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Classically, vision is seen as a cascade of local, feedforward computations. This framework has been tremendously successful, inspiring a wide range of ground-breaking findings in neuroscience and computer vision. Recently, feedforward Convolutional Neural ...
Neural interfaces capable of multi-site electrical recording, on-site signal classification, and closed-loop therapy are critical for the diagnosis and treatment of neurological disorders. However, deploying machine learning algorithms on low-power neural ...
Simulations of the electrical activity of networks of morphologically-detailed neuron models allow for a better understanding of the brain.
Short time to solution is critical in order to study long biological processes such as synaptic plasticity and learn ...
The macroscopic dynamics of large populations of neurons can be mathematically analyzed using low-dimensional firing-rate or neural-mass models. However, these models fail to capture spike synchronization effects and nonstationary responses of the populati ...
The reduction of aversive emotions by a conspecific’s presence—called social buffering—is a universal phenomenon in the mammalian world and a powerful form of human social emotion regulation. Animal and human studies on neural pathways underlying social bu ...
Simulations of electrical activity of networks of morphologically detailed neuron models allow for a better understanding of the brain. State-of-the-art simulations describe the dynamics of ionic currents and biochemical processes within branching topologi ...
Realistic rendering using discrete reflectance measurements is challenging, because arbitrary directions on the light and view hemispheres are queried at render time, incurring large memory requirements and the need for interpolation. This explains the des ...
The way in which cortical microcircuit components -- most importantly neurons -- and their connectivity -- the network -- shape and constrain emergent dynamics is a long-standing question in neuroscience. Experimentally observed dynamical properties can of ...
The existing literature on knowledge graph completion mostly focuses on the link prediction task. However, knowledge graphs have an additional incompleteness problem: their nodes possess numerical attributes, whose values are often missing. Our approach, d ...
Exposing parallelism in scientific applications has become a core requirement for efficiently running on modern distributed multicore SIMD compute architectures. The granularity of parallelism that can be attained is a key determinant for the achievable ac ...