Modeling efficient conjunction detection with spiking neural networks
نویسندگان
چکیده
The design of neural networks that are able to efficiently encode and detect conjunctions of features is an important open challenge that is also referred to as “the binding-problem”. We define a formal framework for neural nodes that process activity in the form of tuples of spike-trains which can efficiently encode and detect feature-conjunctions on a retinal input field in a position-invariant manner, also in the presence of multiple feature-conjunctions.
منابع مشابه
Improving the Izhikevich Model Based on Rat Basolateral Amygdala and Hippocampus Neurons, and Recognizing Their Possible Firing Patterns
Introduction: Identifying the potential firing patterns following different brain regions under normal and abnormal conditions increases our understanding of events at the level of neural interactions in the brain. Furthermore, it is important to be capable of modeling the potential neural activities to build precise artificial neural networks. The Izhikevich model is one of the simplest biolog...
متن کاملRobust Fault Detection on Boiler-turbine Unit Actuators Using Dynamic Neural Networks
Due to the important role of the boiler-turbine units in industries and electricity generation, it is important to diagnose different types of faults in different parts of boiler-turbine system. Different parts of a boiler-turbine system like the sensor or actuator or plant can be affected by various types of faults. In this paper, the effects of the occurrence of faults on the actuators are in...
متن کاملMatlab Model for Spiking Neural Networks
Spiking Neural Networks are the most realistic model compared to its biological counterpart. This paper introduces a MATLAB toolbox that is specifically designed for simulating spiking neural networks. The toolbox includes a set of functions that are useful for: creating and organizing the desired architecture; updating stimuli signals, adapting synapses and simulating the network; extracting a...
متن کاملSTRUCTURAL DAMAGE DETECTION BY MODEL UPDATING METHOD BASED ON CASCADE FEED-FORWARD NEURAL NETWORK AS AN EFFICIENT APPROXIMATION MECHANISM
Vibration based techniques of structural damage detection using model updating method, are computationally expensive for large-scale structures. In this study, after locating precisely the eventual damage of a structure using modal strain energy based index (MSEBI), To efficiently reduce the computational cost of model updating during the optimization process of damage severity detection, the M...
متن کاملSUBCLASS FUZZY-SVM CLASSIFIER AS AN EFFICIENT METHOD TO ENHANCE THE MASS DETECTION IN MAMMOGRAMS
This paper is concerned with the development of a novel classifier for automatic mass detection of mammograms, based on contourlet feature extraction in conjunction with statistical and fuzzy classifiers. In this method, mammograms are segmented into regions of interest (ROI) in order to extract features including geometrical and contourlet coefficients. The extracted features benefit from...
متن کامل