Search

Neuronal responses to sensory stimuli are not independent from each other. For instance, trial-to-trial fluctuations in response strength are shared between neurons. Understanding the mechanisms that underlie these pairwise correlation is critical for determining their role in encoding sensory information. Our goal was to measure the effects of different orientation preferences and electrode recording distances on the correlation of paired neurons under different task difficulty. We simultaneously recorded single-unit activities from V1 neuronal pairs with non-overlapping receptive fields separating in different millimeter scales (up to 7.5 mm), while rhesus monkeys performed a detection task under two levels of task difficulty. We found that correlation of distant neurons is dependent on distances, but not sensitive to differences of orientation preference and task difficulty. These findings suggest that correlation of distant neurons likely involves feedback from extra-striate cortex. The circuit mechanism they imply provides new constraints on the functional connectivity that correlation may play in visual processing.

In this paper, we describe the results of a controlled experiment measuring everyday movement activity through a novel recognition prototype named AIR. AIR measures distance from the feet using infrared (IR) sensors. We tested this approach for recognizing six prevalent activities: standing stationary, walking, running, walking in place, going upstairs, and going downstairs and compared results to other commonly used approaches. Our results show that AIR obtains much higher accuracy in recognizing activity than approaches that rely primarily on accelerometers. Moreover, AIR has good generalization ability when applying recognition model to new users.

User-centric ambient social contexts can be effectively captured by dynamic bluetooth data. However, conventional approaches for training classifiers struggle with social contexts that are incrementally constructed and continuously discovered in everyday environments. Incremental social contexts can confuse a classifier because it assumes that the number and composition of context classes is fixed throughout training and inference phases. To address this challenge we propose ContextSense, an ELM-based learning method for continuous and unobtrusive discovery of new social contexts incrementally from dynamic bluetooth data. Experimental results show that ContextSense can automatically cope with "incremental social context" classes that appear unpredictably in the real-world.