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

   https://doi.org/10.1101/2023.06.24.546401  

   Hossain_Joy, Md Saddam; Nall, Duncan L; Emon, Basher; Lee, Ki Yun; Barishman, Alexandra; Ahmed, Movviz; Rahman, Saeedur; Selvin, Paul R; Saif, M_Taher A (June 2023, bioRxiv)

   Abstract 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 a previously unrecognized paradigm wherein mechanical tension in neurons is essential for communication. Usingin vitrorat 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 (not2D) extracellular matrix, we developed an ultra-sensitive force sensor with 1 nN resolution. We employed Multi-Electrode Array (MEA) 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. Neural contractility may play a crucial role in memory, learning, cognition, and various neuropathologies. 

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2. Energy-Efficient Deep Neural Networks with Mixed-Signal Neurons and Dense-Local and Sparse-Global Connectivity

   https://doi.org/10.1145/3394885.3431614  

   Chatterjee, Baibhab; Sen, Shreyas (January 2021, Asia and South Pacific Design Automation Conference)

   null (Ed.)

   Neuromorphic Computing has become tremendously popular due to its ability to solve certain classes of learning tasks better than traditional von-Neumann computers. Data-intensive classification and pattern recognition problems have been of special interest to Neuromorphic Engineers, as these problems present complex use-cases for Deep Neural Networks (DNNs) which are motivated from the architecture of the human brain, and employ densely connected neurons and synapses organized in a hierarchical manner. However, as these systems become larger in order to handle an increasing amount of data and higher dimensionality of features, the designs often become connectivity constrained. To solve this, the computation is divided into multiple cores/islands, called processing engines (PEs). Today, the communication among these PEs are carried out through a power-hungry network-on-chip (NoC), and hence the optimal distribution of these islands along with energy-efficient compute and communication strategies become extremely important in reducing the overall energy of the neuromorphic computer, which is currently orders of magnitude higher than the biological human brain. In this paper, we extensively analyze the choice of the size of the islands based on mixed-signal neurons/synapses for 3-8 bit-resolution within allowable ranges for system-level classification error, determined by the analog non-idealities (noise and mismatch) in the neurons, and propose strategies involving local and global communication for reduction of the system-level energy consumption. AC-coupled mixed-signal neurons are shown to have 10X lower non-idealities than DC-coupled ones, while the choice of number of islands are shown to be a function of the network, constrained by the analog to digital conversion (or viceversa) power at the interface of the islands. The maximum number of layers in an island is analyzed and a global bus-based sparse connectivity is proposed, which consumes orders of magnitude lower power than the competing powerline communication techniques. 

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3. Graphene/MoS2/SiOx memristive synapses for linear weight update

   https://doi.org/10.1038/s41699-023-00388-y  

   Krishnaprasad, Adithi; Dev, Durjoy; Shawkat, Mashiyat Sumaiya; Martinez-Martinez, Ricardo; Islam, Molla Manjurul; Chung, Hee-Suk; Bae, Tae-Sung; Jung, Yeonwoong; Roy, Tania (December 2023, npj 2D Materials and Applications)

   Abstract Memristors for neuromorphic computing have gained prominence over the years for implementing synapses and neurons due to their nano-scale footprint and reduced complexity. Several demonstrations show two-dimensional (2D) materials as a promising platform for the realization of transparent, flexible, ultra-thin memristive synapses. However, unsupervised learning in a spiking neural network (SNN) facilitated by linearity and symmetry in synaptic weight update has not been explored thoroughly using the 2D materials platform. Here, we demonstrate that graphene/MoS₂/SiO_x/Ni synapses exhibit ideal linearity and symmetry when subjected to identical input pulses, which is essential for their role in online training of neural networks. The linearity in weight update holds for a range of pulse width, amplitude and number of applied pulses. Our work illustrates that the mechanism of switching in MoS₂-based synapses is through conductive filaments governed by Poole-Frenkel emission. We demonstrate that the graphene/MoS₂/SiO_x/Ni synapses, when integrated with a MoS₂-based leaky integrate-and-fire neuron, can control the spiking of the neuron efficiently. This work establishes 2D MoS₂as a viable platform for all-memristive SNNs. 

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4. Cellular mechanisms underlying state-dependent neural inhibition with magnetic stimulation

   https://doi.org/10.1038/s41598-022-16494-8  

   Ye, Hui; Chen, Vincent; Hendee, Jenna (December 2022, Scientific Reports)

   Abstract Novel stimulation protocols for neuromodulation with magnetic fields are explored in clinical and laboratory settings. Recent evidence suggests that the activation state of the nervous system plays a significant role in the outcome of magnetic stimulation, but the underlying cellular and molecular mechanisms of state-dependency have not been completely investigated. We recently reported that high frequency magnetic stimulation could inhibit neural activity when the neuron was in a low active state. In this paper, we investigate state-dependent neural modulation by applying a magnetic field to single neurons, using the novel micro-coil technology. High frequency magnetic stimulation suppressed single neuron activity in a state-dependent manner. It inhibited neurons in slow-firing states, but spared neurons from fast-firing states, when the same magnetic stimuli were applied. Using a multi-compartment NEURON model, we found that dynamics of voltage-dependent sodium and potassium channels were significantly altered by the magnetic stimulation in the slow-firing neurons, but not in the fast-firing neurons. Variability in neural activity should be monitored and explored to optimize the outcome of magnetic stimulation in basic laboratory research and clinical practice. If selective stimulation can be programmed to match the appropriate neural state, prosthetic implants and brain-machine interfaces can be designed based on these concepts to achieve optimal results. 

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5. Functional Implications of Dale's Law in Balanced Neuronal Network Dynamics and Decision Making

   https://doi.org/10.3389/fnins.2022.801847  

   Barranca, Victor J.; Bhuiyan, Asha; Sundgren, Max; Xing, Fangzhou (February 2022, Frontiers in Neuroscience)

   The notion that a neuron transmits the same set of neurotransmitters at all of its post-synaptic connections, typically known as Dale's law, is well supported throughout the majority of the brain and is assumed in almost all theoretical studies investigating the mechanisms for computation in neuronal networks. Dale's law has numerous functional implications in fundamental sensory processing and decision-making tasks, and it plays a key role in the current understanding of the structure-function relationship in the brain. However, since exceptions to Dale's law have been discovered for certain neurons and because other biological systems with complex network structure incorporate individual units that send both positive and negative feedback signals, we investigate the functional implications of network model dynamics that violate Dale's law by allowing each neuron to send out both excitatory and inhibitory signals to its neighbors. We show how balanced network dynamics, in which large excitatory and inhibitory inputs are dynamically adjusted such that input fluctuations produce irregular firing events, are theoretically preserved for a single population of neurons violating Dale's law. We further leverage this single-population network model in the context of two competing pools of neurons to demonstrate that effective decision-making dynamics are also produced, agreeing with experimental observations from honeybee dynamics in selecting a food source and artificial neural networks trained in optimal selection. Through direct comparison with the classical two-population balanced neuronal network, we argue that the one-population network demonstrates more robust balanced activity for systems with less computational units, such as honeybee colonies, whereas the two-population network exhibits a more rapid response to temporal variations in network inputs, as required by the brain. We expect this study will shed light on the role of neurons violating Dale's law found in experiment as well as shared design principles across biological systems that perform complex computations. 

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