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1. Three Artificial Spintronic Leaky Integrate-and-Fire Neurons

   https://doi.org/10.1142/S2010324720400032  

   Brigner, Wesley H.; Hu, Xuan; Hassan, Naimul; Jiang-Wei, Lucian; Bennett, Christopher H.; Garcia-Sanchez, Felipe; Akinola, Otitoaleke; Pasquale, Massimo; Marinella, Matthew J.; Incorvia, Jean Anne; et al (January 2020, SPIN)

   Due to their non-volatility and intrinsic current integration capabilities, spintronic devices that rely on domain wall (DW) motion through a free ferromagnetic track have garnered significant interest in the field of neuromorphic computing. Although a number of such devices have already been proposed, they require the use of external circuitry to implement several important neuronal behaviors. As such, they are likely to result in either a decrease in energy efficiency, an increase in fabrication complexity, or even both. To resolve this issue, we have proposed three individual neurons that are capable of performing these functionalities without the use of any external circuitry. To implement leaking, the first neuron uses a dipolar coupling field, the second uses an anisotropy gradient, and the third uses shape variations of the DW track. 

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2. Hybrid spin-CMOS stochastic spiking neuron for high-speed emulation of In vivo neuron dynamics

   https://doi.org/10.1049/iet-cdt.2017.0145  

   Pyle, Steven D.; Camsari, Kerem Y.; DeMara, Ronald F. (February 2018, IET Computers & Digital Techniques)

   The spintronic stochastic spiking neuron (S3N) developed herein realizes biologically mimetic stochastic spiking characteristics observed within in vivo cortical neurons, while operating several orders of magnitude more rapidly and exhibiting a favorable energy profile. This work leverages a novel probabilistic spintronic switching element device that provides thermally-driven and current-controlled tunable stochasticity in a compact, low-energy, and high-speed package. Simulation program with integrated circuit emphasis (SPICE) simulation results indicate that the equivalent of 1 second of in vivo neuronal spiking characteristics can be generated on the order of nanoseconds, enabling the feasibility of extremely rapid emulation of in vivo neuronal behaviors for future statistical models of cortical information processing. Their results also indicate that the S3N can generate spikes on the order of ten picoseconds while dissipating only 0.6–9.6 μW, depending on the spiking rate. Additionally, they demonstrate that an S3N can implement perceptron functionality, such as AND-gate- and OR-gate-based logic processing, and provide future extensions of the work to more advanced stochastic neuromorphic architectures. 

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3. A computational neural model that incorporates both intrinsic dynamics and sensory feedback in the Aplysia feeding network

   https://doi.org/10.1007/s00422-024-00991-2  

   Li, Yanjun; Webster-Wood, Victoria A; Gill, Jeffrey P; Sutton, Gregory P; Chiel, Hillel J; Quinn, Roger D (May 2024, Biological Cybernetics)

   Abstract Studying the nervous system underlying animal motor control can shed light on how animals can adapt flexibly to a changing environment. We focus on the neural basis of feeding control inAplysia californica. Using the Synthetic Nervous System framework, we developed a model ofAplysiafeeding neural circuitry that balances neurophysiological plausibility and computational complexity. The circuitry includes neurons, synapses, and feedback pathways identified in existing literature. We organized the neurons into three layers and five subnetworks according to their functional roles. Simulation results demonstrate that the circuitry model can capture the intrinsic dynamics at neuronal and network levels. When combined with a simplified peripheral biomechanical model, it is sufficient to mediate three animal-like feeding behaviors (biting, swallowing, and rejection). The kinematic, dynamic, and neural responses of the model also share similar features with animal data. These results emphasize the functional roles of sensory feedback during feeding. 

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4. Semi-supervised learning and inference in domain-wall magnetic tunnel junction (DW-MTJ) neural networks

   https://doi.org/10.1117/12.2530308  

   Bennett, Christopher H.; Hassan, Naimul; Hu, Xuan; Incornvia, Jean Anne; Friedman, Joseph S.; Marinella, Matthew M. (September 2019, SPIE Spintronics XII)

   Advances in machine intelligence have sparked interest in hardware accelerators to implement these algorithms, yet embedded electronics have stringent power, area budgets, and speed requirements that may limit nonvolatile memory (NVM) integration. In this context, the development of fast nanomagnetic neural networks using minimal training data is attractive. Here, we extend an inference-only proposal using the intrinsic physics of domain-wall MTJ (DW-MTJ) neurons for online learning to implement fully unsupervised pattern recognition operation, using winner-take-all networks that contain either random or plastic synapses (weights). Meanwhile, a read-out layer trains in a supervised fashion. We find our proposed design can approach state-of-the-art success on the task relative to competing memristive neural network proposals, while eliminating much of the area and energy overhead that would typically be required to build the neuronal layers with CMOS devices. 

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5. Neuroanatomical Alterations in the CNTNAP2 Mouse Model of Autism Spectrum Disorder

   https://doi.org/10.3390/brainsci13060891  

   Gandhi, Tanya; Canepa, Cade R; Adeyelu, Tolulope T; Adeniyi, Philip A; Lee, Charles C (June 2023, Brain Sciences)

   Autism spectrum disorder (ASD) is associated with neurodevelopmental alterations, including atypical forebrain cellular organization. Mutations in several ASD-related genes often result in cerebral cortical anomalies, such as the abnormal developmental migration of excitatory pyramidal cells and the malformation of inhibitory neuronal circuitry. Notably here, mutations in the CNTNAP2 gene result in ectopic superficial cortical neurons stalled in lower cortical layers and alterations to the balance of cortical excitation and inhibition. However, the broader circuit-level implications of these findings have not been previously investigated. Therefore, we assessed whether ectopic cortical neurons in CNTNAP2 mutant mice form aberrant connections with higher-order thalamic nuclei, potentially accounting for some autistic behaviors, such as repetitive and hyperactive behaviors. Furthermore, we assessed whether the development of parvalbumin-positive (PV) cortical interneurons and their specialized matrix support structures, called perineuronal nets (PNNs), were altered in these mutant mice. We found alterations in both ectopic neuronal connectivity and in the development of PNNs, PV neurons and PNNs enwrapping PV neurons in various sensory cortical regions and at different postnatal ages in the CNTNAP2 mutant mice, which likely lead to some of the cortical excitation/inhibition (E/I) imbalance associated with ASD. These findings suggest neuroanatomical alterations in cortical regions that underlie the emergence of ASD-related behaviors in this mouse model of the disorder. 

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