Exponential multistability of memristive Cohen-Grossberg neural networks with stochastic parameter perturbations

© 2020 Elsevier Ltd. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/.Due to instability being induced easily by parameter disturbances of network systems, this paper investigates the multistability of memristive Cohen-Grossberg neural networks (MCGNNs) under stochastic parameter perturbations. It is demonstrated that stable equilibrium points of MCGNNs can be flexibly located in the odd-sequence or even-sequence regions. Some sufficient conditions are derived to ensure the exponential multistability of MCGNNs under parameter perturbations. It is found that there exist at least (w+2) l (or (w+1) l) exponentially stable equilibrium points in the odd-sequence (or the even-sequence) regions. In the paper, two numerical examples are given to verify the correctness and effectiveness of the obtained results.Peer reviewe

Finite-Time State Estimation for Delayed Neural Networks with Redundant Delayed Channels

10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 61703245 and 61873148); 10.13039/501100010029-Taishan Scholar Project of Shandong Province of China; 10.13039/501100002858-China Post-Doctoral Science Foundation (Grant Number: 2016M600547); Qingdao Post-Doctoral Applied Research Project (Grant Number: 2016117); Post-Doctoral Special Innovation Foundation of Shandong (Grant Number: 201701015); 10.13039/501100000288-Royal Society of the U.K.; 10.13039/100005156-Alexander von Humboldt Foundation of German

Memristor-Based HTM Spatial Pooler with On-Device Learning for Pattern Recognition

This article investigates hardware implementation of hierarchical temporal memory (HTM), a brain-inspired machine learning algorithm that mimics the key functions of the neocortex and is applicable to many machine learning tasks. Spatial pooler (SP) is one of the main parts of HTM, designed to learn the spatial information and obtain the sparse distributed representations (SDRs) of input patterns. The other part is temporal memory (TM) which aims to learn the temporal information of inputs. The memristor, which is an appropriate synapse emulator for neuromorphic systems, can be used as the synapse in SP and TM circuits. In this article, a memristor-based SP (MSP) circuit structure is designed to accelerate the execution of the SP algorithm. The presented MSP has properties of modeling both the synaptic permanence and the synaptic connection state within a single synapse, and on-device and parallel learning. Simulation results of statistic metrics and classification tasks on several real-world datasets substantiate the validity of MSP

Evoked Patterns of Oscillatory Activity in Mean-Field Neuronal Networks

Oscillatory behaviors in populations of neurons are oberved in diverse contexts. In tasks involving working memory, a form of short-term memory, oscillations in different frequency bands have been shown to increase across varying spatial scales using recording methods such as EEG (electroencephalogram) and MEG (magnetoencephalogram). Such oscillatory activity has also been observed in the context of neural binding, where different features of objects that are perceived or recalled are associated with one another. These sets of data suggest that oscillatory dynamics may also play a key role in the maintenance and manipulation of items in working memory. Using similar recording techniques, including EEG and MEG, oscillatory neuronal activity has also been seen to occur when certain images that cause aversion and headaches in healthy human subjects or seizures in those with pattern-sensitive epilepsy are presented. The images most likely to cause such responses are those with dominant spatial frequencies near 3--5 cycles per degree, the same band of wavenumbers to which normal human vision exhibits the greatest contrast sensitivity. We model these oscillatory behaviors using mean-field, Wilson-Cowan-type neuronal networks. In the case of working memory and binding, we find that including the activity of certain long-lasting excitatory synapses in addition to the usual inhibitory and shorter-term excitatory synaptic activity allows for bistability between a low steady state and a high oscillatory state. By coupling several such populations together, both in-phase and out-of-phase oscillations arise, corresponding to distinct and bound items in working memory, respectively. We analyze the network's dynamics and dependence on biophysically relevant parameters using a combination of techniques, including numerical bifurcation analysis and weak coupling theory. In the case of spatially resonant responses to static simtuli, we employ Wilson-Cowan networks extended in one and two spatial dimensions. By placing the networks near Turing-Hopf bifurcations, we find they exhibit spatial resonances that compare well with empirical results. Using simulations, numerical bifurcation analysis, and perturbation theory, we characterize the observed dynamics and gain mathematical insight into the mechanisms that lead to these dynamics

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task