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Articles by Alpha Renner
The backpropagation algorithm implemented on spiking neuromorphic hardware - Nature Communications
Abstract The capabilities of natural neural systems have inspired both new generations of machine learning algorithms as well as neuromorphic, very large-scale integrated circuits capable of fast, low-power information processing. However, it has been argued that most modern machine learning algorithms are not neurophysiologically plausible. In particular, the workhorse of modern deep learning, the backpropagation algorithm, has proven difficult to translate to neuromorphic hardware.
Neuromorphic visual scene understanding with resonator networks - Nature Machine Intelligence
Abstract Analysing a visual scene by inferring the configuration of a generative model is widely considered the most flexible and generalizable approach to scene understanding. Yet, one major problem is the computational challenge of the inference procedure, involving a combinatorial search across object identities and poses.
Visual odometry with neuromorphic resonator networks - Nature Machine Intelligence
Abstract Visual odometry (VO) is a method used to estimate self-motion of a mobile robot using visual sensors. Unlike odometry based on integrating differential measurements that can accumulate errors, such as inertial sensors or wheel encoders, VO is not compromised by drift. However, image-based VO is computationally demanding, limiting its application in use cases with low-latency, low-memory and low-energy requirements.
High Precision of Spike Timing across Olfactory Receptor Neurons Allows Rapid Odor Coding in Drosophila
To determine the speed and temporal precision of odorant-evoked spikes in olfactory receptor neurons, we recorded from pairs of Drosophila olfactory receptor neurons that express the same olfactory receptor (neurons expressing the receptor OR59b and neurons expressing OR22a, Figure 1 A; original data are available in Data S1 ).
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