5/18/2023 0 Comments Xor gate logic world prolblem![]() Results obtained confirm the validity of the approach. In this paper, we present how SNN can be applied with efficacy in image segmentation and edge detection. Moreover, SNNs add a new dimension, the temporal axis, to the representation capacity and the processing abilities of neural networks. Based on dynamic event-driven processing, they open up new horizons for developing models with an exponential capacity of memorizing and a strong ability to fast adaptation. SNNs overcome the computational power of neural networks made of threshold or sigmoidal units. They derive their strength and interest from an accurate modeling of synaptic interactions between neurons, taking into account the time of spike emission. Spiking neuron networks (SNNs) are often referred to as the third generation of neural networks which have potential to solve problems related to biological stimuli. First two generations of neural networks have a lot of successful applications. Artificial neural networks have been well developed so far. The process of segmenting images is one of the most critical ones in automatic image analysis whose goal can be regarded as to find what objects are present in images. A MATLAB code is available as a supplementary material. Simulations were carried out and the results are in agreement with the hypothesis presented. NAC can explain the following points: 1) how neuron groups represent things and states 2) how they retain binary states in memories that do not require any plasticity mechanism and 3) how branching, disbanding, and interaction among assemblies may result in algorithms and behavioral responses. In this sense, neural assembly computing (NAC) can be seen as a new class of spiking neural network machines. Computing and algorithms are used here as in a nonstandard computation approach. Hence, such capabilities and the interaction among assemblies allow neural networks to create and control hierarchical cascades of causal activities, giving rise to parallel algorithms. In addition, assemblies can branch and dismantle other neural groups generating new events that trigger other coalitions. Such bistable neural assemblies become short- or long-term memories that represent the event that triggers them. Neural coalitions can reverberate, becoming bistable loops. It is described how neural groups perform statistic logic functions as they form assemblies. If these two inputs, A and B are both at logic level 1 or both at logic level 0 the output is a 0 making the gate an. It is shown how neural coalitions represent things (and world states), memorize them, and control their hierarchical relations in order to perform algorithms. PDF It give details of different type of logic gates: AND, OR, NOT, NOR, NAND, XOR, XNOR gates Find, read and cite all the research you need on ResearchGate. The truth table above shows that the output of an Exclusive-OR gate ONLY goes HIGH when both of its two input terminals are at DIFFERENT logic levels with respect to each other. A way by which neural assemblies compute is proposed in this paper. This is a prevalent notion, although the mechanisms are not yet understood. Spiking neurons can realize several computational operations when firing cooperatively.
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