainstay of neuroimaging in cognitive neuroscience. Advances in scanner technology, image acquisition protocols, experimental design, and analysis methods promise to push forward fMRI from mere cartography to the true study of brain organization. However, fundamental questions concerning the interpretation of fMRI data abound, as the conclusions drawn often ignore the actual limitations of the methodology. Here I give an overview of the current state of fMRI, and draw on neuroimaging and physiological data to present the current understanding of the haemodynamic signals and the constraints they impose on neuroimaging data interpretation.