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The structure of the lobster STG. Just to be clearer, the blue neurons belong to the gastric circuit and the yellow ones belong to the pyloric circuit.
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The research of the circuit dynamics of the lobster stomatogastric ganglion (STG) inspires the understanding of the roles that regulates the rhythm of the lobster STG. In this paper, the lobster STG was analyzed by using Winnerless Competition (WLC) network. We got the rhythm of the gastric and pyloric circuits, which is similar to the experimental...
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... this paper, in order to analyze the circuit dynamics, a firing model of the lobster J o u r n a l P r e -p r o o f STG is established and shown in Fig. 1 by using the winnerless competition (WLC) network model. The following discussion briefly introduces the functions of neurons in the lobster STG. In the STG, each neuron controls different muscles with their respective responsibility in the physiological activity of the lobster [13] . In gastric circuit, Int1 coordinates the medial and ...
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... dimension WLC system, in the rest part of the paper, we studied the firing patterns, and phase diagrams of it, as well as observe the changes of the system by changing parameters. The activities of these neurons in bio experiments are modeled by the WLC network as mentioned above, and the lobster network those neurons belonging to is shown in Fig. 1. The constant parameters in the model are as ...
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... order of the 1 st , 2 nd and 5 th neurons is in that figure. It corresponds to the inhibition loop of 1 5 2 1 in the network. In the STG, there are excitatory synapses and two-way connections between neurons, which are different from the artifact network. But we can find the similar spike order in it shown in Fig. 2(a, b). In the gastric circuit, Fig. 1 shows a synapse connection in an inhibitory loop formed by Int1, AM, DG, LG, and ...
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... and DG inhibit LG and MG, and finally LG and MG inhibit Int1. The same neurons in Fig. 2(a) show a spike order as Int1 DG, AM LG, MG Int1. DG and AM, LG, and MG fire synchronously because of the gap junctions between them. AM, DG and Int1 fire almost simultaneously due to the excitatory synapse from Int1 to them. Analogously, in pyloric circuit, Fig. 1 shows PD PY LP PD, (4) which forms an inhibitory loop. And we can find the firing order of them in Fig. 2(b), J o u r n a l P r e -p r o o f that is PD LP PY PD. PD fires after a bursting of PY, and LP fires after a bursting of PD, and then, PY fires again immediately. We can find that this order is opposite to that of the inhibit ...
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... exponent may be influenced by the simulate time and the time step. To make sure that the system is indeed in a chaotic state, a second largest Lyapunov exponent which is closer than is needed. So, we set the minimal threshold of as such that when , , . This means that when , the chaos J o u r n a l P r e -p r o o f appears in the lobster STG. Fig. 10 is an example of how the chaotic behaviors disappear as a function of and when holding and applying . Fig. 10 shows that the system is nonchaotic when or are small. We can understand this result from the special case . It is necessary that for the system to generate the chaotic behaviors under this assumption [18] . So, the lobster STG ...
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... in a chaotic state, a second largest Lyapunov exponent which is closer than is needed. So, we set the minimal threshold of as such that when , , . This means that when , the chaos J o u r n a l P r e -p r o o f appears in the lobster STG. Fig. 10 is an example of how the chaotic behaviors disappear as a function of and when holding and applying . Fig. 10 shows that the system is nonchaotic when or are small. We can understand this result from the special case . It is necessary that for the system to generate the chaotic behaviors under this assumption [18] . So, the lobster STG tends to be nonchaotic when is small which breaks the necessary condition . and . The white region is the ...
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... Additionally, the firing r Circle: The circle network (Fig. 8(b)) always contains three populations. Structures like this play an important role in biological neural system structure-function convergent [47], [48]. The excitatory-inhibitory ratio is set to 2 : 1 and the firing rate is about 16˜Hz. ...
Spiking Neural Networks (SNNs) are currently the most widely used computing model for neuroscience communities. There is also an increasing research interest in exploring the potential of SNN in brain-inspired computing, artificial intelligence, and other areas. As SNNs possess distinguished characteristics that originate from biological authenticity, they require dedicated simulation frameworks to achieve usability and efficiency. However, there is no widely-used, easily accessible, high performance SNN simulation framework for GPU clusters. In this paper, we propose ENLARGE, an efficient SNN simulation framework on GPU clusters. ENLARGE provides a multi-level architecture that deals with computation, communication, and synchronization hierarchically. We also propose an efficient communication method with an all-to-all communication pattern. To deal with the delay of spike delivery, which is the most distinguished SNN characteristic, several delay-aware optimization methods are also proposed. We further propose a multilevel workload management method. Various experiments are carried out to demonstrate the performance and scalability of the framework, as well as the effects of the optimization methods. Test results show that ENLARGE can achieve
$3.17\times \sim 28.12\times$
speedup compared with the most widely used NEST simulator and
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... In the hierarchical structure, we define the chunks in layer n as parent chunks (PCs), and their connected sub-chunks in layer n + 1 are named as child chunks (CCs). A winner neuron in a PC can communicate the corresponding CC based on the dynamical principle of winnerless competition (WLC) [13]. Notably, each winner neuron is in a metastable state, and it will switch from one neuron to another through neuronal communication, forming a sequential memory trace. ...
Brain-inspired approaches can efficiently analyze the activities of biological neural networks and solve computationally hard problems with energy efficiencies unattainable with von Neumann architectures, indicating a significant improvement in the understanding of neuronal communications and functionalities. Here, we present a brain-inspired multimodal signal processing system with organic memristor arrays that can potentially integrate the signal sensory, storage, and computation. To facilitate the multimodal signal processing system design, we used four components. First, we present a multimodal signal sensory module mainly responsible for multimodal (iconic, echoic, olfactory, muscular, and gustatory) signal collection, fusion, and storage. Second, a high-density cross-point memristive synapse array is constructed after fabrication of the albumin protein memristor to realize the dense connectivity between layers of computing, data storage, and communication. Third, considering the structure and function of the brain region, we demonstrate a general learning module for hierarchy learning, which can recognize and imagine multimodal information. Finally, the necessary peripheral circuit module (consisting of winnerless competition function circuit, analog-to-digital converter, digital-to-analog converter, pulse modulator, etc.) is designed. Notably, our system can capture massive amounts of data every second and perform in situ processing of multimodal signals. This study is expected to help achieve the deep integration of nano materials into neuromorphic computing systems and energy-efficient integrated circuits.
