The Department of Biology at Washington University in St. Louis seeks a neuroscientist for a tenure-track position at the Assistant or Associate Professor level. The successful candidate will establish a research program focused on cutting-edge questions in developmental, cellular or systems neuroscience with particular interest in neuroethology, biologically-inspired artificial intelligence, evolution, or neural computation. The successful candidate will: join a vibrant neuroscience community; contribute to advising, mentoring, and teaching; and develop an externally funded and internationally recognized research program.

Vector-space models are used frequently to compare similarity and dimensionality among entity concepts. What happens when we apply these models to relational concepts? What is the evidence that such models do apply to relational concepts? If we use such a model, then one implication is that maximizing surface feature variation should improve relational concept learning. For example, in STEM instruction, the effectiveness of teaching by analogy is often limited by students’ focus on superficial features of the source and target exemplars. However, in contrast to the prediction of the vector-space computational model, the strategy of progressive alignment (moving from perceptually similar to different targets) has been suggested to address this issue (Gentner & Hoyos, 2017), and human behavioral evidence has shown benefits from progressive alignment. Here I will present some preliminary data that supports the computational approach. Participants were explicitly instructed to match stimuli based on relations while perceptual similarity of stimuli varied parametrically. We found that lower perceptual similarity reduced accurate relational matching. This finding demonstrates that perceptual similarity may interfere with relational judgements, but also hints at why progressive alignment maybe effective. These are preliminary, exploratory data and I to hope receive feedback on the framework and to start a discussion in a group on the utility of vector-space models for relational concepts in general.

Analogies between old and new concepts are common during classroom instruction. While previous studies of transfer focus on how features of initial learning guide later transfer to new problem solving, less is known about how to best support analogical transfer from previous learning while children are engaged in new learning episodes. Such research may have important implications for teaching and learning in mathematics, which often includes analogies between old and new information. Some existing research promotes supporting learners' explicit connections across old and new information within an analogy. In this talk, I will present evidence that instructors can invite implicit analogical reasoning through warm-up activities designed to activate relevant prior knowledge. Warm-up activities "close the transfer space" between old and new learning without additional direct instruction.

Vocabulary learning applications like Duolingo have millions of users around the world, but yet are based on very simple heuristics to choose teaching material to provide to their users. In this presentation, we will discuss the possibility to develop more advanced artificial teachers, which would be based on modeling of the learner’s inner characteristics. In the case of teaching vocabulary, understanding how the learner memorizes is enough. When it comes to picking grammar exercises, it becomes essential to assess how the learner reasons, in particular by analogy. This second application will illustrate how analogical and case-based reasoning can be employed in an alternative way in education: not as the teaching algorithm, but as a part of the learner’s model.

Extensive research on science teaching has shown the effectiveness of analogies as a didactic tool which, when appropriately and effectively used, facilitates the learning process of abstract concepts. This seminar does not contradict the efficacy of such a didactic use of analogies in this seminar but switches attention and interest on their heuristic use in approaching and understanding of what previously unknown. Such a use of analogies derives from research with 10 to 17 year-olds, who, when asked to make predictions in novel situations and to then provide explanations about these predictions, they self-generated analogies and used them by reasoning on their basis. This heuristic use of analogies can be used in science teaching in revealing how students approach situations they have not considered before as well as the sources they draw upon in doing so.

The cerebellum resembles a feedforward, three-layer network of neurons in which the “hidden layer” consists of Purkinje cells (P-cells) and the output layer consists of deep cerebellar nucleus (DCN) neurons. In this analogy, the output of each DCN neuron is a prediction that is compared with the actual observation, resulting in an error signal that originates in the inferior olive. Efficient learning requires that the error signal reach the DCN neurons, as well as the P-cells that project onto them. However, this basic rule of learning is violated in the cerebellum: the olivary projections to the DCN are weak, particularly in adulthood. Instead, an extraordinarily strong signal is sent from the olive to the P-cells, producing complex spikes. Curiously, P-cells are grouped into small populations that converge onto single DCN neurons. Why are the P-cells organized in this way, and what is the membership criterion of each population? Here, I apply elementary mathematics from machine learning and consider the fact that P-cells that form a population exhibit a special property: they can synchronize their complex spikes, which in turn suppress activity of DCN neuron they project to. Thus complex spikes cannot only act as a teaching signal for a P-cell, but through complex spike synchrony, a P-cell population may act as a surrogate teacher for the DCN neuron that produced the erroneous output. It appears that grouping of P-cells into small populations that share a preference for error satisfies a critical requirement of efficient learning: providing error information to the output layer neuron (DCN) that was responsible for the error, as well as the hidden layer neurons (P-cells) that contributed to it. This population coding may account for several remarkable features of behavior during learning, including multiple timescales, protection from erasure, and spontaneous recovery of memory.

Studies reported the effects of different types and different levels of abstraction of analogical stimuli on designers. However, specific, single visual analogical stimuli on the effects of designers have not been reported. We define this type of stimuli as specific analogical stimuli. We used the extended linkography method to analyze the facilitating and limiting effects of specific analogical stimuli and free association analogical stimuli (nonspecific analogical stimuli) on the students' creativity at different design levels. Through an empirical study, we explored the differences in the effects of specific analogy stimuli on the students at different design levels. It clarifies the use of analogical stimuli in design and the teaching of analogical design methods in design education.

Join the Brain Conference on 24-25 February 2022 for the opportunity to hear from neurology’s leading scientists and clinicians. The two-day virtual programme features clinical teaching talks and research presentations from expert speakers including neuroscientist Professor Gina Poe, and the winner of the 2021 Brain Prize, neurologist Professor Peter Goadsby." "Tickets for The Brain Conference 2022 cost just £30, but register with promotional code BRAINCONEM20 for a discounted rate of £25.

Join the Brain Conference on 24-25 February 2022 for the opportunity to hear from neurology’s leading scientists and clinicians. The two-day virtual programme features clinical teaching talks and research presentations from expert speakers including neuroscientist Professor Gina Poe, and the winner of the 2021 Brain Prize, neurologist Professor Peter Goadsby." "Tickets for The Brain Conference 2022 cost just £30, but register with promotional code BRAINCONEM20 for a discounted rate of £25.

While classical industrial robots are mostly designed for repetitive tasks, assistive robots will be challenged by a variety of different tasks in close contact with humans. Hereby, learning through the direct interaction with humans provides a potentially powerful tool for an assistive robot to acquire new skills and to incorporate prior human knowledge during the exploration of novel tasks. Moreover, an intuitive interactive teaching process may allow non-programming experts to contribute to robotic skill learning and may help to increase acceptance of robotic systems in shared workspaces and everyday life. In this talk, I will discuss recent research I did on interactive robot skill learning and the remaining challenges on the route to human-centered teaching of assistive robots. In particular, I will also discuss potential connections and overlap with cognitive science. The presented work covers learning a library of probabilistic movement primitives from human demonstrations, intention aware adaptation of learned skills in shared workspaces, and multi-channel interactive reinforcement learning for sequential tasks.

Join us to hear about degrees and careers in neuroscience, what it’s like to be a neuroscientist, the wide range of career options open to you after a neuroscience degree, first-hand examples of career paths in neuroscience, and some tips and thoughts to help you in your own careers. This free and friendly webinar will give you the chance to ask questions from people with different experiences in neuroscience: - Emma Soopramanien, the BNA Committee Representative for Students and Early Career Researchers – Emma has just completed her undergraduate course in neuroscience, and will be hosting the webinar. - Professor Anthony Isles, BNA Trustee – Anthony is a professor at Cardiff University, where he researches epigenetic mechanisms of brain and behaviour and how they contribute to neurodevelopmental and neuropsychiatric disorders, as well as teaching undergraduate and postgraduate students. He will talk about how he came to be a neuroscientist researcher and ways into neuroscience. - Dr Anne Cooke, BNA Chief Executive – Anne studied physiology and neuroscience at university and carried out research into neuronal communication, before then following a career path with roles in academia and industry, and now as CE at the BNA. Anne will describe her own career in neuroscience, as well as some of the many other options open to you after a neuroscience degree.

The public has a fascination with the brain, but little attention is given to neuroscience education prior to graduate studies in brain-related fields. One reason may be the lack of low cost and engaging teaching materials. To address this, we have developed a suite of open-source tools which are appropriate for amateurs and for use in high school, undergraduate, and graduate level educational and research programs. This lecture will provide an overview of our mission to re-engineer research-grade lab equipment using first principles and will highlight basic principles of neuroscience in a "DIY" fashion: neurophysiology, functional electrical stimulation, micro-stimulation effect on animal behavior, neuropharmacology, even neuroprosthesis and optogenetics! Finally, with faculty academic positions becoming a scarce resource, I will discuss an alternative academic career path: entrepreneurship. It is possible to be an academic, do research, publish papers, present at conferences and train students all outside the traditional university setting. I will close by discussing my career path from graduate student to PI/CEO of a startup neuroscience company.