Computer-implemented or hardware-implemented method of entity identification, a computer program product and an apparatus for entity identification

1. A computer-implemented or hardware-implemented method of entity identification, comprising:

a) providing a network of nodes with input from a plurality of sensors;

b) generating by each node of the network, an activity level, based on the input from the plurality of sensors;

c) comparing the activity level of each node to a threshold level;

d) based on the comparing, for each node, setting the activity level to a preset value or keeping the generated activity level;

e) calculating a total activity level as the sum of all activity levels of the nodes of the network;

f) iterating a)-e) until a local minimum of the total activity level has been reached; and

g) when the local minimum of the total activity level has been reached, utilizing a distribution of activity levels at the local minimum to identify a measurable characteristic of the entity;

wherein the input changes dynamically over time and follows a sensor input trajectory and

wherein the plurality of sensors monitor a dependency between sensors and

wherein a local minimum of the total activity level has been reached when a sensor input trajectory has been followed with a deviation smaller than a user-definable deviation threshold for a time period longer than a user-definable time threshold;

h) finding the entity of a plurality of physical entities that has the characteristic, which is closest to the measurable characteristic identified from a memory, a look-up table or a database.

2. The computer-implemented or hardware-implemented method of claim 1 , wherein the activity level of each node is utilized as inputs, each weighted with a weight, to all other nodes, and wherein at least one weighted input is negative and/or wherein at least one weighted input is positive and/or wherein all kept generated activity levels are positive scalars.

3. The computer-implemented or hardware-implemented method of claim 2 , wherein the input from the plurality of sensors are pixel values, such as intensity, of images captured by a camera and wherein the distribution of activity levels across all nodes is further utilized to control a position of the camera by rotational and/or translational movement of the camera, thereby controlling the sensor input trajectory and wherein the entity identified is an object or a feature of an object present in at least one image of the captured images.

4. The computer-implemented or hardware-implemented method of claim 2 , wherein the plurality of sensors are touch sensors and the input from each of the plurality of sensors is a touch event signal with a force dependent value and wherein the distribution of activity levels across all nodes are utilized to identify the sensor input trajectory as a new contact event, the end of a contact event, a gesture or as an applied pressure.

5. The computer-implemented or hardware-implemented method of claim 2 , wherein each sensor of the plurality of sensors is associated with a different frequency band of an audio signal, wherein each sensor reports an energy present in the associated frequency band, and wherein the combined input from a plurality of such sensors follows a sensor input trajectory, and wherein the distribution of activity levels across all nodes are utilized to identify a speaker and/or a spoken letter, syllable, phoneme, word or phrase present in the audio signal.

6. The computer-implemented or hardware-implemented method of claim 1 , wherein the network is activated by an activation energy X, which impacts where the local minimum of the total activity level is.

7. A computer program product comprising a non-transitory computer readable medium, having thereon a computer program comprising program instructions, the computer program being loadable into a data processing unit and configured to cause execution of the method according to claim 1 when the computer program is run by the data processing unit.

8. An apparatus for entity identification, the apparatus comprising controlling circuitry configured to cause:

a) provision of a network of nodes with input from a plurality of sensors;

b) generation, by each node of the network, of an activity level, based on the input from the plurality of sensors;

c) comparison of the activity level of each node to a threshold level;

d) based on the comparison, for each node, setting of the activity level to a preset value or keeping of the generated activity level;

e) calculation of a total activity level as the sum of all activity levels of the nodes of the network;

f) iteration of a)-e) until a local minimum of the total activity level has been reached; and

g) when the local minimum of the total activity level has been reached, utilization of a distribution of activity levels at the local minimum to identify a measurable characteristic of the entity;

wherein the input changes dynamically over time and follows a sensor input trajectory and

wherein the plurality of sensors monitor a dependency between sensors and

wherein a local minimum of the total activity level has been reached when a sensor input trajectory has been followed with a deviation smaller than a user-definable deviation threshold for a time period longer than a user-definable time threshold;

h) finding the entity of a plurality of physical entities that has the characteristic, which is closest to the measurable characteristic identified from a memory, a look-up table or a database.