Nonlinear Hebbian learning as a unifying principle in receptive field formation

The development of sensory receptive fields has been modeled in the past by a variety of models including normative models such as sparse coding or independent component analysis and bottom-up models such as spike-timing dependent plasticity or the Bienenstock-Cooper-Munro model of synaptic plasticity. Here we show that the above variety of approaches can all be unified into a single common principle, namely Nonlinear Hebbian Learning. When Nonlinear Hebbian Learning is applied to natural images, receptive field shapes were strongly constrained by the input statistics and preprocessing, but exhibited only modest variation across different choices of nonlinearities in neuron models or synaptic plasticity rules. Neither overcompleteness nor sparse network activity are necessary for the development of localized receptive fields. The analysis of alternative sensory modalities such as auditory models or V2 development lead to the same conclusions. In all examples, receptive fields can be predicted a priori by reformulating an abstract model as nonlinear Hebbian learning. Thus nonlinear Hebbian learning and natural statistics can account for many aspects of receptive field formation across models and sensory modalities

Predictive coding: A Possible Explanation of Filling-in at the blind spot

Filling-in at the blind-spot is a perceptual phenomenon in which the visual system fills the informational void, which arises due to the absence of retinal input corresponding to the optic disc, with surrounding visual attributes. Though there are enough evidence to conclude that some kind of neural computation is involved in filling-in at the blind spot especially in the early visual cortex, the knowledge of the actual computational mechanism is far from complete. We have investigated the bar experiments and the associated filling-in phenomenon in the light of the hierarchical predictive coding framework, where the blind-spot was represented by the absence of early feed-forward connection. We recorded the responses of predictive estimator neurons at the blind-spot region in the V1 area of our three level (LGN-V1-V2) model network. These responses are in agreement with the results of earlier physiological studies and using the generative model we also showed that these response profiles indeed represent the filling-in completion. These demonstrate that predictive coding framework could account for the filling-in phenomena observed in several psychophysical and physiological experiments involving bar stimuli. These results suggest that the filling-in could naturally arise from the computational principle of hierarchical predictive coding (HPC) of natural images.Comment: 23 pages, 9 figure

Sparse visual models for biologically inspired sensorimotor control

Given the importance of using resources efficiently in the competition for survival, it is reasonable to think that natural evolution has discovered efficient cortical coding strategies for representing natural visual information. Sparse representations have intrinsic advantages in terms of fault-tolerance and low-power consumption potential, and can therefore be attractive for robot sensorimotor control with powerful dispositions for decision-making. Inspired by the mammalian brain and its visual ventral pathway, we present in this paper a hierarchical sparse coding network architecture that extracts visual features for use in sensorimotor control. Testing with natural images demonstrates that this sparse coding facilitates processing and learning in subsequent layers. Previous studies have shown how the responses of complex cells could be sparsely represented by a higher-order neural layer. Here we extend sparse coding in each network layer, showing that detailed modeling of earlier stages in the visual pathway enhances the characteristics of the receptive fields developed in subsequent stages. The yield network is more dynamic with richer and more biologically plausible input and output representation