Attractor networks require neuronal connections to be highly structured in order to maintain attractor states that represent information, while excitation and inhibition balanced networks (E-INNs) require neuronal connections to be random and sparse to generate irregular neuronal firing. Despite being regarded as canonical models of neural circuits, both types of networks are usually studied independently, and it remains unclear how they coexist in the brain, given their very different structural demands. In this study, we investigate the compatibility of continuous attractor neural networks (CANNs) and E-INNs. In line with recent experimental data, we find that a neural circuit can exhibit both the traits of CANNs and E-INNs when the neuronal synapses consist of two sets: one set is strong and fast for irregular firing, and the other set is weak and slow for attractor dynamics. In addition, both simulations and theoretical analysis reveal that the network exhibits enhanced performance compared to the case of using only one set of synapses, with accelerated convergence of attractor states and retained E-I balanced condition for localized input. We hope that this study provides insight into how structured neural computations are realized by irregular firings of neurons.

The GAIN Model: A Nature - Inspired Neural Network Framework Based on an Adaptation of the Izhikevich Model Gage K. R. Hooper Independent Researcher Future Aerospace Engineering Student, Embry - Riddle Aeronautical University May 3 1, 2025 1 Abstract While many neural networks focus on layers to process information, the GAIN model uses a grid - based structure to improve biological plausibility and the dynamics of the model. The grid structure helps neurons to interact with their closest neighbors and im prove their connections with one another, which is seen in biological neurons. While also being implemented with the Izhikevich model this approach allows for a computationally efficient and biologically accurate simulation that can aid in the development of neural networks, large scale simulations, and the development in the neuroscience field. This adaptation of the Izhikevich model can improve the dynamics and accuracy of the model, allowing for its uses to be specialized but efficient. Early models of SSNs, such as the Hodgkin - Huxley model (1952), were detailed and capable of replicating the exact dynamics of neuronal spiking, considering every ion channel, but it was too computationally inefficie nt. A computational model that can simulate the function of neurons. The activation of neurons determined by its action potential when a neuron's difference between interior and exterior voltages (membrane potential) rapidly increases and decreases. In response to limitations seen in these models, Eugene Izhikevich (2003) introduced a spiking neural network model, achieving a balance between biological plausibility and computational efficiency (See Appendix A). The Izhikevich model can reproduce neuron behaviors while remaining computationally lightweight, resulting in it being widely adopted for large - scale simulations.

The nociceptive withdrawal reflex (NWR) is a mechanism to mediate interactions and protect the body from damage in a potentially dangerous environment. To better convey warning signals to users of prosthetic arms or autonomous robots and protect them by triggering a proper NWR, it is useful to use a biological representation of temperature information for fast and effective processing. In this work, we present a neuromorphic spiking network for heat-evoked NWR by mimicking the structure and encoding scheme of the reflex arc. The network is trained with the bio-plausible reward modulated spike timing-dependent plasticity learning algorithm. We evaluated the proposed model and three other methods in recent studies that trigger NWR in an experiment with radiant heat. We found that only the neuromorphic model exhibits the spatial summation (SS) effect and temporal summation (TS) effect similar to humans and can encode the reflex strength matching the intensity of the stimulus in the relative spike latency online. The improved bio-plausibility of this neuromorphic model could improve sensory feedback in neural prostheses.