The malleable brain: plasticity of neural circuits and behavior: A review from students to students

One of the most intriguing features of the brain is its ability to be malleable, allowing it to adapt continually to changes in the environment. Specific neuronal activity patterns drive long-lasting increases or decreases in the strength of synaptic connections, referred to as long-term potentiation (LTP) and long-term depression (LTD) respectively. Such phenomena have been described in a variety of model organisms, which are used to study molecular, structural, and functional aspects of synaptic plasticity. This review originated from the first International Society for Neurochemistry (ISN) and Journal of Neurochemistry (JNC) Flagship School held in Alpbach, Austria (Sep 2016), and will use its curriculum and discussions as a framework to review some of the current knowledge in the field of synaptic plasticity. First, we describe the role of plasticity during development and the persistent changes of neural circuitry occurring when sensory input is altered during critical developmental stages. We then outline the signaling cascades resulting in the synthesis of new plasticity-related proteins, which ultimately enable sustained changes in synaptic strength. Going beyond the traditional understanding of synaptic plasticity conceptualized by LTP and LTD, we discuss system-wide modifications and recently unveiled homeostatic mechanisms, such as synaptic scaling. Finally, we describe the neural circuits and synaptic plasticity mechanisms driving associative memory and motor learning. Evidence summarized in this review provides a current view of synaptic plasticity in its various forms, offers new insights into the underlying mechanisms and behavioral relevance, and provides directions for future research in the field of synaptic plasticity.Fil: Schaefer, Natascha. University of Wuerzburg; AlemaniaFil: Rotermund, Carola. University of Tuebingen; AlemaniaFil: Blumrich, Eva Maria. Universitat Bremen; AlemaniaFil: Lourenco, Mychael V.. Universidade Federal do Rio de Janeiro; BrasilFil: Joshi, Pooja. Robert Debre Hospital; FranciaFil: Hegemann, Regina U.. University of Otago; Nueva ZelandaFil: Jamwal, Sumit. ISF College of Pharmacy; IndiaFil: Ali, Nilufar. Augusta University; Estados UnidosFil: García Romero, Ezra Michelet. Universidad Veracruzana; MéxicoFil: Sharma, Sorabh. Birla Institute of Technology and Science; IndiaFil: Ghosh, Shampa. Indian Council of Medical Research; IndiaFil: Sinha, Jitendra K.. Indian Council of Medical Research; IndiaFil: Loke, Hannah. Hudson Institute of Medical Research; AustraliaFil: Jain, Vishal. Defence Institute of Physiology and Allied Sciences; IndiaFil: Lepeta, Katarzyna. Polish Academy of Sciences; ArgentinaFil: Salamian, Ahmad. Polish Academy of Sciences; ArgentinaFil: Sharma, Mahima. Polish Academy of Sciences; ArgentinaFil: Golpich, Mojtaba. University Kebangsaan Malaysia Medical Centre; MalasiaFil: Nawrotek, Katarzyna. University Of Lodz; ArgentinaFil: Paid, Ramesh K.. Indian Institute of Chemical Biology; IndiaFil: Shahidzadeh, Sheila M.. Syracuse University; Estados UnidosFil: Piermartiri, Tetsade. Universidade Federal de Santa Catarina; BrasilFil: Amini, Elham. University Kebangsaan Malaysia Medical Centre; MalasiaFil: Pastor, Verónica. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Biología Celular y Neurociencia ; ArgentinaFil: Wilson, Yvette. University of Melbourne; AustraliaFil: Adeniyi, Philip A.. Afe Babalola University; NigeriaFil: Datusalia, Ashok K.. National Brain Research Centre; IndiaFil: Vafadari, Benham. Polish Academy of Sciences; ArgentinaFil: Saini, Vedangana. University of Nebraska; Estados UnidosFil: Suárez Pozos, Edna. Instituto Politécnico Nacional; MéxicoFil: Kushwah, Neetu. Defence Institute of Physiology and Allied Sciences; IndiaFil: Fontanet, Paula. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Biología Celular y Neurociencia ; ArgentinaFil: Turner, Anthony J.. University of Leeds; Reino Unid

Learning Sensor Feedback Models from Demonstrations via Phase-Modulated Neural Networks

In order to robustly execute a task under environmental uncertainty, a robot needs to be able to reactively adapt to changes arising in its environment. The environment changes are usually reflected in deviation from expected sensory traces. These deviations in sensory traces can be used to drive the motion adaptation, and for this purpose, a feedback model is required. The feedback model maps the deviations in sensory traces to the motion plan adaptation. In this paper, we develop a general data-driven framework for learning a feedback model from demonstrations. We utilize a variant of a radial basis function network structure --with movement phases as kernel centers-- which can generally be applied to represent any feedback models for movement primitives. To demonstrate the effectiveness of our framework, we test it on the task of scraping on a tilt board. In this task, we are learning a reactive policy in the form of orientation adaptation, based on deviations of tactile sensor traces. As a proof of concept of our method, we provide evaluations on an anthropomorphic robot. A video demonstrating our approach and its results can be seen in https://youtu.be/7Dx5imy1KcwComment: 8 pages, accepted to be published at the International Conference on Robotics and Automation (ICRA) 201

Overlapping memory replay during sleep builds cognitive schemata

Sleep enhances integration across multiple stimuli, abstraction of general rules, insight into hidden solutions and false memory formation. Newly learned information is better assimilated if compatible with an existing cognitive framework or schema. This article proposes a mechanism by which the reactivation of newly learned memories during sleep could actively underpin both schema formation and the addition of new knowledge to existing schemata. Under this model, the overlapping replay of related memories selectively strengthens shared elements. Repeated reactivation of memories in different combinations progressively builds schematic representations of the relationships between stimuli. We argue that this selective strengthening forms the basis of cognitive abstraction, and explain how it facilitates insight and false memory formation

Recovering from External Disturbances in Online Manipulation through State-Dependent Revertive Recovery Policies

Robots are increasingly entering uncertain and unstructured environments. Within these, robots are bound to face unexpected external disturbances like accidental human or tool collisions. Robots must develop the capacity to respond to unexpected events. That is not only identifying the sudden anomaly, but also deciding how to handle it. In this work, we contribute a recovery policy that allows a robot to recovery from various anomalous scenarios across different tasks and conditions in a consistent and robust fashion. The system organizes tasks as a sequence of nodes composed of internal modules such as motion generation and introspection. When an introspection module flags an anomaly, the recovery strategy is triggered and reverts the task execution by selecting a target node as a function of a state dependency chart. The new skill allows the robot to overcome the effects of the external disturbance and conclude the task. Our system recovers from accidental human and tool collisions in a number of tasks. Of particular importance is the fact that we test the robustness of the recovery system by triggering anomalies at each node in the task graph showing robust recovery everywhere in the task. We also trigger multiple and repeated anomalies at each of the nodes of the task showing that the recovery system can consistently recover anywhere in the presence of strong and pervasive anomalous conditions. Robust recovery systems will be key enablers for long-term autonomy in robot systems. Supplemental info including code, data, graphs, and result analysis can be found at [1].Comment: 8 pages, 8 figures, 1 tabl

Creativity Training for Future Engineers: Preliminary Results from an Educative Experience

Due in part to the increased pace of cultural and environmental change, as well as increased competition due to globalization, innovation is become one of the primary concerns of the 21st century. We present an academic course designed to develop cognitive abilities related to creativity within an engineering education context, based on a conceptual framework rooted in cognitive sciences. The course was held at \'Ecole Polytechnique de Montr\'eal (\'EPM), a world renowned engineering school and a pillar in Canada's engineering community. The course was offered twice in the 2014-2015 academic year and more than 30 students from the graduate and undergraduate programs participated. The course incorporated ten pedagogical strategies, including serious games, an observation book, individual and group projects, etc., that were expected to facilitate the development of cognitive abilities related to creativity such as encoding, and associative analytical thinking. The CEDA (Creative Engineering Design Assessment) test was used to measure the students' creativity at the beginning and at the end of the course. Field notes were taken after each of the 15 three-hour sessions to qualitatively document the educative intervention along the semester and students gave anonymous written feedback after completing the last session. Quantitative and qualitative results suggest that an increase in creativity is possible to obtain with a course designed to development cognitive abilities related to creativity. Also, students appreciated the course, found it relevant, and made important, meaningful learnings regarding the creative process, its cognitive mechanism and the approaches available to increase it.Comment: 10 page

Challenges for identifying the neural mechanisms that support spatial navigation: the impact of spatial scale.

Spatial navigation is a fascinating behavior that is essential for our everyday lives. It involves nearly all sensory systems, it requires numerous parallel computations, and it engages multiple memory systems. One of the key problems in this field pertains to the question of reference frames: spatial information such as direction or distance can be coded egocentrically-relative to an observer-or allocentrically-in a reference frame independent of the observer. While many studies have associated striatal and parietal circuits with egocentric coding and entorhinal/hippocampal circuits with allocentric coding, this strict dissociation is not in line with a growing body of experimental data. In this review, we discuss some of the problems that can arise when studying the neural mechanisms that are presumed to support different spatial reference frames. We argue that the scale of space in which a navigation task takes place plays a crucial role in determining the processes that are being recruited. This has important implications, particularly for the inferences that can be made from animal studies in small scale space about the neural mechanisms supporting human spatial navigation in large (environmental) spaces. Furthermore, we argue that many of the commonly used tasks to study spatial navigation and the underlying neuronal mechanisms involve different types of reference frames, which can complicate the interpretation of neurophysiological data