Projects per year
Retriggerable and non-retriggerable monostable multivibrators are simple timers with a single characteristic, their period. Motivated by the fact that monostable multivibrators are implementable in large quantities as counters in digital programmable hardware, we set out to investigate their applicability as building blocks of artificial neural networks. Wederive the nonlinear input-output firing rate relations for single multivibrator neurons as well as the equilibrium firing rate of large recurrent networks. We show that in rate-encoded monostable multivibrators networks the synaptic weights are tunable as the period ratio of connected units, and thus reconfigurable at run time in a counter-based digital implementation. This is illustrated with the task of handwritten digit recognition. Furthermore, we show in a taskindependent manner that networks of monostable multivibrators are capable of nonlinear separation, when operating directly on pulse streams. Our research implies that pulse-coupled neural networks with excitable neurons showing a delayed response can perform computations even when working solely with suprathreshold pulses.
- Network dynamics
- Neuron model
- Pulse coupled
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FWOAL863: Development and security of high-speed key distribution using chaos synchronisation of photonics sources
1/01/18 → 31/12/21
SRP31: SRP (Groeiers): Applied physics and systems biology. Towards a cross-disciplinary complex systems center @ VUB
1/03/14 → 28/02/19