The Morphology and Intrinsic Excitability of Developing Mouse Retinal Ganglion Cells

The retinal ganglion cells (RGCs) have diverse morphology and physiology. Although some studies show that correlations between morphological properties and physiological properties exist in cat RGCs, these properties are much less distinct and their correlations are unknown in mouse RGCs. In this study, using three-dimensional digital neuron reconstruction, we systematically analyzed twelve morphological parameters of mouse RGCs as they developed in the first four postnatal weeks. The development of these parameters fell into three different patterns and suggested that contact from bipolar cells and eye opening might play important roles in RGC morphological development. Although there has been a general impression that the morphological parameters are not independent, such as RGCs with larger dendritic fields usually have longer but sparser dendrites, there was not systematic study and statistical analysis proving it. We used Pearson's correlation coefficients to determine the relationship among these morphological parameters and demonstrated that many morphological parameters showed high statistical correlation. In the same cells we also measured seven physiological parameters using whole-cell patch-clamp recording, focusing on intrinsic excitability. We previously reported the increase in intrinsic excitability in mouse RGCs during early postnatal development. Here we showed that strong correlations also existed among many physiological parameters that measure the intrinsic excitability. However, Pearson's correlation coefficient revealed very limited correlation across morphological and physiological parameters. In addition, principle component analysis failed to separate RGCs into clusters using combined morphological and physiological parameters. Therefore, despite strong correlations within the morphological parameters and within the physiological parameters, postnatal mouse RGCs had only limited correlation between morphology and physiology. This may be due to developmental immaturity, or to selection of parameters

Y-Like Retinal Ganglion Cells Innervate the Dorsal Raphe Nucleus in the Mongolian Gerbil (Meriones unguiculatus)

Background: The dorsal raphe nucleus (DRN) of the mesencephalon is a complex multi-functional and multi-transmitter nucleus involved in a wide range of behavioral and physiological processes. The DRN receives a direct input from the retina. However little is known regarding the type of retinal ganglion cell (RGC) that innervates the DRN. We examined morphological characteristics and physiological properties of these DRN projecting ganglion cells. Methodology/Principal Findings: The Mongolian gerbils are highly visual rodents with a diurnal/crepuscular activity rhythm. It has been widely used as experimental animals of various studies including seasonal affective disorders and depression. Young adult gerbils were used in the present study. DRN-projecting RGCs were identified following retrograde tracer injection into the DRN, characterized physiologically by extracellular recording and morphologically after intracellular filling. The result shows that DRN-projecting RGCs exhibit morphological characteristics typical of alpha RGCs and physiological response properties of Y-cells. Melanopsin was not detected in these RGCs and they show no evidence of intrinsic photosensitivity. Conclusions/Significance: These findings suggest that RGCs with alpha-like morphology and Y-like physiology appear to perform a non-imaging forming function and thus may participate in the modulation of DRN activity which includes regulation of sleep and mood

On the Origin of the Functional Architecture of the Cortex

The basic structure of receptive fields and functional maps in primary visual cortex is established without exposure to normal sensory experience and before the onset of the critical period. How the brain wires these circuits in the early stages of development remains unknown. Possible explanations include activity-dependent mechanisms driven by spontaneous activity in the retina and thalamus, and molecular guidance orchestrating thalamo-cortical connections on a fine spatial scale. Here I propose an alternative hypothesis: the blueprint for receptive fields, feature maps, and their inter-relationships may reside in the layout of the retinal ganglion cell mosaics along with a simple statistical connectivity scheme dictating the wiring between thalamus and cortex. The model is shown to account for a number of experimental findings, including the relationship between retinotopy, orientation maps, spatial frequency maps and cytochrome oxidase patches. The theory's simplicity, explanatory and predictive power makes it a serious candidate for the origin of the functional architecture of primary visual cortex

From Computer Metaphor to Computational Modeling: The Evolution of Computationalism

In this paper, I argue that computationalism is a progressive research tradition. Its metaphysical assumptions are that nervous systems are computational, and that information processing is necessary for cognition to occur. First, the primary reasons why information processing should explain cognition are reviewed. Then I argue that early formulations of these reasons are outdated. However, by relying on the mechanistic account of physical computation, they can be recast in a compelling way. Next, I contrast two computational models of working memory to show how modeling has progressed over the years. The methodological assumptions of new modeling work are best understood in the mechanistic framework, which is evidenced by the way in which models are empirically validated. Moreover, the methodological and theoretical progress in computational neuroscience vindicates the new mechanistic approach to explanation, which, at the same time, justifies the best practices of computational modeling. Overall, computational modeling is deservedly successful in cognitive (neuro)science. Its successes are related to deep conceptual connections between cognition and computation. Computationalism is not only here to stay, it becomes stronger every year