Neuronal interactions are an intricate part of cortical information processing generating internal representations of the environment beyond simple one-to-one mappings of the input parameter space. Here we examined functional ranges of interaction processes within ensembles of neurons in cat primary visual cortex. Seven "elementary" stimuli consisting of small squares of light were presented at contiguous horizontal positions. The population representation of these stimuli was compared to the representation of "composite" stimuli, consisting of two squares of light at varied separations. Based on receptive field measurements and by application of an Optimal Linear Estimator, the representation of retinal location was constructed as a distribution of population activation (DPA) in visual space. The spatiotemporal pattern of the DPA was investigated by obtaining the activity of each neuron for a sequence of time intervals. We found that the DPA of composite stimuli deviates from the superposition of its components because of distance-dependent (1) early excitation and (2) late inhibition. (3) The shape of the DPA of composite stimuli revealed a distance-dependent repulsion effect. We simulated these findings within the framework of dynamic neural fields. In the model, the feedforward response of neurons is modulated by spatial ranges of excitatory and inhibitory interactions within the population. A single set of model parameters was sufficient to describe the main experimental effects. Combined, our results indicate that the spatiotemporal processing of visual stimuli is characterized by a delicate, mutual interplay between stimulus-dependent and interaction-based strategies contributing to the formation of widespread cortical activation patterns.
We describe a method for user tracking and localization based on textile capacitive sensor arrays placed under the floor. The sensor array is a commercial product (SensFloor R ) that can be installed under any standard floor type (from carpet to stone) and is able to detect objects (including the user's foot) being placed on it. The challenges addressed in this paper are (1) how to map sequences of such signals onto user trajectories and (2) how to correlate the steps detected by the SensFloor system with the step detection based on a wearable accelerometer as means of user identification. Footstep detection is performed online on the devices, which are seamlessly integrated with the floor's wireless sensor network. Initial experiments performed over a week in a real life office environment show the ability to track multiple humans and to identify up to three users walking in a narrow corridor at the same time.
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