Abstract
Correlated, spontaneous neural activity is known to play a necessary role in visual development, but the higher-order statistical structure of these coherent, amorphous patterns has only begun to emerge in the past decade. Several computational studies have demonstrated how this endogenous activity can be used to train a developing visual system. Models that generate spontaneous activity analogous to retinal waves have shown that these waves can serve as stimuli for efficient coding models of V1. This general strategy in development has one clear advantage: The same learning algorithm can be used both before and after eye-opening. This same insight can be applied to understanding LGN/V1 spontaneous activity. Although lateral geniculate nucleus (LGN) activity has been less discussed in the literature than retinal waves, here we argue that the waves found in the LGN have a number of properties that fill the role of a training pattern. We make the case that the role of “innate learning” with spontaneous activity is not only possible, but likely in later stages of visual development, and worth pursuing further using an efficient coding paradigm.
Highlights
Lateral geniculate nucleus (LGN) activity has been less discussed in the literature than retinal waves, here we argue that the waves found in the lateral geniculate nucleus (LGN) have a number of properties that fill the role of a training pattern
The results showed this abstract, physiological spontaneous activity is capable of forming V1 receptive fields in the same way as an efficient coding of natural scenes
The same efficient coding strategy learning both on endogenous activity and external, natural stimuli may help explain many of the statistical properties of spontaneous activity deeper than retinal waves
Summary
Drawing comparisons between experimentally recorded receptive fields and the resulting filters from spontaneous neural activity. The three parameters of the model were directly related to known parameters in retinal wave physiology (fraction of recruitable amacrine cells, dendritic field sizes, and threshold number of neighboring amacrine cells needed to fire) The results showed this abstract, physiological spontaneous activity is capable of forming V1 receptive fields in the same way as an efficient coding of natural scenes. The same efficient coding strategy learning both on endogenous activity and external, natural stimuli may help explain many of the statistical properties of spontaneous activity deeper than retinal waves This has the potential to build bridges between development and experience-based learning in areas that are often treated separately. According to (Albert et al, 2008), a simple model of spontaneous activity can produce receptive fields with adult-like receptive field properties This has been done using the efficient coding strategy, mainly ICA applied on images of natural spontaneous activity patterns. The goal is to answer the same question that is naturally posed when one is first exposed to the highly structured, spontaneous patterns, “What is the purpose of this activity?”
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