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Alzheimer's disease (AD) is the most prevalent dementia in the world. Its cause(s) are presently largely unknown. The most common explanation for AD, now, is the amyloid cascade hypothesis, which states that the cause of AD is senile plaque formation by the Amyloid β peptide, and the formation of neurofibrillary tangles by hyperphosphorylated tau. A second, burgeoning theory by which to explain AD is based on the infection hypothesis. Much experimental and epidemiologic data support an involvement of infections in the development of dementia. According to this mechanism, the infection either directly, or via microbial virulence factors, precedes the formation of Amyloid β plaques. The amyloid β peptide, possessing antimicrobial properties, is beneficial at an early stage of AD, but becomes detrimental with the progression of the disease, concomitantly with alterations to the innate immune system at both the peripheral and central levels. Infection results in neuroinflammation, leading to and sustained by systemic inflammation, causing eventual neurodegeneration, and the senescence of the immune cells. The sources of AD-involved microbes are various body microbiome communities from the gut, mouth, nose, and skin. The infection hypothesis opens a vista to new therapeutic approaches, either by treating the infection itself, or modulating the immune system, its senescence, or the body's metabolism, either separately, in parallel, or in a multi-step way. Key points:1. Experimental and epidemiologic data support an involvement of infections in the development of Alzheimer's Disease (AD) and the sources of AD-involved microbes are various body microbiome communities from the gut, mouth, nose, and skin; 2. The amyloid β peptide, possessing antimicrobial properties, is beneficial at an early stage of AD, but becomes detrimental with the progression of the disease; 3. Infection results in neuroinflammation, leading to and sustained by systemic inflammation, causing neurodegeneration, and the senescence of the immune cells preceding the clinical manifestations; 4. The infection hypothesis and the antimicrobial protection hypothesis of AD opens the way to new therapeutic approaches.

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In the brain, spiking patterns live in a high-dimensional space of neurons and time. Thus, determining the intrinsic structure of this space presents a theoretical and experimental challenge. To address this challenge, we introduce a new framework for applying topological data analysis (TDA) to spike train data and use it to determine the geometry of spiking patterns in the visual cortex. Key to our approach is a parametrized family of distances based on the timing of spikes that quantifies the dissimilarity between neuronal responses. We applied TDA to visually driven single-unit and multiple single-unit spiking activity in macaque V1 and V2. TDA across timescales reveals a common geometry for spiking patterns in V1 and V2 which, among simple models, is most similar to that of a low-dimensional space endowed with Euclidean or hyperbolic geometry with modest curvature. Remarkably, the inferred geometry depends on timescale and is clearest for the timescales that are important for encoding contrast, orientation and spatial correlations.

In the brain, spiking patterns live in a high-dimensional space of neurons and time. Thus, determining the intrinsic structure of this state space for neural activity presents a theoretical and experimental challenge. To address this challenge, we introduce a new framework for applying topological data analysis (TDA) to spike train data to determine the geometry of neural activity space. Key to our approach is a parametrized family of distances based on the timing of spikes to determine the dissimilarity between neuronal responses. We applied TDA to visually driven single-unit and multiple single-unit spiking activity in macaque V1 and V2. TDA across timescales reveals a common geometry in V1 and V2 that is most consistent with a low-dimensional space endowed with Euclidean or hyperbolic geometry with modest curvature. Remarkably, the inferred geometry depends on timescale, and is most distinct for the timescales that are important for encoding contrast, orientation, and spatial correlations.

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