Glossary
This section aims to provide concise definitions of terms commonly used at Numenta and in the Monty project.
Dendrites
Implement pattern recognizers to identify patterns such as a specific SDR. One neuron is typically associated with multiple dendrites such that it can identify multiple patterns. In biology, dendrites of a postsynaptic cell receive information from the axons of other presynaptic cells. The axons of these presynaptic cells connect to the dendrites of postsynaptic cells at a junction called a "synapse". An SDR can be thought of as a pattern which is represented by a set of synapses that are collocated on a single dendritic segment.
Efference Copy
A copy of the motor command that was output by the policy and sent to the actuators. This copy can be used by learning modules to update their states or make predictions.
Environment
Depending on the environments' state and agents' actions and sensors, the environment returns an observation for each sensor.
Features
Characteristics that can be sensed at a specific location. Features may vary depending on the sensory modality (for example, color in vision but not in touch).
Graph
A set of nodes that are connected to each other with edges. Both nodes and edges can have features associated with them. For instance all graphs used in the Monty project have a location associated with each node and a variable list of features. An edge can, for example, have a displacement associated with it.
Inductive bias
An assumption that is built into an algorithm/model. If the assumption holds, this can make the model a lot more efficient than without the inductive bias. However, it will cause problems when the assumption does not hold.
Learning Module
A computational unit that takes features at poses as input and uses this information to learn models of the world. It is also able to recognize objects and their poses from the input if an object has been learned already.
Model
In Monty, a model (sometimes referred to as Object Model), is a representation of an object stored entirely within the boundaries of a learning module. The notion of a model in Monty differs from the concept of a deep learning neural network model in several ways:
- A single learning module stores multiple object models in memory, simultaneously.
- The Monty system may have multiple models of the same object if there are multiple learning modules - this is a desired behavior.
- Learning modules update models independently of each other.
- Models are structured using reference frames, not just a bag of features.
- Models represent complete objects, not just parts of objects. These objects can still become subcomponents of compositional objects but are also objects themselves (like the light bulb in a lamp).
A useful analogy is to think of Monty models as CAD representations of objects that exist within the confines of a learning module.
Also see Do Cortical Columns in the Brain Really Model Whole Objects Like a Coffee Mug in V1?
Path Integration
Updating an agent's location by using its own movement and features in the environment.
Policy
Defines the function used to select actions. Selected actions can be dependent on a model's internal state and on external inputs.
Pose
An object's location and orientation (in a given reference frame). The location can for example be x, y, z coordinates and the orientation can be represented as a quaternion, Euler angles or rotation matrix.
displacement: The spatial difference between two locations. In 3D space, this would be a 3D vector.
Reference Frame
A specific coordinate system within which locations and rotations can be represented. For instance, a location may be represented relative to the body (body/ego-centric reference frame) or relative to some point in the world (world/allo-centric reference frame) or relative to an object's center (object-centric reference frame).
Rigid Body Transformation
Applies a displacement/translation and a rotation to a set of points. Every point is transformed in the same way such that the overall shape stays the same (i.e. the relative distance between points is fixed).
Sensor Module
A computational unit that turns raw sensory input into the cortical messaging protocol. The structure of the output of a sensor module is independent of the sensory modality and represents a list of features at a pose.
Sensorimotor/Embodied
Learning or inference through interaction with an environment using a closed loop between action and perception. This means, observations depend on actions and in turn the choice of these actions depend on the observations.
Sparse Distributed Representation (SDR)
A binary vector with significantly more 0 bits than 1 bits. Significant overlap between the bit assignments in different SDRs captures similarity in representational space (e.g. similar features).
Transformation
Applies a displacement/translation and a rotation to a point.
Voting
Multiple computational units share information about their current state with each other. This can for instance be their current estimate of an object's ID or pose. This information is then used to update each unit's internal state until all units reach a consensus.
Updated 1 day ago