Darjan Salaj

• Master of Science
• inovex GmbH

For solving tasks such as recognizing a song, answering a question, or inverting a sequence of symbols, cortical microcircuits need to integrate and manipulate information that was dispersed over time during the preceding seconds. Creating biologically realistic models for the underlying computations, especially with spiking neurons and for behavio...

Existing security and surveillance systems have been one of the main factors behind the current trend of privacy decline. State-of-the-art AI methods have allowed security systems to scale up much faster without improving privacy. We identify data transfers from surveillance system deployment sites to the cloud as the main reason for the lack of pr...

Recurrently connected networks of spiking neurons underlie the astounding information processing capabilities of the brain. Yet in spite of extensive research, how they can learn through synaptic plasticity to carry out complex network computations remains unclear. We argue that two pieces of this puzzle were provided by experimental data from neur...

Brains are able to integrate memory from the recent past into their current computations, seemingly without effort. This ability is critical for cognitive tasks such as speech understanding or working with sequences of symbols according to dynamically changing rules. But it has remained unknown how networks of spiking neurons in the brain can achie...

Recurrently connected networks of spiking neurons underlie the astounding information processing capabilities of the brain. But in spite of extensive research, it has remained open how learning through synaptic plasticity could be organized in such networks. We argue that two pieces of this puzzle were provided by experimental data from neuroscienc...

The way how recurrently connected networks of spiking neurons in the brain acquire powerful information processing capabilities through learning has remained a mystery. This lack of understanding is linked to a lack of learning algorithms for recurrent networks of spiking neurons (RSNNs) that are both functionally powerful and can be implemented by...

Networks of spiking neurons (SNNs) are frequently studied as models for networks of neurons in the brain, but also as paradigm for novel energy efficient computing hardware. In principle they are especially suitable for computations in the temporal domain, such as speech processing, because their computations are carried out via events in time and...