The microcircuitry of the mammalian neocortex remains largely unknown. Although the neocortex could be composed of scores of precise circuits, an alternative possibility is that local connectivity is probabilistic or even random. To examine the precision and degree of determinism in the neocortical microcircuitry, we used optical probing to reconstruct microcircuits in layer 5 from mouse primary visual cortex. We stimulated "trigger" cells, isolated from a homogenous population of corticotectal pyramidal neurons, while optically detecting "follower" neurons directly driven by the triggers. Followers belonged to a few selective anatomical classes with stereotyped physiological and synaptic responses. Moreover, even the position of the followers appeared determined across animals. Our data reveal precisely organized cortical microcircuits.

Dendritic morphology constrains brain activity, as it determines first which neuronal circuits are possible and second which dendritic computations can be performed over a neuron's inputs. It is known that a range of chemical cues can influence the final shape of dendrites during development. Here, we investigate the extent to which self-referential influences, cues generated by the neuron itself, might influence morphology. To this end, we developed a phenomenological model and algorithm to generate virtual morphologies, which are then compared to experimentally reconstructed morphologies. In the model, branching probability follows a Galton–Watson process, while the geometry is determined by “homotypic forces” exerting influence on the direction of random growth in a constrained space. We model three such homotypic forces, namely an inertial force based on membrane stiffness, a soma-oriented tropism, and a force of self-avoidance, as directional biases in the growth algorithm. With computer simulations we explored how each bias shapes neuronal morphologies. We show that based on these principles, we can generate realistic morphologies of several distinct neuronal types. We discuss the extent to which homotypic forces might influence real dendritic morphologies, and speculate about the influence of other environmental cues on neuronal shape and circuitry.

An experimental difficulty in unraveling circuits in the mammalian nervous system is the identification of postsynaptic targets of a given neuron. Besides ultrastructural reconstructions, simultaneous recordings from pairs of cells in brain slices have been used to identify connected neurons. We describe in this paper a technique using calcium imaging that allows rapid identification of potential postsynaptic targets. This method consists of stimulating one neuron (''trigger'') while imaging a population of cells to detect which other neurons (''followers'') are activated by the trigger. By using bulk-loading of calcium indicators in slices of mouse visual cortex, we demonstrate that neurons that display somatic calcium transients time-locked to the spikes of a trigger neuron can be monosynaptically connected to it. This technique could be applied to reconstruct and assay circuits in the central nervous system. A n essential part of neurobiology is the characterization of circuits. Although knowledge of circuit diagrams is necessary to understand properly any computation (1), in most nervous systems the detailed circuits remain mysterious, even when the nature of the computation is clear (2). A direct approach to deciphering circuits is their reconstruction with electron microscopy. This reconstruction has been achieved only in the nervous system of Caenorhabditis elegans, which consists of 302 neurons with a stereotyped connectivity from animal to animal (3). For most preparations, however, electron microscopic reconstructions of entire circuits are impractical because of the high number of neurons present and the laboriousness of the serial reconstruction (4-6).Another approach to identify circuits is to perform intracellular recordings from connected cells. This has been done extensively in invertebrate studies (7). In vertebrate preparations, dual recordings of randomly chosen neurons in brain slices have been combined with anatomical reconstructions to identify synaptic contacts (8-10). This approach, however, suffers from the problem that the probability that randomly chosen neurons are connected is low. This and the large number of neuronal classes make testing of possible connections and sequential examination of circuits impractical.In this paper, we describe a method to identify potential postsynaptic targets of a given neuron in brain slices. We bulk-load calcium indicators into populations of neurons and then image somatic calcium transients to detect the neurons that produce action potentials (APs) time-locked to a stimulated cell. We demonstrate the usefulness of this technique by finding monosynaptically connected neurons in layer 5 from the mouse visual cortex. MethodsSlice Preparation and Staining. Slices were made from visual cortex of postnatal day (P)12-P23 C57BL͞6 mice. Animals were anesthetized with 120 mg/kg ketamine͞10 mg/kg xylazine and decapitated. The brain quickly was removed and placed into cold artificial cerebrospinal fluid (ACSF; 126 mM NaCl͞3 mM KCl͞26 mM NaHCO 3 ͞1 mM NaH 2 PO 4...

Highlights d Multiple approaches reveal transient K + elevations during ChR2 excitation d ChR2-mediated K + elevations increase neuronal excitability and cFos expression d Neuronal effects of K + are recapitulated with a model and in vivo d Increased K + may contribute to astrocyte experiments employing ChR2 in vivo

Neural tissue simulation extends requirements and constraints of previous neuronal and neural circuit simulation methods, creating a tissue coordinate system. We have developed a novel tissue volume decomposition, and a hybrid branched cable equation solver. The decomposition divides the simulation into regular tissue blocks and distributes them on a parallel multithreaded machine. The solver computes neurons that have been divided arbitrarily across blocks. We demonstrate thread, strong, and weak scaling of our approach on a machine with more than 4000 nodes and up to four threads per node. Scaling synapses to physiological numbers had little effect on performance, since our decomposition approach generates synapses that are almost always computed locally. The largest simulation included in our scaling results comprised 1 million neurons, 1 billion compartments, and 10 billion conductance-based synapses and gap junctions. We discuss the implications of our ultrascalable Neural Tissue Simulator, and with our results estimate requirements for a simulation at the scale of a human brain.