Complementary Sensory and Associative Microcircuitry in Primary Olfactory Cortex

The three-layered primary olfactory (piriform) cortex is the largest component of the olfactory cortex. Sensory and intracortical inputs converge on principal cells in the anterior piriform cortex (aPC).Wecharacterize organization principles of the sensory and intracortical microcircuitry of layer II and III principal cells in acute slices of rat aPC using laser-scanning photostimulation and fast two-photon population Ca²⁺ imaging. Layer II and III principal cells are set up on a superficial-to-deep vertical axis. We found that the position on this axis correlates with input resistance and bursting behavior. These parameters scale with distinct patterns of incorporation into sensory and associative microcircuits, resulting in a converse gradient of sensory and intracortical inputs. In layer II, sensory circuits dominate superficial cells, whereas incorporation in intracortical circuits increases with depth. Layer III pyramidal cells receive more intracortical inputs than layer II pyramidal cells, but with an asymmetric dorsal offset. This microcircuit organization results in a diverse hybrid feedforward/recurrent network of neurons integrating varying ratios of intracortical and sensory input depending on a cell’s position on the superficial-to-deep vertical axis. Since burstiness of spiking correlates with both the cell’s location on this axis and its incorporation in intracortical microcircuitry, the neuronal output mode may encode a given cell’s involvement in sensory versus associative processing

Sentinel-1 Imaging Performance Verification with TerraSAR-X

This paper presents dedicated analyses of TerraSAR-X data with respect to the Sentinel-1 TOPS imaging mode. First, the analysis of Doppler centroid behaviour for high azimuth steering angles, as occurs in TOPS imaging, is investigated followed by the analysis and compensation of residual scalloping. Finally, the Flexible-Dynamic BAQ (FD-BAQ) raw data compression algorithm is investigated for the first time with real TerraSAR-X data and its performance is compared to state-of-the-art BAQ algorithms. The presented analyses demonstrate the improvements of the new TOPS imaging mode as well as the new FD-BAQ data compression algorithm for SAR image quality in general and in particular for Sentinel-1

A dual role for prediction error in associative learning

Confronted with a rich sensory environment, the brain must learn statistical regularities across sensory domains to construct causal models of the world. Here, we used functional magnetic resonance imaging and dynamic causal modeling (DCM) to furnish neurophysiological evidence that statistical associations are learnt, even when task-irrelevant. Subjects performed an audio-visual target-detection task while being exposed to distractor stimuli. Unknown to them, auditory distractors predicted the presence or absence of subsequent visual distractors. We modeled incidental learning of these associations using a Rescorla--Wagner (RW) model. Activity in primary visual cortex and putamen reflected learning-dependent surprise: these areas responded progressively more to unpredicted, and progressively less to predicted visual stimuli. Critically, this prediction-error response was observed even when the absence of a visual stimulus was surprising. We investigated the underlying mechanism by embedding the RW model into a DCM to show that auditory to visual connectivity changed significantly over time as a function of prediction error. Thus, consistent with predictive coding models of perception, associative learning is mediated by prediction-error dependent changes in connectivity. These results posit a dual role for prediction-error in encoding surprise and driving associative plasticity

Energy-efficient wireless communication

In this chapter we present an energy-efficient highly adaptive network interface architecture and a novel data link layer protocol for wireless networks that provides Quality of Service (QoS) support for diverse traffic types. Due to the dynamic nature of wireless networks, adaptations in bandwidth scheduling and error control are necessary to achieve energy efficiency and an acceptable quality of service. In our approach we apply adaptability through all layers of the protocol stack, and provide feedback to the applications. In this way the applications can adapt the data streams, and the network protocols can adapt the communication parameters