Highlights d Populations of layer 2-3 pyramidal neurons in M1 report motor performance outcome d Success and failure activity is late, prolonged, and dissociated from kinematics and reward d At trial start, layer 5 pyramidal tract activity is affected by previous outcome d Post-movement activity in M1 is required for motor performance and learning

Texture discrimination is a fundamental function of somatosensory systems, yet the manner by which texture is coded and spatially represented in the barrel cortex are largely unknown. Using in vivo two-photon calcium imaging in the rat barrel cortex during artificial whisking against different surface coarseness or controlled passive whisker vibrations simulating different coarseness, we show that layer 2–3 neurons within barrel boundaries differentially respond to specific texture coarsenesses, while only a minority of neurons responded monotonically with increased or decreased surface coarseness. Neurons with similar preferred texture coarseness were spatially clustered. Multi-contact single unit recordings showed a vertical columnar organization of texture coarseness preference in layer 2–3. These findings indicate that layer 2–3 neurons perform high hierarchical processing of tactile information, with surface coarseness embodied by distinct neuronal subpopulations that are spatially mapped onto the barrel cortex.DOI: http://dx.doi.org/10.7554/eLife.03405.001

Abstract-In the wake of recent advances in experimental methods in neuroscience, the ability to record in-vivo neuronal activity from awake animals has become feasible. The availability of such rich and detailed physiological measurements calls for the development of advanced data analysis tools, as commonly used techniques do not suffice to capture the spatio-temporal network complexity. In this paper, we propose a new hierarchical coupled geometry analysis, which exploits the hidden connectivity structures between neurons and the dynamic patterns at multiple time-scales. Our approach gives rise to the joint organization of neurons and dynamic patterns in data-driven hierarchical data structures. These structures provide local to global data representations, from local partitioning of the data in flexible trees through a new multiscale metric to a global manifold embedding. The application of our techniques to in-vivo neuronal recordings demonstrate the capability of extracting neuronal activity patterns and identifying temporal trends, associated with particular behavioral events and manipulations introduced in the experiments.

Abstract. Modern distributed applications require coallocation of massive amounts of resources. Grid level allocation systems must efficiently decide where these applications can be executed. To this end, the resource requests are described as labeled graphs, which must be matched with equivalent labeled graphs of available resources. The coallocation problem described in the paper has real-world requirements and inputs that differ from those of a classical graph matching problem. We propose a new algorithm to solve the coallocation problem. The algorithm is especially tailored for medium to large grid systems, and is currently being integrated into the QosCosGrid system's allocation module.

Adaptive movements are critical to animal survival. To guide future actions, the brain monitors different outcomes, including achievement of movement and appetitive goals. The nature of outcome signals and their neuronal and network realization in motor cortex (M1), which commands the performance of skilled movements, is largely unknown. Using a dexterity task, calcium imaging, optogenetic perturbations, and behavioral manipulations, we studied outcome signals in murine M1. We find two populations of layer 2-3 neurons, "success"and "failure" related neurons that develop with training and report end-result of trials.In these neurons, prolonged responses were recorded after success or failure trials, independent of reward and kinematics. In contrast, the initial state of layer-5 pyramidal tract neurons contains a memory trace of the previous trial's outcome.Inter-trial cortical activity was needed to learn new task requirements. These M1 reflective layer-specific performance outcome signals, can support reinforcement motor learning of skilled behavior.