This work introduces their symmetric counterpart by replacing adaptation with slow self-excitation, motivated by intrinsic calcium-mediated membrane currents, and derives and validate a mean-field neural mass model that remains stable while retaining working-memory functionality.
Abstract
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.
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
It is demonstrated that neuromorphic-triggered optogenetic inhibition significantly alters ripple dynamics and reduces oscillatory energy, establishing a practical and accessible neuromorphic framework for low-latency closed-loop control of fast brain dynamics in vivo.
P. Félix, M. Jurado-Parras, J. Freitas et al.· bioRxiv· 0 citations
It is aimed at proving that SNNs have potential in such areas as computer vision, robotics, and speech recognition, and their role in overcoming the barrier between artificial and biological neural systems is proved.
Mesala Sravani, K. Kumari, S. M. Reddy· International Journal of Unc...· 0 citations
Neuromorphic computing aims to build electronic systems that mimic the brain’s remarkable efficiency and adaptability. A key feature of brain is homeostatic plasticity, which stabilizes neural activity; the Bienenstock–Cooper–Munro (BCM) rule accurately describes this by adjusting a neuron’s sensitivity threshold based on past activity. However, implementing the homeostatic rule in synaptic device remains challenging. Here we show that a CuInP₂S₆ memristor naturally realizes the complete BCM rule. We reveal that the device’s intrinsic junction capacitance generates reverse spikes that cause spike-rate-dependent conductance depression, combining with second-order ionic dynamics to replicate key BCM features. Through simulations, we demonstrate that adding a similar capacitance can universally enable this behavior in conventional memristors. Finally, we build a physical reservoir computing system based on this strategy that exhibits robust, noise-tolerant spatiotemporal processing. This work provides a viable path for deploying adaptive homeostatic plasticity in hardware, enhancing the reliability of neuromorphic systems. Neuromorphic computing leverages emerging devices to emulate brain learning mechanisms. Huang et al. report a memristor with a junction capacitor that generates reverse voltage spikes during pulse stimulation to realize homeostatic synaptic plasticity, with efficacy validated through spatiotemporal information processing.
Jiangshun Huang, Anping Huang, Qiaofeng Yang et al.· Nature Communications· 0 citations
Neuromorphic computing is closely associated with spiking neuronal networks. However, an alternative class of so-called"rate-based"models arising from computational neuroscience and machine learning forgoes spiking interactions and instead relies on continuous coupling between neurons. Existing neuromorphic implementations designed around spike-based interactions are not well-suited for emulating such models. Here view the distributed simulation of these models as message-passing algorithms on parallel hardware. Leveraging prior art in numerical algorithms and distributed simulation, we outline steps that enable the design of efficient digital neuromorphic accelerators for non-spiking neuronal models. In particular, we show that multi-bit packets, rather than spikes, are the most efficient communication strategy in packet-switched networks and that compared to basic numerical integration methods, higher-order differential equation solvers decrease both computation and communication costs while achieving lower numerical error, but that these benefits are ultimately limited by arithmetic precision. Using our proposed design principles, we convert an existing neuromorphic architecture into a distributed numerical solver - a spikeless neuromorphic system - for continuously-coupled neuronal models. We thereby demonstrate that our theoretical considerations indeed translate into practical advantages, namely reduced energy consumption and delay.
Jakob Jordan, Ole Richter, Cong Li et al.· 0 citations
A high-performance hardware implementation of large-scale neuromorphic system to investigate anesthetic-induced neural dynamics and paves the way for advanced brain–machine interfaces and closed-loop anesthetic delivery systems.
Chuan-Guang Wang, Xiaotian Pan, Si Chen et al.· Frontiers in Systems Neurosc...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
