The deceptively simple laminar structure of neocortex belies the complexity of intra- and interlaminar connectivity. responses varied by layer: 13% in layer 2/3, 54% in layer 5A, 25% in layer 5B, and 17% in layer 6. The frequency and phase of the resulting oscillation also depended on stimulation layer. By demonstrating the effectiveness of combined static and dynamic analysis, our results show how static brain maps can be related to the results of brain activity mapping. 1 Introduction The laminar structure of neocortex has been known for over a century, but cellular properties and the synaptic connectivity of the microcircuit are only now being elucidated. While both conventional electrophysiological recordings that characterize intrinsic properties of neurons (Chen & Fetz, 2005; Dembrow, Chitwood, & Johnston, 2010; Hattox & Nelson, 2007), and anatomical methods that reveal structural aspects of connectivity continue to be useful (Kameda et al., 2012; Tanaka et al., 2011), much progress has been made in elucidating the circuitry of microcircuits using a variety of high-resolution electrophysiological and optical techniques (for reviews, see Bastos et al., 2012; Douglas & Martin, 2004; Thomson & Bannister, 2003). Paired recordings have revealed the detailed laminar organization of the pyramidal neuron network in somatosensory (Lefort, Tomm, 17-AAG (KOS953) IC50 Floyd Sarria, & Petersen, 2009) and prefrontal (Morishima & Kawaguchi, 2006; Wang et al., 2006) cortices. Glutamate uncaging has been used with laser scanning to map excitatory connections in visual (Dantzker & Callaway, 2000), barrel (Bureau, Shepherd, & Svoboda, 2004, Schubert et al., 2001), and auditory (Oviedo, Bureau, Svoboda, & Zador, 2010) cortices. Optical and optogenetic methods have aslo emerged as a tool for mapping circuits involving interneurons (K?tzel, Zemelman, Buetfering, W?lfel, & Miesenb?ck, 2010; Packer & Yuste, 2011). Assimilation of anatomical and physiological information through computational simulations is required for understanding functional connectivity. For example, quantitative approaches have drawn on morphological reconstructions to explore circuit structure (Binzegger, Douglas, & Martin, 2004; Stepanyants & Chklovskii, 2005; Stepanyants et al., 2008). A variety of network modeling efforts are beginning to incorporate aspects of the detailed 17-AAG (KOS953) IC50 cellular and synaptic-level circuit information to constrain simulations of the dynamic behavior of cortical circuits (Ainsworth et al., 2011; Lang, Dercksen, Sakmann, & Oberlaender, 2011; Markram, 2006; Roopun et al., 2008; Vierling-Claassen, Cardin, Moore, & Jones, 2010; Wang, 2010). The simulations were built on a data set obtained using a variety of techniques, including anatomical and cell recording, as well as laser-scanning photostimulation with glutamate uncaging and optogenetic stimulation. Simulation is a 17-AAG (KOS953) IC50 valuable complement to brain activity mapping. Effects of underdefined parameters can be tested in the simulation to determine critical parameters, which can then be sought in experiment. Simulation also has the advantage of providing Mouse monoclonal to CHUK access to information that must otherwise be inferred, enabling systematic analysis of functional connectivity and network dynamics. In some cases, simulation permits compensation for experimentally imposed limitations. These features make simulation a useful complement to experiment and can potentially lead to predictions that can be verified or falsified through empirical 17-AAG (KOS953) IC50 testing. Simulations can be performed at multiple scales. Mean field models, whose state variables represent the averages of action potential firing across a large population of cells (Wilson & Cowan, 1972), have the advantages of speed and analyzability at the cost of ignoring spike timing and unit synchrony. They also do not take into account detailed connectivity patterns, making them inadequate to study the effects of specific wiring patterns in the microcircuit. Simulations, however, can take into account electrical and chemical activity in individual neurons, with detailed characterization of subcellular compartments such as dendrites and synapses. While closely following empirical details from pharmacology and genomics, the computational cost of such simulations is extremely high. In addition, many parameters remain poorly constrained by experiment. The current simulation design is at an intermediate level of abstraction, utilizing a large network of modified integrate-and-fire neurons that can be tuned to grossly produce various dynamical features of different neuronal subtypes. We have developed a spiking unit model of primary motor cortex (M1), based.