Abstract: A method for identifying and/or tracking objects in a spatial area. The method includes observing the area with the aid of at least one event-based sensor, the event-based sensor including light-sensitive pixels, and a relative change of the light intensity incident upon a pixel by at least a predefined percentage prompting the sensor to output an event assigned to this pixel. The method further includes, in response to the sensor, outputting a new event, ascertaining an assessment for this event which is a measure of the extent to which this event matches an already detected distribution of events, and/or of the extent to which it is plausible that the event stems from an already detected object; and in response to the ascertained assessment meeting a predefined criterion, assigning the new event to the already detected distribution, or the already detected object.

Abstract: A method for ascertaining a physical property of an object. The method includes detecting, for each input point in time of a sequence of input points in time, sensor data including information about a physical object, using an event-based sensor; for each subsequence of a breakdown of the sequence of input points in time into multiple subsequences including: feeding the sensor data detected for the input points in time of the subsequence to a pulsed neural network which generates a first processing result of the subsequence; feeding the processing result of the subsequence to a non-pulsed neural network; and processing the processing result of the subsequence by non-pulsed neurons of one or multiple first layer(s) of the non-pulsed neural network for generating a second processing result of the subsequence; and feeding the second processing results of the multiple subsequences to one or multiple second layer(s) of the non-pulsed neural network.

Abstract: A method for identifying and/or tracking objects in a spatial area. The method includes observing the area with the aid of at least one event-based sensor, the event-based sensor including light-sensitive pixels, and a relative change of the light intensity incident upon a pixel by at least a predefined percentage prompting the sensor to output an event assigned to this pixel. The method further includes, in response to the sensor, outputting a new event, ascertaining an assessment for this event which is a measure of the extent to which this event matches an already detected distribution of events, and/or of the extent to which it is plausible that the event stems from an already detected object; and in response to the ascertained assessment meeting a predefined criterion, assigning the new event to the already detected distribution, or the already detected object.

Abstract: A method for creating a pulsed neural network (Spiking Neural Network). The method begins with an assignment of a predefinable control pattern (rollout pattern) to a deep neural network. This is followed by a training of the deep neural network using the control pattern. This is followed by a conversion of the deep neural network into the pulsed neural network, the connections of the pulsed neural network being assigned a delay, in each case as a function of the control pattern. A computer program, a device for carrying out the method, and to a machine-readable memory element are also described.