This paper presents Deep ARTMAP, a novel extension of the ARTMAP architecture that generalizes the self-consistent modular ART (SMART) architecture to enable hierarchical learning (supervised and unsupervised) across arbitrary transformations of data. The Deep ARTMAP framework operates as a divisive clustering mechanism, supporting an arbitrary number of modules with customizable granularity within each module. Inter-ART modules regulate the clustering at each layer, permitting unsupervised learning while enforcing a one-to-many mapping from clusters in one layer to the next. While Deep ARTMAP reduces to both ARTMAP and SMART in particular configurations, it offers significantly enhanced flexibility, accommodating a broader range of data transformations and learning modalities.

Two universal functional principles of Adaptive Resonance Theory simulate the brain code of all biological learning and adaptive intelligence. Low level representations of multisensory stimuli in their immediate environmental context are formed on the basis of bottom up activation and under the control of top down matching rules that integrate high level long term traces of contextual configuration. These universal coding principles lead to the establishment of lasting brain signatures of perceptual experience in all living species, from aplysiae to primates. They are revisited in this paper here on the basis of examples drawn from the original code and from some of the most recent related empirical findings on contextual modulation in the brain, highlighting the potential of Grossberg's pioneering insights and groundbreaking theoretical work for intelligent solutions in the domain of developmental and cognitive robotics.

We present an unsupervised deep learning model for 3D object classification. Conventional Hebbian learning, a well-known unsupervised model, suffers from loss of local features leading to reduced performance for tasks with complex geometric objects. We present a deep network with a novel Neuron Activity Aware (NeAW) Hebbian learning rule that dynamically switches the neurons to be governed by Hebbian learning or anti-Hebbian learning, depending on its activity. We analytically show that NeAW Hebbian learning relieves the bias in neuron activity, allowing more neurons to attend to the representation of the 3D objects. Empirical results show that the NeAW Hebbian learning outperforms other variants of Hebbian learning and shows higher accuracy over fully supervised models when training data is limited. Supervised deep networks for recognizing objects from 3D point clouds have demonstrated high accuracy but generally suffer from poor performance when labeled training data is limited (Wu et al., 2015; Qi et al., 2017a;b; Wang et al., 2019; Maturana & Scherer, 2015). On the other hand, self-supervised or unsupervised models can be trained without labeled data hence improving the performance in data efficient scenarios. Self-supervised learning methods have been studied for 3D object recognition mostly in an autoencoder setting, which necessarily reconstructs input to learn the representation (Achlioptas et al., 2018; Girdhar et al., 2016). Unsupervised learning has also been applied to pre-process the input for an encoder but still largely relying on supervised learning (Li et al., 2018). Conventionally, self-organizing maps and growing neural gas have been used as fully unsupervised learning for 3D objects while they aim to reconstruct the surface of the objects (do Rêgo et al., 2007; Mole & Araújo, 2010). A fully unsupervised deep network for 3D object classification has rarely been studied. Unsupervised Hebbian learning is known to offer attractive advantages such as data efficiency, noise robustness, and adaptability for various applications (Najarro & Risi, 2020; Kang et al., 2022; Miconi et al., 2018; Zhou et al., 2022). The basic Hebbian and anti-Hebbian learning refer to that synaptic weight is strengthened and weakened, respectively, when pre-and post-synaptic neurons are simultaneously activated (Hebb, 2005). Many past efforts have developed variants of Hebb's rule.

We introduce an exploration bonus for deep reinforcement learning methods calculated using self-organising feature maps. Our method uses adaptive resonance theory (ART) providing online, unsupervised clustering to quantify the novelty of a state. This heuristic is used to add an intrinsic reward to the extrinsic reward signal for then to optimize the agent to maximize the sum of these two rewards. We find that this method was able to play the game Ordeal at a human level after a comparable number of training epochs to ICM arXiv:1705.05464. Agents augmented with RND arXiv:1810.12894 were unable to achieve the same level of performance in our space of hyperparameters.

I co-authored a book that claims consciousness has been "solved". One of the greatest neuroscientists of our generation who is largely ignored within the field and unknown outside has conclusively put this thousand-year mystery to rest after sixty-five years of work. Many are skeptical of this claim, as you might guess. This article is not another attempt to convince the skeptics. Instead, it is to help understand why it is hard for us to believe we have an answer to the mystery of consciousness. It is to help understand why understanding consciousness is more important now -- at the dawn of the AI age -- than ever before in the history of humanity.

But according to IEEE Spectrum the inability of a typical deep learning program to perform well on more than one task, for example, severely limits the application of the technology to specific tasks in rigidly controlled environments. More seriously deep learning is untrustworthy because it is not explainable -- and unsuitable for some applications because it can experience catastrophic forgetting. If the algorithm works, it may be impossible to fully understand why. And while the tool is slowly learning a new database, an arbitrary part of its learned memories can suddenly collapse. Therefore, it might be risky to use deep learning on any life-or-death application, such as a medical one.

The creation of machine learning algorithms for intelligent agents capable of continuous, lifelong learning is a critical objective for algorithms being deployed on real-life systems in dynamic environments. Here we present an algorithm inspired by neuromodulatory mechanisms in the human brain that integrates and expands upon Stephen Grossberg\'s ground-breaking Adaptive Resonance Theory proposals. Specifically, it builds on the concept of uncertainty, and employs a series of neuromodulatory mechanisms to enable continuous learning, including self-supervised and one-shot learning. Algorithm components were evaluated in a series of benchmark experiments that demonstrate stable learning without catastrophic forgetting. We also demonstrate the critical role of developing these systems in a closed-loop manner where the environment and the agent\'s behaviors constrain and guide the learning process. To this end, we integrated the algorithm into an embodied simulated drone agent. The experiments show that the algorithm is capable of continuous learning of new tasks and under changed conditions with high classification accuracy (greater than 94 percent) in a virtual environment, without catastrophic forgetting. The algorithm accepts high dimensional inputs from any state-of-the-art detection and feature extraction algorithms, making it a flexible addition to existing systems. We also describe future development efforts focused on imbuing the algorithm with mechanisms to seek out new knowledge as well as employ a broader range of neuromodulatory processes.

This editorial summarizes selected key contributions of Prof. Stephen Grossberg and describes the papers in this 80th birthday special issue in his honor. His productivity, creativity, and vision would each be enough to mark a scientist of the first caliber. In combination, they have resulted in contributions that have changed the entire discipline of neural networks. Grossberg has been tremendously influential in engineering, dynamical systems, and artificial intelligence as well. Indeed, he has been one of the most important mentors and role models in my career, and has done so with extraordinary generosity and encouragement. All authors in this special issue have taken great pleasure in hereby commemorating his extraordinary career and contributions.

This paper illustrates how we create a software agent by employing FALCON, a self-organizing neural network that performs reinforcement learning, to play a well-known first person shooter computer game known as Unreal Tournament 2004. Through interacting with the game environment and its opponents, our agent learns in real-time without any human intervention. Our agent bot participated in the 2K Bot Prize competition, similar to the \emph{Turing test} for intelligent agents, wherein human judges were tasked to identify whether their opponents in the game were human players or virtual agents. To perform well in the competition, an agent must act like human and be able to adapt to some changes made to the game. Although our agent did not emerge top in terms of human-like, the overall performance of our agent was encouraging as it acquired the highest game score while staying convincing to be human-like in some judges' opinions.

Conditioning experiments probe the ways that animals make predictions aboutrewards and punishments and use those predictions to control their behavior. One standard model of conditioning paradigms which involve many conditioned stimuli suggests that individual predictions should be added together. Various key results show that this model fails in some circumstances, and motivate analternative model, in which there is attentional selection between different available stimuli. The new model is a form of mixture of experts, has a close relationship with some other existing psychologicalsuggestions, and is statistically well-founded.