Delay-Induced Self-Organization Dynamics in a Prey-Predator Network with Diffusion

Time delays can induce the loss of stability and degradation of performance. In this paper, the pattern dynamics of a prey-predator network with diffusion and delay are investigated, where the inhomogeneous distribution of species in space can be viewed as a random network, and delay can affect the stability of the network system. Our results show that time delay can induce the emergence of Hopf and Turing bifurcations, which are independent of the network, and the conditions of bifurcation are derived by linear stability analysis. Moreover, we find that the Turing pattern can be related to the network connection probability. The Turing instability region involving delay and network connection probability is obtained. Finally, the numerical simulation verifies our results.

Influence of Temperature and Noise on Subthreshold Signal Propagation in Feedforward Neural Network

Temperature is an important environmental factor that all creatures depend on. Under the appropriate temperature, the neural system shows good biological performance. Based on an improved Hodgkin-Huxley (HH) neuron model considering temperature and noise, the ten-layers pure excitatory feedforward neural network and the ten-layers excitatory-inhibitory (EI) neural network are constructed to study the subthreshold signal propagation. It’s found that increasing temperature can restrain the signal propagation, and raise the noises intensity threshold where the failed signal propagation can transform into succeed signal propagation. Under the large noise, the signal propagation in network in different temperatures exhibits different anti-noise capabilities. There exists the saturation value of interlayer connection probability, that is, the signal propagation maintains constant when interlayer connection probability beyond a certain value. Moreover, in EI network with large noise, the network’s intrinsic oscillation activity will completely cover subthreshold signal, and block the signal propagation. The jumping phenomenon in the value of fidelity, which measures the similarity between input signal and output signal, appears in both pure excitatory network and EI network. This paper provides potential value for understanding the regulation of both temperature and noise in information propagation in neural network.

Parcellation of the Hippocampus According to Its Connection Probability with Prefrontal Cortex Subdivisions in a Malaysian Malay Population: Preliminary Findings

Background: Lesion studies have shown distinct roles for the hippocampus, with the dorsal subregion being involved in processing of spatial information and memory, and the ventral aspect coding for emotion and motivational behaviour. However, its structural connectivity with the subdivisions of the prefrontal cortex (PFC), the executive area of the brain that also has various distinct functions, has not been fully explored, especially in the Malaysian population. Methods: We performed diffusion magnetic resonance imaging with probabilistic tractography on four Malay males to parcellate the hippocampus according to its relative connection probability to the six subdivisions of the PFC. Results: Our findings revealed that each hippocampus showed putative connectivity to all the subdivisions of PFC, with the highest connectivity to the orbitofrontal cortex (OFC). Parcellation of the hippocampus according to its connection probability to the six PFC subdivisions showed variability in the pattern of the connection distribution and no clear distinction between the hippocampal subregions. Conclusion: Hippocampus displayed highest connectivity to the OFC as compared to other PFC subdivisions. We did not find a unifying pattern of distribution based on the connectivity- based parcellation of the hippocampus.

Simulation scalability of large brain neuronal networks thanks to time asynchrony

We present here a new algorithm based on a random model for simulating efficiently large brain neuronal networks. Model parameters (mean firing rate, number of neurons, synaptic connection probability and postsynaptic duration) are easy to calibrate further on real data experiments. Based on time asynchrony assumption, both computational and memory complexities are proved to be theoretically linear with the number of neurons. These results are experimentally validated by sequential simulations of millions of neurons and billions of synapses in few minutes on a single processor desktop computer.

Scale production of conductive cotton yarns by sizing process and its conductive mechanism

Conductive yarn is an important component and connector of electronic and intelligent textiles, and with the development of high-performance and low-cost conductive yarns, it has attracted more attention. Herein, a simple, scalable sizing process was introduced to prepare the graphene-coated conductive cotton yarns. The electron conductive mechanism of fibers and yarns were studied by the percolation and binomial distribution theory, respectively. The conductive paths are formed due to the conductive fibers' contact with each other, and the results revealed that the connection probability of the fibers in the yarn (p) is proportional to the square of the fibers filling coefficient (φ) as p ∝ φ2. The calculation formula of the staple spun yarn resistance can be derived from this conclusion and verified by experiments, which further proves the feasibility of produce conductive cotton yarns by sizing process.

Emergent population activity in metric-free and metric networks of neurons with stochastic spontaneous spikes and dynamic synapses

We show that networks of excitatory neurons with stochastic spontaneous spiking activity and short-term synaptic plasticity can exhibit spontaneous repetitive synchronization in so-called population spikes. The major reason for this is that synaptic plasticity nonlinearly modulates the interaction between neurons. For large-scale two-dimensional networks, where the connection probability decreases exponentially with increasing distance between the neurons resulting in a small-world network connectome, a population spike occurs in the form of circular traveling waves diverging from seemingly non-stationary nucleation sites. The latter is in drastic contrast to the case of networks with a fixed fraction of steady pacemaker neurons, where the set of a few spontaneously formed nucleation sites is stationary. Despite the spatial non-stationarity of their nucleation, population spikes may occur surprisingly regularly. From a theoretical viewpoint, these findings show that the regime of nearly-periodic population spikes, which mimics respiratory rhythm, can occur strictly without stochastic resonance. In addition, the observed spatiotemporal effects serve as an example of transient chimera patterns.

Uniformly Most Reliable Three-Terminal Graph of Dense Graphs

A graph G with k specified target vertices in vertex set is a k -terminal graph. The k -terminal reliability is the connection probability of the fixed k target vertices in a k -terminal graph when every edge of this graph survives independently with probability p . For the class of two-terminal graphs with a large number of edges, Betrand, Goff, Graves, and Sun constructed a locally most reliable two-terminal graph for p close to 1 and illustrated by a counterexample that this locally most reliable graph is not the uniformly most reliable two-terminal graph. At the same time, they also determined that there is a uniformly most reliable two-terminal graph in the class obtained by deleting an edge from the complete graph with two target vertices. This article focuses on the uniformly most reliable three-terminal graph of dense graphs with n vertices and m edges. First, we give the locally most reliable three-terminal graphs of n and m in certain ranges for p close to 0 and 1. Then, it is proved that there is no uniformly most reliable three-terminal graph with specific n and m , where n ≥ 7 and n 2 − ⌊ n − 3 / 2 ⌋ ≤ m ≤ n 2 − 2 . Finally, some uniformly most reliable graphs are given for n vertices and m edges, where 4 ≤ n ≤ 6 and m = n 2 − 2 or n ≥ 5 and m = n 2 − 1 .

Connectivity and Synaptic Physiology in the Mouse and Human Cortex

We present a unique, extensive, public synaptic physiology dataset. The dataset contains over 20,000 neuron pairs probed with multipatch using standardized protocols to capture short-term dynamics. Recordings were made in the human temporal cortex and the adult mouse visual cortex. Our main purpose is to offer data and analyses that provide a more complete picture of the cortical microcircuit to the community. We also make several important findings that relate connectivity and synaptic properties to the major cell subclasses and cortical layer via the development of novel analysis methods for quantifying connectivity, synapse properties, and synaptic dynamics. We find that excitatory synaptic dynamics depend strongly on the postsynaptic cell subclass, whereas inhibitory synaptic dynamics depend on the presynaptic cell subclass. Despite these associations, short-term synaptic plasticity is heterogeneous in most subclass to subclass connections. We also find that intralaminar connection probability exhibits a strong layer dependence. In human cortex, we find that excitatory synapses are highly reliable, recover rapidly, and are distinct from mouse excitatory synapses.

Missing link prediction and spurious link detection based on attractive force and community

With the rapid development of Internet and information technology, networks have become an important media of information diffusion in the global. In view of the increasing scale of network data, how to ensure the completeness and accuracy of the obtainable links from networks has been an urgent problem that needs to be solved. Different from most traditional link prediction methods only focus on the missing links, a novel link prediction approach is proposed in this paper to handle both the missing links and the spurious links in networks. At first, we define the attractive force for any pair of nodes to denote the strength of the relation between them. Then, all the nodes can be divided into some communities according to their degrees and the attractive force on them. Next, we define the connection probability for each pair of unconnected nodes to measure the possibility if they are connected, the missing links can be predicted by calculating and comparing the connection probabilities of all the pairs of unconnected nodes. Moreover, we define the break probability for each pair of connected nodes to measure the possibility if they are broken, the spurious links can also be detected by calculating and comparing the break probabilities of all the pairs of connected nodes. To verify the validity of the proposed approach, we conduct experiments on some real-world networks. The results show the proposed approach can achieve higher prediction accuracy and more stable performance compared with some existing methods.

Propagation of spiking regularity in feedforward networks with recurrent connections

The regularity of the inter-spike intervals (ISIs) gives a critical window into how the information is coded temporally in the cortex. Previous researches mostly adopt pure feedforward networks (FFNs) to study how the network structure affects spiking regularity propagation, which ignore the role of local dynamics within the layer. In this paper, we construct an FFN with recurrent connections and investigate the propagation of spiking regularity. We argue that an FFN with recurrent connections serves as a basic circuit to explain that the regularity increases as spikes propagate from middle temporal visual areas to higher cortical areas. We find that the reduction of regularity is related to the decreased complexity of the shared activity co-fluctuations. We show in simulations that there is an appropriate excitation–inhibition ratio maximizing the regularity of deeper layers. Furthermore, it is demonstrated that collective temporal regularity in deeper layers exhibits resonance-like behavior with respect to both synaptic connection probability and synaptic weight. Our work provides a critical link between cortical circuit structure and realistic spiking regularity.