Phenomenological spiking neuron models such as Izhikevich, adaptive quadratic integrate-and-fire (aQIF), and Adaptive Exponential (AdEx) are widely used because of their simplicity and numerical efficiency. These models reproduce diverse neuronal dynamics through a slow self-inhibitory adaptation variable. Here we introduce their symmetric counterpart by replacing adaptation with slow self-excitation, motivated by intrinsic calcium-mediated membrane currents. This minimal modification enables robust persistent spiking and working-memory dynamics without compromising computational efficiency. These properties remain in excitatory spiking neural networks. We then derive and validate a mean-field neural mass model that remains stable while retaining working-memory functionality. Additionally, we implement the single-neuron model in a minimal memristor-based neuromorphic circuit and experimentally confirm its dynamics. These results provide scalable tools for large-scale brain simulations and neuromorphic applications in robotics, brain-machine interfaces, and edge AI devices.
D. Depannemaecker, Adrien D’hollande, G. Casagrande et al.· Nature Communications· 0 citations
We introduce a hardware circuit model that implements spike-time dependent plasticity (STDP) to endow spiking neural networks with learning capabilities. Our circuit model is characterized as both minimal and bio-inspired, due to its simplicity and to a novel active dendrite compartment that mimics the synaptic potentiation mechanism. The active dendrite consists of an integrate-and-fire stage which produces a train of pulses whose number is inversely related to the timing between pre- and post-synaptic spikes. The dendrite pulses modulate in a reliable manner the synaptic efficacy (conductance) that we implemented with a digipot, considered as an idealized non-volatile memristor. We demonstrate the behavior of the circuit by implementing a minimal spiking neuron model of associative learning by STDP, which is analog to the classic conditioning experiment of Pavlov’s dog.
Adrien D’hollande, Olivier Schneegans, Kang Wang et al.· Neuromorphic Computing and E...· 0 citations