Animals have evolved to rapidly detect and recognize brief and intermittent encounters with
odor packages, exhibiting recognition capabilities within milliseconds. Artificial olfaction has …

In this study, we present a highly configurable neuromorphic computing substrate and use it
for emulating several types of neural networks. At the heart of this system lies a mixed-signal …

The search strategy for the memory trace and its semantics is exemplified for the case of
olfactory learning in the honeybee brain. The logic of associative learning is used to guide …

Animals face highly complex and dynamic olfactory stimuli in their natural environments,
which require fast and reliable olfactory processing. Parallel processing is a common …

Computational neuroscience has uncovered a number of computational principles used by
nervous systems. At the same time, neuromorphic hardware has matured to a state where …

In insects, odorant receptors facilitate olfactory communication and require the functionality
of the highly conserved co-receptor gene orco. Genome editing studies in a few species of …

In their natural environment, animals face complex and highly dynamic olfactory input. Thus
vertebrates as well as invertebrates require fast and reliable processing of olfactory …

How complex is the memory structure that honeybees use to navigate? Recently, an insect-
inspired parsimonious spiking neural network model was proposed that enabled simulated …

In insects, olfactory information received by peripheral olfactory receptor neurons (ORNs) is
conveyed from the antennal lobes (ALs) to higher brain regions by olfactory projection …

In order to understand how olfactory stimuli are encoded and processed in the brain, it is
important to build a computational model for olfactory receptor neurons (ORNs). Here, we …