The robot vibrissal system: Understanding mammalian sensorimotor co-ordination through biomimetics

Chapter 10 The Robot Vibrissal System: Understanding Mammalian Sensorimotor Co-ordination Through Biomimetics Tony J. Prescott, Ben Mitchinson, Nathan F. Lepora, Stuart P. Wilson, Sean R. Anderson, John Porrill, Paul Dean, Charles ..

What does the honeybee see? And how do we know?

This book is the only account of what the bee, as an example of an insect, actually detects with its eyes. Bees detect some visual features such as edges and colours, but there is no sign that they reconstruct patterns or put together features to form objects. Bees detect motion but have no perception of what it is that moves, and certainly they do not recognize “things” by their shapes. Yet they clearly see well enough to fly and find food with a minute brain. Bee vision is therefore relevant to the construction of simple artificial visual systems, for example for mobile robots. The surprising conclusion is that bee vision is adapted to the recognition of places, not things. In this volume, Adrian Horridge also sets out the curious and contentious history of how bee vision came to be understood, with an account of a century of neglect of old experimental results, errors of interpretation, sharp disagreements, and failures of the scientific method. The design of the experiments and the methods of making inferences from observations are also critically examined, with the conclusion that scientists are often hesitant, imperfect and misleading, ignore the work of others, and fail to consider alternative explanations. The erratic path to understanding makes interesting reading for anyone with an analytical mind who thinks about the methods of science or the engineering of seeing machines

Biological Intelligence: From Behavior to Learning Theory

Knowing how to learn, think, and act is not just a hallmark of intelligence, but a necessity of survival for many organisms. Behavior, the complete set of actions of species, allows us to glimpse into the minds of humans and animals, and by extension, intelligence itself. Biological intelligence is characterized by fast adaptation to changes and challenges, which is what allows species to survive in natural environments from starvation and predation. To study learning in a controlled setting, we can observe the behavior evoked through decision-making tasks that make it possible to quantify and analyze learning. By modeling the extracted behavioral features, we could start to understand the possible underlying mechanisms by proposing neural theory models, and look for those signals in the brain. Understanding the neural mechanisms of learning also strengthens the basis for building intelligent machines that are flexible and adaptive to the nonstationary world we live in. In this thesis, I present works in (1) automating behavioral setups and modeling suboptimal behavior in a traditional decision-making task, (2) using an ethological navigation task to characterize fast-sequence learning, and (3) how neural theory can explain some core behavioral phenomena in (2), and be used to solve a central problem in graph search.</p

A Review of Findings from Neuroscience and Cognitive Psychology as Possible Inspiration for the Path to Artificial General Intelligence

This review aims to contribute to the quest for artificial general intelligence by examining neuroscience and cognitive psychology methods for potential inspiration. Despite the impressive advancements achieved by deep learning models in various domains, they still have shortcomings in abstract reasoning and causal understanding. Such capabilities should be ultimately integrated into artificial intelligence systems in order to surpass data-driven limitations and support decision making in a way more similar to human intelligence. This work is a vertical review that attempts a wide-ranging exploration of brain function, spanning from lower-level biological neurons, spiking neural networks, and neuronal ensembles to higher-level concepts such as brain anatomy, vector symbolic architectures, cognitive and categorization models, and cognitive architectures. The hope is that these concepts may offer insights for solutions in artificial general intelligence.Comment: 143 pages, 49 figures, 244 reference

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task