Biological networks as defense against adversarial attacks.

In recent years, more and more importance is given to interpretability in the ML field. The best known and most famous area in which the interpretability of a neural network is needed is that of cyber-security. The first paper to expose the potential issue is by Ian Goodfellow et al. 2014, in ”Intriguing properties of neural networks”, in which it is shown how an image, if altered in the right way, can be completely misclassified by a network trained to classify images. In this thesis I proposed a new method based on a hybrid network, i.e half biological and half artificial, in order to develop a neural network capable of resisting a lot of different adversarial attacks. The biological part is based on the hebbian-anti hebbian neural-dynamics, while the artificial one is based on probability and Boltzmann machines."In recent years, more and more importance is given to interpretability in the ML field. The best known and most famous area in which the interpretability of a neural network is needed is that of cyber-security. The first paper to expose the potential issue is by Ian Goodfellow et al. 2014, in ”Intriguing properties of neural networks”, in which it is shown how an image, if altered in the right way, can be completely misclassified by a network trained to classify images. In this thesis I proposed a new method based on a hybrid network, i.e half biological and half artificial, in order to develop a neural network capable of resisting a lot of different adversarial attacks. The biological part is based on the hebbian-anti hebbian neural-dynamics, while the artificial one is based on probability and Boltzmann machines.

An Architecture-Altering and Training Methodology for Neural Logic Networks: Application in the Banking Sector

Artificial neural networks have been universally acknowledged for their ability on constructing forecasting and classifying systems. Among their desirable features, it has always been the interpretation of their structure, aiming to provide further knowledge for the domain experts. A number of methodologies have been developed for this reason. One such paradigm is the neural logic networks concept. Neural logic networks have been especially designed in order to enable the interpretation of their structure into a number of simple logical rules and they can be seen as a network representation of a logical rule base. Although powerful by their definition in this context, neural logic networks have performed poorly when used in approaches that required training from data. Standard training methods, such as the back-propagation, require the network’s synapse weight altering, which destroys the network’s interpretability. The methodology in this paper overcomes these problems and proposes an architecture-altering technique, which enables the production of highly antagonistic solutions while preserving any weight-related information. The implementation involves genetic programming using a grammar-guided training approach, in order to provide arbitrarily large and connected neural logic networks. The methodology is tested in a problem from the banking sector with encouraging results

ITER: An algorithm for predictive regression rule extraction. Data warehousing and knowledge discovery. Proceedings.

Various benchmarking studies have shown that artificial neural networks and support vector machines have a superior performance when compared to more traditional machine learning techniques. The main resistance against these newer techniques is based on their lack of interpretability: it is difficult for the human analyst to understand the motivation behind these models' decisions. Various rule extraction techniques have been proposed to overcome this opacity restriction. However, most of these extraction techniques are devised for classification and only few algorithms can deal with regression problems.

Towards NeuroAI: Introducing Neuronal Diversity into Artificial Neural Networks

Throughout history, the development of artificial intelligence, particularly artificial neural networks, has been open to and constantly inspired by the increasingly deepened understanding of the brain, such as the inspiration of neocognitron, which is the pioneering work of convolutional neural networks. Per the motives of the emerging field: NeuroAI, a great amount of neuroscience knowledge can help catalyze the next generation of AI by endowing a network with more powerful capabilities. As we know, the human brain has numerous morphologically and functionally different neurons, while artificial neural networks are almost exclusively built on a single neuron type. In the human brain, neuronal diversity is an enabling factor for all kinds of biological intelligent behaviors. Since an artificial network is a miniature of the human brain, introducing neuronal diversity should be valuable in terms of addressing those essential problems of artificial networks such as efficiency, interpretability, and memory. In this Primer, we first discuss the preliminaries of biological neuronal diversity and the characteristics of information transmission and processing in a biological neuron. Then, we review studies of designing new neurons for artificial networks. Next, we discuss what gains can neuronal diversity bring into artificial networks and exemplary applications in several important fields. Lastly, we discuss the challenges and future directions of neuronal diversity to explore the potential of NeuroAI