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1. Synapses without tension fail to fire in an in vitro network of hippocampal neurons

   https://doi.org/10.1073/pnas.2311995120  

   Joy, Md Saddam; Nall, Duncan L.; Emon, Bashar; Lee, Ki Yun; Barishman, Alexandra; Ahmed, Movviz; Rahman, Saeedur; Selvin, Paul R.; Saif, M. Taher (December 2023, Proceedings of the National Academy of Sciences)

   Neurons in the brain communicate with each other at their synapses. It has long been understood that this communication occurs through biochemical processes. Here, we reveal that mechanical tension in neurons is essential for communication. Using in vitro rat hippocampal neurons, we find that 1) neurons become tout/tensed after forming synapses resulting in a contractile neural network, and 2) without this contractility, neurons fail to fire. To measure time evolution of network contractility in 3D (not 2D) extracellular matrix, we developed an ultrasensitive force sensor with 1 nN resolution. We employed Multi-Electrode Array and iGluSnFR, a glutamate sensor, to quantify neuronal firing at the network and at the single synapse scale, respectively. When neuron contractility is relaxed, both techniques show significantly reduced firing. Firing resumes when contractility is restored. This finding highlights the essential contribution of neural contractility in fundamental brain functions and has implications for our understanding of neural physiology. 

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2. 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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3. Optimizing Initial State of Detector Sensors in Quantum Sensor Networks

   https://doi.org/10.1145/3655028  

   Zhan, Caitao; Gupta, Himanshu; Hillery, Mark (June 2024, ACM Transactions on Quantum Computing)

   none (Ed.)

   In this article, we consider a network of quantum sensors, where each sensor is a qubit detector that “fires,” i.e., its state changes when an event occurs close by. The change in state due to the firing of a detector is given by a unitary operator, which is the same for all sensors in the network. Such a network of detectors can be used to localize an event, using a protocol to determine the firing sensor, presumably the one closest to the event. The determination of the firing sensor can be posed as aQuantum State Discriminationproblem, which incurs a probability of error depending on the initial state and the measurement operators used. In this article, we address the problem of determining the optimal initial global state of a network of detectors that incur a minimum probability of error in determining the firing sensor. For this problem, we derive necessary and sufficient conditions for the existence of an initial state that allows for perfect discrimination, i.e., zero probability of error. Using insights from this result, we derive a conjectured optimal solution for the initial state, provide a pathway to prove the conjecture, and validate the conjecture empirically using multiple search heuristics that seem to perform near-optimally. 

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4. Uncovering the electrical synapse proteome in retinal neurons via in vivo proximity labeling

   https://doi.org/10.1101/2024.11.26.625481  

   Tetenborg, Stephan; Shihabeddin, Eyad; Kumar, Elizebeth_Olive_Akansha Manoj; Sigulinsky, Crystal L; Dedek, Karin; Lin, Ya-Ping; Echeverry, Fabio A; Hoff, Hannah; Pereda, Alberto E; Jones, Bryan W; et al (November 2024, bioRxiv)

   Abstract Electrical synapses formed by Connexin 36 (Cx36) serve as a fast means for communication in the nervous systems. Only little is known about the protein complexes that constitute these synapses. In the present study, we applied different BioID strategies to screen the interactomes of Connexin 36 the major neuronal connexin and its zebrafish orthologue Cx35b in retinal neurons. Forin vivoproximity labeling in mice, we took advantage of the Cx36-EGFP strain and expressed a GFP-nanobody-TurboID fusion construct selectively in AII amacrine cells. Forin vivoBioID in zebrafish, we generated a transgenic line expressing a Cx35b-TurboID fusion under control of the Cx35b promoter. Both strategies allowed us to capture a plethora of molecules that were associated with electrical synapses and showed a high degree of evolutionary conservation in the proteomes of both species. Besides known interactors of Cx36 such as ZO-1 and ZO-2 we have identified more than 50 new proteins, such as scaffold proteins, adhesion molecules and regulators of the cytoskeleton. We further determined the subcellular localization of these proteins in AII amacrine and tested potential binding interactions with Cx36. Of note, we identified signal induced proliferation associated 1 like 3 (SIPA1L3), a protein that has been implicated in cell junction formation and cell polarity as a new scaffold of electrical synapses. Interestingly, SIPA1L3 was able to interact with ZO-1, ZO-2 and Cx36, suggesting a pivotal role in electrical synapse function. In summary, our study provides a first detailed view of the electrical synapse proteome in retinal neurons. 

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5. Optimizing state change detection in functional temporal networks through dynamic community detection

   https://doi.org/10.1093/comnet/cny030  

   Vaiana, Michael; Goldberg, Ethan M.; Muldoon, Sarah F.; Porter, ed., Mason (December 2018, Journal of Complex Networks)

   Abstract Dynamic community detection provides a coherent description of network clusters over time, allowing one to track the growth and death of communities as the network evolves. However, modularity maximization, a popular method for performing multilayer community detection, requires the specification of an appropriate null network as well as resolution and interlayer coupling parameters. Importantly, the ability of the algorithm to accurately detect community evolution is dependent on the choice of these parameters. In functional temporal networks, where evolving communities reflect changing functional relationships between network nodes, it is especially important that the detected communities reflect any state changes of the system. Here, we present analytical work suggesting that a uniform null network provides improved sensitivity to the detection of small evolving communities in temporal networks with positive edge weights bounded above by 1, such as certain types of correlation networks. We then propose a method for increasing the sensitivity of modularity maximization to state changes in nodal dynamics by modelling self-identity links between layers based on the self-similarity of the network nodes between layers. This method is more appropriate for functional temporal networks from both a modelling and mathematical perspective, as it incorporates the dynamic nature of network nodes. We motivate our method based on applications in neuroscience where network nodes represent neurons and functional edges represent similarity of firing patterns in time. We show that in simulated data sets of neuronal spike trains, updating interlayer links based on the firing properties of the neurons provides superior community detection of evolving network structure when groups of neurons change their firing properties over time. Finally, we apply our method to experimental calcium imaging data that monitors the spiking activity of hundreds of neurons to track the evolution of neuronal communities during a state change from the awake to anaesthetized state. 

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