- Related item:
- Neural Network Model Develops Border Ownership Representation through Visually Guided Learning
- Description:
-
As Rubin’s famous vase demonstrates, our visual perception tends to assign luminance contrast borders to one or other of the adjacent image regions. Experimental evidence for the neuronal coding of such border-ownership in the primate visual system has been reported in neurophysiology. We have investigated exactly how such neural circuits may develop through visually-guided learning. More specifically, we have investigated through computer simulation how top-down connections may play a fundamental role in the development of border ownership representations in the early cortical visual layers V1/V2. Our model consists of a hierarchy of competitive neuronal layers, with both bottom-up and top-down synaptic connections between successive layers, and the synaptic connections are self-organised by a biologically plausible, temporal trace learning rule during training on differently shaped visual objects. The simulations reported in this paper have demonstrated that top-down connections may help to guide competitive learning in lower layers, thus driving the formation of lower level (border ownership) visual representations in V1/V2 that are modulated by higher level (object boundary element) representations in V4. Lastly we investigate the limitations of our model in the more general situation where multiple objects are presented to the network simultaneously.
- Related item:
- Understanding the Neural Basis of Cognitive Bias Modification as a Clinical Treatment for Depression.
- Description:
-
Objective: Cognitive bias modification (CBM) eliminates cognitive biases toward negative information and is efficacious in reducing depression recurrence, but the mechanisms behind the bias elimination are not fully understood. The present study investigated, through computer simulation of neural network models, the neural dynamics underlying the use of CBM in eliminating the negative biases in the way that depressed patients evaluate facial expressions. Method: We investigated 2 new CBM methodologies using biologically plausible synaptic learning mechanisms—continuous transformation learning and trace learning—which guide learning by exploiting either the spatial or temporal continuity between visual stimuli presented during training. We first describe simulations with a simplified 1-layer neural network, and then we describe simulations in a biologically detailed multilayer neural network model of the ventral visual pathway. Results: After training with either the continuous transformation learning rule or the trace learning rule, the 1-layer neural network eliminated biases in interpreting neutral stimuli as sad. The multilayer neural network trained with realistic face stimuli was also shown to be able to use continuous transformation learning or trace learning to reduce biases in the interpretation of neutral stimuli. Conclusions: The simulation results suggest 2 biologically plausible synaptic learning mechanisms, continuous transformation learning and trace learning, that may subserve CBM. The results are highly informative for the development of experimental protocols to produce optimal CBM training methodologies with human participants. (PsycINFO Database Record (c) 2016 APA, all rights reserved)
- Related item:
- The visually guided development of facial representations in the primate ventral visual pathway: A computer modeling study
- Description:
-
Experimental studies have shown that neurons at an intermediate stage of the primate ventral visual pathway, occipital face area, encode individual facial parts such as eyes and nose while neurons in the later stages, middle face patches, are selective to the full face by encoding the spatial relations between facial features. We have performed a computer modeling study to investigate how these cell firing properties may develop through unsupervised visually guided learning. A hierarchical neural network model of the primate’s ventral visual pathway is trained by presenting many randomly generated faces to the network while a local learning rule modifies the strengths of the synaptic connections between neurons in successive layers. After training, the model is found to have developed the experimentally observed cell firing properties. In particular, we have shown how the visual system forms separate representations of facial features such as the eyes, nose, and mouth as well as monotonically tuned representations of the spatial relationships between these facial features. We also demonstrated how the primate brain learns to represent facial expression independently of facial identity. Furthermore, based on the simulation results, we propose that neurons encoding different global attributes simply represent different spatial relationships between local features with monotonic tuning curves or particular combinations of these spatial relations.
- Related item:
- Computational Modelling of the Neural Representation of Object Shape in the Primate Ventral Visual System
- Description:
-
Neurons in successive stages of the primate ventral visual pathway encode the spatial structure of visual objects. In this paper, we investigate through computer simulation how these cell firing properties may develop through unsupervised visually-guided learning. Individual neurons in the model are shown to exploit statistical regularity and temporal continuity of the visual inputs during training to learn firing properties that are similar to neurons in V4 and TEO. Neurons in V4 encode the conformation of boundary contour elements at a particular position within an object regardless of the location of the object on the retina, while neurons in TEO integrate information from multiple boundary contour elements. This representation goes beyond mere object recognition, in which neurons simply respond to the presence of a whole object, but provides an essential foundation from which the brain is subsequently able to recognise the whole object.
