… In conclusion, a geometric neuron can be seen as a kind of geometric correlation operator,
which, in contrast to the McCulloch–Pitts neuron, offers not only points but higher grade …

… recurrent neural networks, we … neural data to identify contingency representations in neural
data and to dissociate different models of working memory. Our findings characterize a neural …

… self-organizing neural computing in terms of geometric algebra … the geometric visual
processing in biological creatures. … process of the self-organized neural network GNG [4], to obtain …

… Unfortunately, the application of these concepts in current work on image processing, neural
computing, and robotics is still somewhat limited. Statistical physics and optimization theory …

… system of the Clifford geometric algebra. The Authors compare the McCulloch–Pitts neuron
and the geometric neuron. An instance of geometric neuron is the conformal neuron which …

… We implement the algorithms induced from this quantitative geometric approach, which …
geometric perspective can bring new insights and theoretical tools to the investigation of neural…

A method for the description of patterns is proposed that is based on the evaluation of their
inner geometric relations. They serve as features and are determined through operations that …

… , a neural network library in Torch aimed towards bridging the gap between classic geometric
computer … on the data in geometric computer vision. These layers can be inserted within a …

Connectivity patterns in biological brains exhibit many repeating motifs. This repetition
mirrors inherent geometric regularities in the physical world. For example, stimuli that excite …

… 7] can perform neural rendering for scenes with simple geometric … neural rendering in such
environments. To this end, we introduce an attention mechanism that leverages the geometric …