Patent Description:
From a biology perspective, embodiments of the present invention are framed in the computational understanding of the underlying general cognitive mechanism of neuron-based life beings. From a computational perspective, embodiments of the invention are directed to automatic learning and processing mechanisms, specifically, to recurrent and parallel computing methods under the connectionist paradigm and, strictly, under no neuron, no dendrites or any biology physical simulation-based approach.

According to embodiments of the present invention, an FCN should be understood as a node that performs computational operations according to cognitive and fractal premises.

The target problem to solve is the definition of a computational model of cognition which, through exposure of external input data, produces output relational responses at sensory-motor episodic and high attentional levels, and capable to model and reproduce component-oriented reactive and reasoning behaviors.

The problem can be divided into the following sub-problems:.

The cognition definition is used in the context of cognitive science approach and it refers to any kind of mental operation or structure that can be studied in precise terms. In a more generalist way, cognition can be defined as follows: The mental action or process of acquiring knowledge and understanding thought, experience, and the senses and actuators.

Besides the above, general cognition problem can be defined as a real-time spatio-temporal sampling strategy of synchronic events to deliver scalable abstraction and inference effects over a n-dimensional sampling domain in a universal, scalable, structurally and representationally adaptive manner and synthesized through a suitable physical memorization mechanism. <FIG> illustrates an example thereof, in which:.

Additionally, the Cognitive System must perform the cognitive function over domain in one or more, or in many cases all, of the following manners:.

Some patents and/or patent applications are known in the field.

For example, <CIT> discloses methods, systems and computer program products to memorize multiple inputs into an artificial neuron that includes multiple dendrites each having multiple dendrite compartments. Operations include computing coincidence detection as distal synapse activation that flows from more proximal ones of the dendrite compartments to a soma of the artificial neuron, generating a dendritic action potential responsive to the coincidence detection from a non-zero activation value input received at a corresponding one of the dendrite compartments that includes a non-zero receptivity, and responsive to generating the dendritic action potential, decrementing the activation value and the receptivity and passing the decremented activation value to a next one of the dendrite compartments.

<CIT> discloses a computer-implemented system including an edge module and at least one input device coupled to the edge module. The at least one input device is configured to generate data input signals. The system also includes a cognitive module coupled to the edge module. The cognitive module includes a perception sub-module coupled to the edge module. The perception sub-module is configured to receive the data input signals. The cognitive module also includes a learning sub-module coupled to the perception sub-module. The learning sub-module is configured to adaptively learn at least in part utilizing the data input signals.

<CIT> relates to a data processing method, which runs on a data processing device, for mapping input data to be processed onto output data. According to this method: the data objects to be processed are input as input data; the input data objects are processed with aid of a topology-preserving map by the arrangement of neurons according to a predetermined schema in an arrangement space; code book objects in the result space are assigned to the neurons, and; code book objects are processed in accordance with the calculation rule of a topology-preserving map while using data objects of the investigation space. The processed code book objects are output as output data. Embodiments of the invention are characterized in that at least a portion of the input data objects is used in order to determine the arrangement of neurons in the arrangement space and/or in that data objects are input, which are required for data processing, are independent of the input data to be processed, and which are used as data objects of the information space.

<CIT> discloses methods, systems, and apparatus for pattern recognition. A pattern recognizing engine includes multiple pattern recognizer processors that form a hierarchy of pattern recognizer processors. The pattern recognizer processors include a child pattern recognizer processor at a lower level in the hierarch and a parent pattern recognizer processor at a higher level of the hierarchy, where the child pattern recognizer processor is configured to provide a first complex recognition output signal to a pattern recognizer processor at a higher level than the child pattern recognizer processor, and the parent pattern recognizer processor is configured to receive as an input a second complex recognition output signal from a pattern recognizer processor at a lower level than the parent pattern recognizer processor.

<CIT> provides a cognitive architecture that uses hierarchically arranged modules to associate the computation of many expected attributes with a single concept. A first value stored in a first module represents a concept state. A second value stored in the first module represents an expectation state. The first module receives a third value that represents an input state. The first module has a lower hierarchy than a second module and a higher hierarchy than a third module. The first module provides the first value to the second module and the second value to the third module. The first value is computed using the third value and concept information stored in the first module. The second value is computed using the first value, the third value and expectation information stored in the first module. An actuated mechanism receives a signal derived from the second value, which influences an operation of the actuated mechanism.

<CIT> discloses a system that includes an encoder neural network configured to receive an input sequence and generate encoded representations of the network inputs. The encoder neural network comprises a sequence of one or more encoder subnetworks, each encoder subnetwork configured to receive a respective encoder subnetwork input for each of the input positions and to generate a respective subnetwork output for each of the input positions, and each encoder subnetwork comprising: an encoder self-attention sub-layer that is configured to receive the subnetwork input for each of the input positions and, for each particular input position in the input order: apply an attention mechanism over the encoder subnetwork inputs using one or more queries derived from the encoder subnetwork input at the particular input position.

Finally, scientific article "Detecting performance anomalies in scientific workflows using hierarchical temporal memory" discloses the use of Hierarchical Temporal Memory (HTM) to detect performance anomalies in the execution of scientific workflows in distributed computing environments such as clouds. The method is an online approach that can be deployed on different infrastructures without the need of previously collecting data for training purposes. HTM enables the framework to learn incrementally and detect anomalies in an unsupervised manner while adjusting to changes in the statistics of the data. The data analyzed corresponded to resource consumption metrics of executing workflow tasks and was processed in an online manner, as it became available.

Further technological background is known from an article of<NPL>.

However, none of the known state of the art proposals allows solving the cited problems based on the goals stated above.

Accordingly, the subject matter according to the independent claims is suggested.

Embodiments of the present invention provide, according to one aspect, a computer system having a fractal cognitive computing node (FCN) for learning procedures. The proposed FCN includes: a first input, to receive a first input signal, said first input signal comprising a set of pairs <key, value>, the key representing an identifier and the value representing a magnitude of the first input signal; a first output, to provide a first output signal, said first output signal comprising a set of pairs <key, value>, the key representing an identifier and the value representing a magnitude of the output signal; a second input, to receive a second input signal, said second input signal being a first output signal of another FCN and comprising a set of pairs <key, value>, the key representing an identifier and the value representing a magnitude of the second input signal; and a third input, to receive a spatial attention (SA) parameter comprising a single dimension value.

According to embodiments of the invention, the FCN may also include a memory and at least one processing unit. The memory stores a collection of items, each item having a set of tuples <key, value>, and each item representing a previously stored input from at least one of said first or second input signals. The processing unit is adapted to implement a computational function that compares the first input signal, the second input signal or a combination thereof to some or all of the stored collection of items and calculates a similarity measure for each compared item.

The first output signal is particularly calculated as a selection of the compared items having a similarity measure greater than the cited SA parameter. In case the selection of compared items is empty, a new item is added to the memory and the set of tuples <key, value> of the new item and the first output signal are set according to the first input signal, according to the second input signal or according to the combination of the first and second input signals. On the contrary, if the selection of compared items is not empty, the first output signal is set to said selection. In addition, the first output signal can be also set taking into consideration any tuple matching specific criteria, e.g. by considering the "value" fields of the tuples.

In an embodiment, particularly, the FCN further has a second output to provide a confidence (CF) parameter. The CF parameter is computed, by the processing unit, as a statistical grouping function over the similarity measures of the cited selection.

In an embodiment, the FCN further may have a fourth input to receive a temporal attention (TA) parameter comprising a single dimension value. The processing unit can modify the values of a given tuple stored in the memory by decreasing the value by said TA parameter.

Likewise, the FCN may further have a fifth input to receive a conditioning (CD) parameter comprising a single dimension value. In this case, each item stored in the memory comprises a set of tuples <key, value, depth>, and the processing unit can modify the depth value of tuples for the selection of compared items by a magnitude defined as an addition or subtraction of a value of said CD parameter.

In addition, the FCN may further have a sixth input to receive a focus parameter (AF) relating to a filtering criterion. The processing unit can modify the value of the first output signal based on the filtering criterion of the AF parameter.

Moreover, the FCN may further have a third output to provide a coherence (CH) parameter. In particular, the CH parameter is computed as a similarity measure between the second input signal and the first output signal by the processing unit.

According to an embodiment of the invention, the first and second input signals and the first output signal can be received/provided in sparse-distributed representation, among any other form of compositional signal.

The first input signal is received from another FCN connected thereto. Alternatively, the first input signal is received from a (remote) sensor/actuator.

In a particular embodiment, the FCN may be connected to one or more different FCNs forming a hierarchical structure.

Embodiments of the present invention also provide, according to a second aspect, a computer-implemented method for learning procedures, the method comprises: receiving, by a first input of a computing node, a first input signal comprising a set of pairs <key, value>, the key representing an identifier and the value representing a magnitude of the first input signal; receiving, by a second input of said computing node, a second input signal comprising a set of pairs <key, value>, the key representing an identifier and the value representing a magnitude of the second input signal, the second input signal being an output signal of another computing node; and receiving, by a third input of the computing node, a SA parameter comprising a single dimension value.

An embodiment of the proposed method may also comprise implementing, by a processing unit of the computing node, a computational function that compares the first input signal or the second input signal or a combination of said first and second input signals to some or all of a collection of items stored in a memory of the computing node, each item having a set of tuples <key, value>, and each item representing a previously stored input from at least one of said first or second input signals, and that calculates a similarity measure for each compared item.

In an embodiment, the first output signal may be output via a first output of the computing node. The first output signal comprises a set of pairs <key, value>, and is calculated as a selection of the compared items having a similarity measure greater than said SA parameter.

In an embodiment, the proposed method may also comprise computing, by the processing unit, a CF parameter as a statistical grouping function over the similarity measures of said selection. The computed CF parameter is outputted via a second output of the computing node.

In an embodiment, the proposed method may also comprise receiving, by a fourth input of the computing node, a TA parameter comprising a single dimension value, and modifying, by the processing unit, the value of a given tuple stored in the memory by decreasing the value by said TA parameter.

In an embodiment, the proposed method may also comprise receiving, by a fifth input of the computing node, a CD parameter comprising a single dimension value, each item stored in the memory has a set of tuples <key, value, depth>, and modifying, by the processing unit, the depth value of tuples for the selection of compared items by a magnitude defined as an addition or subtraction of a value of said CD parameter.

In an embodiment, the proposed method may also comprise receiving, by a sixth input of the computing node, a focus parameter (AF) relating to a filtering criterion. Then, the processing unit modifies the value of the first output signal based on the filtering criterion of the AF parameter.

In another embodiment, the proposed method may also comprise computing, by the processing unit, a CH parameter as a similarity measure between the second input signal and the first output signal, and providing the computed CH parameter via a third output of the computing node.

In an embodiment, the proposed method may also comprise replication of the FCN (e.g. parent FCN) into a new FCN (e.g. child FCN) in hierarchical configuration based on each specific item in the memory, assigning to the new FCN specific SA and TA parameters and an attachment to its corresponding tuple.

Embodiments of the present invention provide a fractal cognitive computing node (FCN) <NUM> that provides a computational approach for general cognition, a computer-implemented method for learning procedures, a computational cognition cluster (CLU) <NUM> and a computational cognition architecture (CCA) <NUM>.

<FIG> shows a cognition method illustrating a sampling approach for cognition with steps of capturing <NUM>, cognitive function <NUM>, and inference projection <NUM>. Generally, as can be seen in <FIG>, the cognition requirements definition is based on a sampling approach, consequent with the fact that domain is connected to the Cognitive System in real-time.

Capturing <NUM> involves capturing new data. In some embodiments, capturing <NUM> involves capturing a new sample of a synchronic event. Cognitive function <NUM> involves the action of applying abstraction and inference functions. Inference projection <NUM> involves the action of projection of the inferred dimensions activity.

<FIG> shows a conceptual illustration of a fractal cognitive computing node (FCN) <NUM>, according to one embodiment of the present invention. The FCN <NUM> includes a first input <NUM>, a second input <NUM>, a third input <NUM>, a first output <NUM>, a memory <NUM> and at least one processing unit with one or more processors (not shown for simplicity of the figure).

The first input <NUM> is configured to receive a first input signal comprising a set of pairs <key, value>, the key representing an identifier and the value representing a magnitude of the first input signal. The first output <NUM> is configured to provide a first output signal comprising a set of pairs <key, value>, the key representing an identifier and the value representing a magnitude of the output signal. The second input <NUM> is configured to receive a second input signal being a first output signal of another FCN <NUM> and comprising a set of pairs <key, value>, the key representing an identifier and the value representing a magnitude of the second input signal. For example, the first and second input signals and the first output signal are received/provided in sparse-distributed representation (SDR). The first input signal can be received from another FCN <NUM> (e.g. from a children FCN) in case it is an internal FCN structure or the first input signal can be generated by an encoder sensor if the FCN is a leaf of a structure.

The third input <NUM> is configured to receive a spatial attention (SA) parameter comprising a single dimension value. In particular, the SA parameter defines the threshold which is applied in order to drive learning from new signals and the recalling of learned ones. SA parameter impacts on the level of detail at which the cognitive step takes place.

The cited memory <NUM> of the FCN <NUM> is configured to store a collection of items, where each item has a set of tuples <key, value>, and represents a previously stored input from at least one of said first or second input signals. The processing unit implements the computational function for performing the learning/recalling procedures against all previously learned patterns. To that end, the processing unit compares the first input signal, the second input signal or a combination of said first and second input signals to some (or all) of the stored collection of items (in general of a plurality of interconnected FCNs <NUM>) and calculates a similarity measure for each compared item. The processing unit generates the first output signal as the selection of the compared items having a similarity measure greater than the SA parameter.

The use of the SA parameter is to set the precision that will be applied to the similarity measure. SA parameter may be defined as a fixed value for each FCN <NUM>. If SA has a very low value (for instance <NUM>), the items created in the memory <NUM> will be selected based on this precision and, therefore, very few items will be created in the memory <NUM>, providing a very high simplification of the representational space relative to the actual domain exposure. Consequently, the inference effect or the recall of specific item in the memory <NUM> will be very relevant since the related inferred tuples of the item recalled based on an input sample are relevantly different. On the other hand, if SA parameter has very high values, a lot of items will be created in the memory <NUM>, but consequently, very poor inference will be produced since for a specific recalled item only happens when samples are very similar to the item and therefore, very few tuples will be inferred.

<FIG> shows a conceptual illustration of a FCN <NUM> according to another embodiment of the present invention. Besides the features previously described, in this case the FCN <NUM> also includes a fourth input <NUM> to receive a temporal attention (TA) parameter, a fifth input <NUM> to receive a conditioning (CD) parameter and a sixth input <NUM> to receive a focus (AF) parameter.

<FIG> shows another conceptual illustration of a FCN <NUM> according to another embodiment of the present invention. In this case, the FCN <NUM> also includes a second output <NUM> to provide a confidence (CF) parameter and a third output <NUM> to provide a coherence (CH) parameter. It should be noted that in other embodiments, in this case not illustrated it is not necessary to have all these additional inputs and outputs but only one of them.

The TA parameter, which comprises a single dimension value, defines the time range that the cited computational function is applied on. Wider ranges allow the FCN <NUM> to change the sequence length of pattern processing. When receiving this TA parameter, the processing unit can modify the value of any tuple stored in the memory <NUM> by decreasing that value by TA parameter.

TA parameter is used to specify a fixed sequencing context that will be considered in the similarity operation. If TA has a value of <NUM>, any "value" of any tuple greater than zero, will be zero in one processing step, producing strict spatial cognition. If TA has a higher value such as <NUM>, the "value" of greater than zero tuples will be zero progressively after <NUM> processing steps. The overall effect consists in a sequencing or transitional cognition effect, converting a temporal relationship into spatial relationship, since transitional items of the memory <NUM> will represent a spatial transition.

The CD parameter, which also comprises a single dimension value, either positive or negative, defines, when positive, a reinforcement level and, when negative, it defines an inhibition level. In this case, each item stored in the memory <NUM> comprises a set of tuples <key, value, depth>. The processing unit can modify the depth value of tuples for the selection of compared items by a magnitude defined as an addition or subtraction of the value of said CD parameter.

The CD parameter is considered as an accelerator or inhibitor of the depth field of the tuples of each item in the memory <NUM>. In a specific sample processing step, if CD is greater than zero, the delta increment of the depth value of the selected items in the similarity operation result will be proportional to CD parameter value. This allows applying a progressive sub-selection process of specific sub-domain, producing a valuable mechanism to short-term adaptation as cognition guidance criteria. This mechanism is especially valuable when not all the exposed samples of the domain are correlated or otherwise directly related to achieving a target task or behavior. For example, if a certain targeted output is, through a known or determined mechanism or physical phenomenon, based on some, but not all of the exposed samples in a domain, the CD can be set to a value greater than zero for the exposed samples that impact the targeted output to guide cognition. On the other hand, if CD is smaller than zero, the effect on the depth values of the selected items by the similarity operation will be temporally inhibited and therefore, they will be excluded from the current outputted signal, providing the best next alternative item selection.

The focus parameter (AF) refers to a filtering criterion. The processing unit can modify the value of first output signal taking into consideration this filtering criterion.

The AF parameter is used as a filter operating between the memory items selection from the similarity operation and the final outputted signal. This allows applying specific criteria of sub-selection of current selection, allowing a mechanism of focusable cognition. Depending on the specific AF value set, the projection of inferred signals to the dimensions of the domain will be more reactive, understood as the best similar selection of items in the memory <NUM> or more focusable or attentional, producing projections more specific on the potential total possible selection.

The CF parameter is computed as a statistical grouping function over the similarity measures of the cited selection of compared items. That is, this output parameter shows the value of confidence of recalled pattern delivered in the first output <NUM>.

The CF parameter is a measure of the recalling percentage of the similarity function produced in a specific sample processing. If only one item in the memory <NUM> is recalled, CF parameter will be equal to the similarity value result of comparing the sample signal to the item tuple composition. If more than one item is selected from the memory <NUM>, CF parameter value is set to a grouping arithmetic function over the individual similarity measures (CFs), for instance, the average of the individual CFs. Consequently, CF parameter can be understood as a measure of local recall degree.

The CH parameters shows a measure of difference between the second input signal and the first output signal, understood as a measure of success of cognitive function. Additionally to CF measure, an FCN <NUM> can provide another measure related to the quality of cognition, not locally as CF, but structurally contextually to the rest of the FCN hierarchy it belongs. CH parameter is calculated as the similarity comparison of signals in output <NUM> and input <NUM>. This calculation measures the coherence between the evidences of sampling from the environment and the expected inference or spatial transition at each FCN <NUM>. Therefore, CH parameter measures the cognitive coherence at each FCN <NUM>, providing valuable information on changes of cognitive status.

According to embodiments of the invention, the first output signal outputted via the first output <NUM> can be reduced in size by a percentage set by a redux parameter. This reduction has to be compliant with the semantic perspective, showing the same internal relationships as the original signal filtered by relevance.

<FIG> is a conceptual drawing of different FCNs <NUM> interconnected at different levels forming a hierarchical structure, according to an embodiment. In this hierarchical structure the different FCNs <NUM> learn from each other. <FIG> illustrates an example with three different levels L1-L3 of connection. Each of FCNs <NUM> will implement the computational function for performing the learning/recalling procedures based on their corresponding signals. This make FCNs <NUM> to learn/recall the most similar previously learned patterns and produce a first output signal containing the pattern recalled/learned. The FCN <NUM> at the third layer L3 receives in the first input <NUM> the combination of the two FCNs <NUM> of the second layer L2. Taking this combination as a single first input signal, the FCN <NUM> at the third layer L3 will perform the computational function, learning/recalling based on this combined signal.

As a result, this structure creates a first level of knowledge individually for each FCN <NUM> but at the same time, creates a second level of knowledge as an overall context, with the capability of inferring one to each other. In this structure, a partial stimulation of lowest level will produce that top FCN <NUM> will produce a first output signal <NUM> containing the most similar combined pattern, which will be sent to the lower FCNs <NUM> (children FCNs) via the second input <NUM>. The FCN <NUM> which is not stimulated will choose the second input signal as input signal and, therefore, will activate original individual pattern.

This advantageously achieves individual cognition for each FCN <NUM> at lowest level; associative cognition between them at top level; bottom-up and top-down flow of information in the same structure; and continuous learning operational behavior for new data as well as new predicted values.

It should be noted that as a derived structure of the principle shown in the basic structure of <FIG>, custom hierarchical structures can be also defined including <NUM>-dimensional structures (i.e. defined based on a 2D space) or any n-dimensional or natural structure (i.e. no space arrangement is known, so all possible relationships between dimensions are captured).

With reference to <FIG>, therein it is illustrated an embodiment of a computational cognition cluster (CLU) <NUM>. As a consequence of a replication operation of the FCNs <NUM>, a hierarchy is created in such a configuration where the seed FCN SA parameter is set to low values, providing low precision cognition. As the different items in the memory <NUM> have a specific new FCN <NUM> for their composition of tuples with higher SA value and lower TA values, the overall cognition is performed in more spatial and sequence context precision, producing an escalation of the cognition effect. This allows to, from the CLU <NUM> perspective, to perform invariance archetyping and inference without any precision parameter. Each CLU <NUM> is also defined as a connectable component in hierarchical structures, comprising a first input <NUM> to receive an input compositional signal of tuples from the output <NUM> of its children CLUs, an output <NUM> connected to the input <NUM> of the parent CLU <NUM>, and a second input <NUM> to receive a second signal from the output <NUM> of the parent CLU <NUM>. Additionally, a CD parameter may be defined equally to the FCN <NUM>, which value is inputted via input <NUM> from the external domain and internally connected to all the FCNs <NUM> in the CLU internal hierarchy via input <NUM>.

Hence, the CLU <NUM> may be defined as a memorization component that in an unsupervised manner creates a distributed knowledge representation with the essence of the domain and produces maximum inference complementation and increasingly to maximum levels of precision with no internal parameters.

With reference to <FIG>, therein it is illustrated an embodiment of a computational cognition architecture (CCA) <NUM>. The CCA <NUM> comprises a hierarchical structure of CLUs <NUM> that may be connected as described above. The CCA <NUM> also comprises several parameters such as an input compositional signal of tuples received via a first input <NUM>, and output <NUM> to project to the domain dimensions and a domain CD parameter defined as the same as the other substructures which is received via input <NUM>. The CCA <NUM> performs the same cognitive effects of archetyping and inference but in a more efficient way due to given sub-domain specification which reduces sub-domain complexities and takes the benefit of recurrency of cognition. The structures of CLU map the sub-domain structures of the domain, typically interpreted as domain structure of the discrete data or domain structure of a virtual o physical configuration of an embodied agent in terms of modalities or sensing/actuation dimension ranges. Hence, the CLU hierarchical structure allows defining a suitable cognition architecture to deploy a cognition-based control system of components or agents, through the domain sampling and conditioning stimulation, producing universal, real-time, spatio-temporal, scalable, adaptive and explainable unsupervised machine learning effects.

With reference to <FIG>, therein it is illustrated another embodiment of a CCA <NUM>. In this case, the CCA <NUM> includes a first input <NUM> to receive a first input signal from the interface and an output <NUM> to provide a first output signal to the interface between the domain and the architecture. In this embodiment, the CCA <NUM> is formed by a configured hierarchical structure of CLUs <NUM> connecting the first output signal of each child (provided via the first output <NUM>) to the first input <NUM> of the parent and connecting the first output <NUM> of the parent to the second input <NUM> of the children. Each CLU <NUM> may initially contain one FCN <NUM>, which will be replicated based on the items of the memory <NUM> that will be created and by connecting the first output signal of each child (provided via the first output <NUM>) to the first input <NUM> of parent and by connecting the first output <NUM> from parent to the second input <NUM> of each child. The specific architecture is defined based on known domain and sub-domains structures and it is specified through the interface.

<FIG> is a flowchart illustrating a method for learning procedures, according to an embodiment of the present invention. At step <NUM>, a first input of a computing node such as a FCN <NUM> receives a first input signal comprising a set of pairs <key, value>, the key representing an identifier and the value representing the first input signal magnitude. At step <NUM>, a second input of the computing node <NUM>, receives a second input signal comprising a set of pairs <key, value>, the second input signal being an output signal of another FCN <NUM>. At step <NUM>, a third input of the computing node <NUM> receives a SA parameter. At step <NUM> the computing node <NUM> implements a computational function for learning/recall procedures by comparing the first input signal, the second input signal or a combination of the first and second input signals with a collection of stored items and by calculating a similarity measure for each compared item. Each item represents a previously stored input from at least one of said first or second input signals, and each item at least has a set of tuples <key, value>.

At step <NUM>, a first output signal is calculated as a selection of the compared items having a similarity measure greater than a SA parameter. At step <NUM>, it is checked whether the selection of compared items is empty or not. If the selection of compared items is empty, a new item is added (step <NUM>) to the memory <NUM> and the set of tuples and the first output signal are set to the first input signal, to the second input signal or to a combination of the first and second input signals. On the contrary, if the selection of compared items is not empty, the first output signal is set (step <NUM>) to the selection.

<FIG> shows a two-dimensional system made up of FCNs <NUM>. This is an embodiment of the custom hierarchical structures for a given space that is discussed above in <FIG>.

Claim 1:
Computer system having a fractal cognitive computing node (<NUM>) configured to perform computational operations for learning procedures, the fractal cognitive computing node (<NUM>) comprising:
- a first input (<NUM>), configured to receive a first input signal from another fractal cognitive computing node or from a remote sensor or actuator, said first input signal comprising a set of pairs <key, value>, wherein the key representing an identifier and the value representing a magnitude of the first input signal;
- a first output (<NUM>), configured to provide a first output signal, said first output signal comprising a set of pairs <key, value>, wherein the key representing an identifier and the value representing a magnitude of the first output signal;
- a second input (<NUM>), configured to receive a second input signal, said second input signal being a first output signal of another fractal cognitive computing node and comprising a set of pairs <key, value> wherein the key representing an identifier and the value representing a magnitude of the second input signal;
- a third input (<NUM>), configured to receive a spatial attention parameter comprising a single dimension value;
- a memory, configured to store a collection of items, each item having a set of tuples <key, value>, and each item representing a previously stored input from at least one of said first or second input signals; and
- a processing unit, configured to implement a computational function that:
- compares the first input signal or the second input signal or a combination of said first and second input signals to some or all of the stored collection of items; and
- calculates a similarity measure for each compared item;
wherein said first output signal outputted via said first output (<NUM>) being calculated as a selection of the compared items having a similarity measure greater than said spatial attention parameter;
wherein if the selection of compared items is empty, a new item is added to said memory and the set of tuples <key, value> of the new item and the first output signal are set according to the first input signal, to the second input signal or to a combination of the first and second input signals; and
wherein if the selection of compared items is not empty, the first output signal is set to said selection.