Mechanisms of Feature Selectivity and Invariance in Primary Visual Cortex

Visual object identification requires both selectivity for specific visual features that are important to the object’s identity and invariance to feature manipulations. For example, a hand can be shifted in position, rotated, or contracted but still be recognized as a hand. How are the competing requirements of selectivity and invariance built into the early stages of visual processing? Typically, cells in the primary visual cortex are classified as either simple or complex. They both show selectivity for edge-orientation but complex cells develop invariance to edge position within the receptive field (spatial phase). Using a data-driven model that extracts the spatial structures and nonlinearities associated with neuronal computation, we quantitatively describe the balance between selectivity and invariance in complex cells. Phase invariance is frequently partial, while invariance to orientation and spatial frequency are more extensive than expected. The invariance arises due to two independent factors: (1) the structure and number of filters and (2) the form of nonlinearities that act upon the filter outputs. Both vary more than previously considered, so primary visual cortex forms an elaborate set of generic feature sensitivities, providing the foundation for more sophisticated object processing.

Mechanisms of Feature Selectivity and Invariance in Primary Visual Cortex

Visual object identification requires both selectivity for specific visual features that are important to the object’s identity and invariance to feature manipulations. For example, a hand can be shifted in position, rotated, or contracted but still be recognised as a hand. How are the competing requirements of selectivity and invariance built into the early stages of visual processing? Typically, cells in the primary visual cortex are classified as either simple or complex. They both show selectivity for edge-orientation but complex cells develop invariance to edge position within the receptive field (spatial phase). Using a data-driven model that extracts the spatial structures and nonlinearities associated with neuronal computation, we show that the balance between selectivity and invariance in complex cells is more diverse than thought. Phase invariance is frequently partial, thus retaining sensitivity to brightness polarity, while invariance to orientation and spatial frequency are more extensive than expected. The invariance arises due to two independent factors: (1) the structure and number of filters and (2) the form of nonlinearities that act upon the filter outputs. Both vary more than previously considered, so primary visual cortex forms an elaborate set of generic feature sensitivities, providing the foundation for more sophisticated object processing.

A Cognitive Architecture for Visual Memory Identification

Memory is an important process of human behavior. In particular visual memory encode, store, and retrieve acquired knowledge about the environment. The visual memory system involves different kinds of processes, such as sensory input and short-term visual memory. The model presents a first approach for visual memory recognition that supports the three stages mentioned above. The model design is based on neuroscience results. The model consists of nodes. Each node represents a brain area that is involved in the visual memory system. The nodes run in a distributed system and send messages with visual memory information. This document presents only the memory system specifications that support a cognitive architecture for visual object identification. The authors validated the model with two case studies: known and unknown stimulus.

Brain Architecture for Visual Object Identification

Visual memory identification is a key cognitive process for intelligent virtual agents living on virtual environments. This process allows the virtual agents to develop an internal representation of the environment for the posterior production of intelligent responses. There are many architectures based on memory modules for environment visual elements identification, as if they were invariant, this way of processing a visual scene is different from the one that real humans use. This document presents the description of a visual memory identification model based on current neuroscience state of art. Furthermore; the proposed model considers memory as a system that treats information in three stages: to encode, store and retrieve acquired knowledge about the environment. On the other hand, the authors validate the implementation of their approach with two identification tasks: when the stimulus is known and when it is unknown. Actually, this work is part of a proposal for a cognitive architecture that will let the authors create virtual agents with more credible human behaviors.