Establishing the flow of information between two bio-inspired spiking neural networks

The stationarity control of the average links for the Hebb complex dynamical network via external stimulus signals

The model of complex dynamical network (CDN) can be represented as the mathematic graph, in which some characteristics may emerge from the dynamic nodes group (NG) and links group (LG). This paper primarily focuses on the feature appearing from the dynamic links. The average link weight (ALW), as a novel quantitative index to describe the characteristic of dynamic links is introduced. Inspired by the Hebb's neuroscience theory, the Hebb complex dynamical network (HCDN) is constructed. The ALW of the HCDN can track a given target via external stimulus signals with adaptive amplifiers' proportional coefficients. In other words, the stationary network implies the ALW is a constant in time. Finally, two simulation examples are performed to validate the proposed adaptive update law's effectiveness.

Recognizing intertwined patterns using a network of spiking pattern recognition platforms

Artificial intelligence computing adapted from biology is a suitable platform for the development of intelligent machines by imitating the functional mechanisms of the nervous system in creating high-level activities such as learning, decision making and cognition in today's systems. Here, the concentration is on improvement the cognitive potential of artificial intelligence network with a bio-inspired structure. In this regard, four spiking pattern recognition platforms for recognizing digits and letters of EMNIST, patterns of YALE, and ORL datasets are proposed. All networks are developed based on a similar structure in the input image coding, model of neurons (pyramidal neurons and interneurons) and synapses (excitatory AMPA and inhibitory GABA currents), and learning procedure. Networks 1-4 are trained on Digits, Letters, faces of YALE and ORL, respectively, with the proposed un-supervised, spatial-temporal, and sparse spike-based learning mechanism based on the biological observation of the brain learning. When the networks have reached the highest recognition accuracy in the relevant patterns, the main goal of the article, which is to achieve high-performance pattern recognition system with higher cognitive ability, is followed. The pattern recognition network that is able to detect the combination of multiple patterns which called intertwined patterns has not been discussed yet. Therefore, by integrating four trained spiking pattern recognition platforms in one system configuration, we are able to recognize intertwined patterns. These results are presented for the first time and could be the pioneer of a new generation of pattern recognition networks with a significant ability in smart machines.

Information Transmitted From Bioinspired Neuron-Astrocyte Network Improves Cortical Spiking Network's Pattern Recognition Performance

We trained two spiking neural networks (SNNs), the cortical spiking network (CSN) and the cortical neuron-astrocyte network (CNAN), using a spike-based unsupervised method, on the MNIST and alpha-digit data sets and achieve an accuracy of 96.1% and 77.35%, respectively. We then connected CNAN to CSN by preserving maximum synchronization between them thanks to the concept of prolate spheroidal wave functions (PSWF). As a result, CSN receives additional information from CNAN without retraining. The important outcome is that CSN reaches 70.57% correct classification rate on capital letters without being trained on them. The overall contribution of transfer is 87.47%. We observed that for CSN the classifying neurons that relate to digits 0-9 of the alpha-digit data set are completely supported by the ones that relate to digits 0-9 of the MNIST data set. This means that CSN recognizes the similarity between the digits of the MNIST and alpha-digit data sets and classifies each digit of both data sets in the same class.