Abstract
The mammalian visual system has been the focus of countless experimental and theoretical studies designed to elucidate principles of neural computation and sensory coding. Most theoretical work has focused on networks intended to reflect developing or mature neural circuitry, in both health and disease. Few computational studies have attempted to model changes that occur in neural circuitry as an organism ages non-pathologically. In this work we contribute to closing this gap, studying how physiological changes correlated with advanced age impact the computational performance of a spiking network model of primary visual cortex (V1). Our results demonstrate that deterioration of homeostatic regulation of excitatory firing, coupled with long-term synaptic plasticity, is a sufficient mechanism to reproduce features of observed physiological and functional changes in neural activity data, specifically declines in inhibition and in selectivity to oriented stimuli. This suggests a potential causality between dysregulation of neuron firing and age-induced changes in brain physiology and functional performance. While this does not rule out deeper underlying causes or other mechanisms that could give rise to these changes, our approach opens new avenues for exploring these underlying mechanisms in greater depth and making predictions for future experiments.
Highlights
While healthy aging, and in particular its impact on neurological performance, has been the focus of numerous experimental studies [1,2,3,4,5,6,7,8], there are comparatively few theoretical and computational studies that focus on normal aging
In this work we propose a computational model of an aging-like process in primary visual cortex that reproduces several experimentally-observed changes in senescent cats
Our model predicts that an age-induced increase in excitatory neural activity amid ongoing synaptic plasticity and homeostatic regulation leads to decreased strengths of synaptic connections and deterioration of neural receptive fields, which in turn lead to decreased network sensitivity to oriented features in visual stimuli
Summary
In particular its impact on neurological performance, has been the focus of numerous experimental studies [1,2,3,4,5,6,7,8], there are comparatively few theoretical and computational studies that focus on normal aging. Because advanced age is one of the most important risk factors for developing such neurological disorders [9, 10, 33], to fully understand the progression of these diseases we ought to have a baseline understanding of how the brain’s circuitry changes during healthy aging, both in terms of physiological properties and functional performance. This would help dissociate disease-related changes from those caused during normal aging, and thereby allow researchers to focus their attention on treating potential causes of the disease progression not directly related to normal aging. On the other hand, understanding how the healthy brain ages may enable us to treat declines in performance caused solely by aging, in both healthy subjects and those with neurological disorders or diseases
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