Fig. 3: Signal processing of satellite learning modules (SLMs).

Associative learning of texture and nociceptive signals. a Biological and artificial neural network. Relative timing between pre- and postsynaptic spikes create voltage differences across synapses/satellite weight adjusting resistive memories (SWARMs), tuning the firing rate of neurons/satellite spiking neurons (SSNs). In the proposed approach, teacher signals from the nociceptor/STAR modulates the synaptic weights creating association. b Weight changes in the SWARM follow an anti-Hebbian spike-timing-dependent plasticity (STDP) rule. Representative raw I–t curves of long-term potentiation (LTP) and depression (LTD) are shown for clarity. c Associative learning of pain and texture signals using satellite threshold adjusting receptors (STARs) and SWARMs. Four SWARMs are trained with signals related to the texture of objects and noxious output signals from STARs.