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1. Artificial Neuronal Devices Based on Emerging Materials: Neuronal Dynamics and Applications

   https://doi.org/10.1002/adma.202205047  

   Liu, Hefei; Qin, Yuan; Chen, Hung‐Yu; Wu, Jiangbin; Ma, Jiahui; Du, Zhonghao; Wang, Nan; Zou, Jingyi; Lin, Sen; Zhang, Xu; et al (March 2023, Advanced Materials)

   Abstract Artificial neuronal devices are critical building blocks of neuromorphic computing systems and currently the subject of intense research motivated by application needs from new computing technology and more realistic brain emulation. Researchers have proposed a range of device concepts that can mimic neuronal dynamics and functions. Although the switching physics and device structures of these artificial neurons are largely different, their behaviors can be described by several neuron models in a more unified manner. In this paper, the reports of artificial neuronal devices based on emerging volatile switching materials are reviewed from the perspective of the demonstrated neuron models, with a focus on the neuronal functions implemented in these devices and the exploitation of these functions for computational and sensing applications. Furthermore, the neuroscience inspirations and engineering methods to enrich the neuronal dynamics that remain to be implemented in artificial neuronal devices and networks toward realizing the full functionalities of biological neurons are discussed. 

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2. Automated sorting of neuronal trees in fluorescent images of neuronal networks using NeuroTreeTracer

   https://doi.org/10.1038/s41598-018-24753-w  

   Kayasandik, Cihan; Negi, Pooran; Laezza, Fernanda; Papadakis, Manos; Labate, Demetrio (December 2018, Scientific Reports)
3. Uncovering Neuronal Networks Defined by Consistent between-Neuron Spike Timing from Neuronal Spike Recordings

   https://doi.org/10.1523/ENEURO.0379-17.2018  

   van der Meij, Roemer; Voytek, Bradley (January 2018, eneuro)

   It is widely assumed that distributed neuronal networks are fundamental to the functioning of the brain. Consistent spike timing between neurons is thought to be one of the key principles for the formation of these networks. This can involve synchronous spiking or spiking with time delays, forming spike sequences when the order of spiking is consistent. Finding networks defined by their sequence of time-shifted spikes, denoted here as spike timing networks, is a tremendous challenge. As neurons can participate in multiple spike sequences at multiple between-spike time delays, the possible complexity of networks is prohibitively large. We present a novel approach that is capable of (1) extracting spike timing networks regardless of their sequence complexity, and (2) that describes their spiking sequences with high temporal precision. We achieve this by decomposing frequency-transformed neuronal spiking into separate networks, characterizing each network’s spike sequence by a time delay per neuron, forming a spike sequence timeline. These networks provide a detailed template for an investigation of the experimental relevance of their spike sequences. Using simulated spike timing networks, we show network extraction is robust to spiking noise, spike timing jitter, and partial occurrences of the involved spike sequences. Using rat multi-neuron recordings, we demonstrate the approach is capable of revealing real spike timing networks with sub-millisecond temporal precision. By uncovering spike timing networks, the prevalence, structure, and function of complex spike sequences can be investigated in greater detail, allowing us to gain a better understanding of their role in neuronal functioning. 

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4. Recurrent activity in neuronal avalanches

   https://doi.org/10.1038/s41598-023-31851-x  

   Salners, Tyler; Avila, Karina E.; Nicholson, Benjamin; Myers, Christopher R.; Beggs, John; Dahmen, Karin A. (December 2023, Scientific Reports)

   Abstract A new statistical analysis of large neuronal avalanches observed in mouse and rat brain tissues reveals a substantial degree of recurrent activity and cyclic patterns of activation not seen in smaller avalanches. To explain these observations, we adapted a model of structural weakening in materials. In this model, dynamical weakening of neuron firing thresholds closely replicates experimental avalanche size distributions, firing number distributions, and patterns of cyclic activity. This agreement between model and data suggests that a mechanism like dynamical weakening plays a key role in recurrent activity found in large neuronal avalanches. We expect these results to illuminate the causes and dynamics of large avalanches, like those seen in seizures. 

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5. Neuronal Autophagy by the Numbers

   https://doi.org/10.1080/27694127.2022.2163091  

   Cason, Sydney E.; Mogre, Saurabh S.; Koslover, Elena F.; Holzbaur, Erika L. (December 2023, Autophagy Reports)