AMatterRequirements
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aMatter Requirements
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Contents:
Contents
Overall Features
Mocked Functions
- M-01. aMatter has primitive predefined set of effectors producing representation in external world based on predefined set of low-level commands provided by hardcoded motor strategies
- M-02. aMatter limits effectors actions to ones explaining internal representations by means of hardcoded symbolical language
- M-03. aMatter has primitive predefined hierarchy of behavioural strategies, on leaf level directly connected with effectors commands
Cognition
- C-01. aMatter receives information using predefined set of sensors
- C-02. aMatter recognises received information in real-time mode and calculates recognition metric R reflecting percentage of successfully recognised sensors inputs in given environment
- C-03. aMatter generalises unrecognised inputs so that R monotonously increases for the same static environment
- C-04. aMatter forms growing set of internal entities, so that specific subset of internal entities, when being in active state, can be treated as a representation of specific external data from sensors, disregarding whether previously perceived or internally inspired
- C-05. aMatter is able to forget internal entities, if not activated for a long time, so that the same input triggers another set of internal entities after a while
- C-06. aMatter forms growing set of associations between internal entities activated about the same time
Feeling
- F-01. aMatter collects information from predefined set of embodiment signals equivalent in purpose with human being, with body treated as related operating system process with all its inherent features and properties
- F-02. aMatter collects predefined set of uncertainty metrics from expectation flow of behavioural strategies
Goals Achievement
Perception/Self-Learning features
Perception
- Effective Signal Processing
- MindSensorArea - receptive field averaging and inhibition
- ThalamusArea - create relay sensory nuclei - done
- ThalamusArea - create inhibitory NeuroPool - done
- Connect PerceptionArea feedback and inhibitory NeuroPool - done
- Connect internal InhibitoryLink from relay NeuroPool and inhibitory NeuroPool - done
- Implement realistic inhibitory properties (150ms inhibition vs 20 ms excitatory non-firing interval)
- Functional value
- implement sampling
- implement subsampling
Self-Learning
- Mock cortex implementation - to allow development of other components
- create feed-forward NeuroPool - done
- create spatial pooler - process feed-forward signal from ThalamusArea - done
- create temporal pooler - process fixed-size sequences and derive predicted spatial pooler item - done
- create feedback NeuroPool - done
- apply temporal pooler prediction to feedback NeuroPool and generate cortex feedback signal - done
- Cortex implementation
- Columnar processing
- Hierarchical Processing
- Infinite Temporal Prediciton
- Focus Processing
- Attention Processing
- Event Driven Implementation
- Functional value
- implement high-probability predictive sampling and subsampling
Cognition as Meaningful Sensor Control
- saccadic scene scanning, spacial into temporal-spatial approach
- novelty, motavation, attention
- virtuality
Demonstrate Feeling Feature
- TBD
Real-Time Neural Networks
- Neural Structures
- NeuroPool - accumulate arriving action potentials in membrane potential - done
- NeuroPool - time-based dissolving of membrane potential - done
- NeuroPool - postpone firing to minimum time interval after last firing - done
- Signal Processing
- ExcitatoryLink - project excitatory signal to NeuroPool - done
- ExcitatoryLink - generate excited signal from projection - done
- NeuroSignal - store only activated source items - done
