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.

The mammalian visual system has been the focus of countless experimental and theoretical studies designed to elucidate principles of 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 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. IntroductionDespite affecting approximately 100% of the human population, healthy aging, and in particular its impact on neurological performance, has been the focus of few theoretical and computational studies in neuroscience, 1 compared to diseases such as Alzheimer's [1][2][3][4][5][6][7][8][9][10][11][12] or , which often manifest in advanced age in humans [21]. Only recently has theoretical and computational work on aging in non-pathological networks begun to emerge [22]. Because advanced age is one of the most important risk factors for developing such neurological disorders [21,23], to fully understand the progression of these diseases we ought to have a baseline understanding of how the brain's circuitry changes under 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 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.In this work we seek to advance our understanding of potential mechanisms and consequences of ageinduced changes in physiology and performance in visual cortex. We do so by adapting a previouslydeveloped spiking network model of V1 activity, E-I Net [24]. The network structure of E-I Net is learned by training it on pixel images of natural scenes, to which it develops Gabor-like receptive fields. The synaptic and homeostatic learning rules are local, and therefore bio...