Nikolas Andreakos is a PhD candidate at the University of Lincoln, who is working on developing computational models of associative memory formation and recognition in the mammalian hippocampus.

Recently Nikolas attended the 13th International Conference on Brain Informatics (BI2020). Due to the current travel restrictions, this year’s conference, which was scheduled to take place on 19th September 2020 in Padova, Italy, was moved online.

About Brain Informatics 2020

The Brain Informatics (BI) conference series has established itself as the world’s premier research forum on Brain Informatics, which is an emerging interdisciplinary and multidisciplinary research field with joint efforts from neuroscience, cognitive science, medicine and life sciences, data science, artificial intelligence, neuroimaging technologies, and information and communication technologies.

The 13th International Conference on Brain Informatics (BI2020) provided a premier international forum to bring together researchers and practitioners from diverse fields for presentation of original research results, as well as exchange and dissemination of innovative and practical development experiences on Brain Informatics research, brain-inspired technologies and brain/mental health applications.

The theme of BI2020 was: Brain Informatics in the Virtual World.

The BI2020 solicits high-quality original research and application papers (both full paper and abstract submissions). Relevant topics included but were not limited to:

• Track 1: Cognitive and Computational Foundations of Brain Science
• Track 2: Human Information Processing Systems
• Track 3: Brain Big Data Analytics, Curation and Management
• Track 4: Informatics Paradigms for Brain and Mental Health Research
• Track 5: Brain-Machine Intelligence and Brain-Inspired Computing

Nikolas presented his research Andreakos N., Yue S., Cutsuridis V. (2020) Recall Performance Improvement in a Bio-Inspired Model of the Mammalian Hippocampus. In: Mahmud M., Vassanelli S., Kaiser M.S., Zhong N. (eds) Brain Informatics. BI 2020. Lecture Notes in Computer Science, vol 12241. Springer, Cham. https://doi.org/10.1007/978-3-030-59277-6_29.

Abstract

Mammalian hippocampus is involved in short-term formation of declarative memories. We employed a bio-inspired neural model of hippocampal CA1 region consisting of a zoo of excitatory and inhibitory cells. Cells’ firing was timed to a theta oscillation paced by two distinct neuronal populations exhibiting highly regular bursting activity, one tightly coupled to the trough and the other to the peak of theta. To systematically evaluate the model’s recall performance against number of stored patterns, overlaps and ‘active cells per pattern’, its cells were driven by a non-specific excitatory input to their dendrites. This excitatory input to model excitatory cells provided context and timing information for retrieval of previously stored memory patterns. Inhibition to excitatory cells’ dendrites acted as a non-specific global threshold machine that removed spurious activity during recall. Out of the three models tested, ‘model 1’ recall quality was excellent across all conditions. ‘Model 2’ recall was the worst. The number of ‘active cells per pattern’ had a massive effect on network recall quality regardless of how many patterns were stored in it. As ‘active cells per pattern’ decreased, network’s memory capacity increased, interference effects between stored patterns decreased, and recall quality improved. Key finding was that increased firing rate of an inhibitory cell inhibiting a network of excitatory cells has a better success at removing spurious activity at the network level and improving recall quality than increasing the synaptic strength of the same inhibitory cell inhibiting the same network of excitatory cells, while keeping its firing rate fixed.

When asked about his experience, Nikolas said:

“I really enjoyed the conference and learned a lot. It was a valuable and absorbing experience for me. The atmosphere was friendly. I shared my research and my experience with other attendants, and exchange ideas which would help me to improve my existing work”.

Nikolas Andreakos is a PhD candidate at the University of Lincoln who is working on developing computational models of associative memory formation and recognition in the mammalian hippocampus.

Recently, Nikolas attended the 9^th International Conference on Biomimetic and Biohybrid Systems, Living Machines 2020 (LM2020). This year’s conference which was scheduled to take place between 28^th and 30^th July 2020 in Freiburg, Germany, but due to the current situation around Covid-19, it was moved online.

Andreakos N., Yue S., Cutsuridis V. (2020) Improving Recall in an Associative Neural Network Model of the Hippocampus. In: Vouloutsi V., Mura A., Tauber F., Speck T., Prescott T.J., Verschure P.F.M.J. (eds) Biomimetic and Biohybrid Systems. Living Machines 2020. Lecture Notes in Computer Science, vol 12413. Springer, Cham. https://doi.org/10.1007/978-3-030-64313-3_1

ABOUT LIVING MACHINES 2020

The development of future real-world technologies will depend strongly on our understanding and harnessing of the principles underlying living systems and the flow of communication signals between living and artificial systems.

Biomimetics is the development of novel technologies through the distillation of principles from the study of biological systems. The investigation of biomimetic systems can serve two complementary goals. First, a suitably designed and configured biomimetic artefact can be used to test theories about the natural system of interest. Second, biomimetic technologies can provide useful, elegant and efficient solutions to unsolved challenges in science and engineering.

Biohybrid systems are formed by combining at least one biological component—an existing living system—and at least one artificial, newly-engineered component. By passing information in one or both directions, such a system forms a new hybrid bio-artificial entity. The theme of the conference also encompasses biomimetic methods for manufacture, repair and recycling inspired by natural processes such as reproduction, digestion, morphogenesis andmetamorphosis.

The following are some examples of “Living Machines” as featured at past conferences:

• Biomimetic robots and their component technologies (sensors, actuators, processors) that can intelligently interact with their environments.
• Biomimetic computers neuromimetic emulations of the physiological basis for intelligent behaviour.
• Active biomimetic materials and structures that self-organise and self-repair or show other bio-inspired functions.
• Nature inspired designs and manufacturing processes.
• Biohybrid brain-machine interfaces and neural implants.
• Artificial organs and body-parts including sensory organ-chip hybrids and intelligent prostheses.
• Organism-level biohybrids such as robot-animal or robot-human systems.

Nikolas presented his research Improving recall in an associative neural network model of the hippocampus.

Abstract

The mammalian hippocampus is involved in auto-association and hetero-association of declarative memories. We employed a bio-inspired neural model of hippocampal CA1 region to systematically evaluate its mean recall quality against different number of stored patterns, overlaps and active cells per pattern. Model consisted of excitatory (pyramidal cells) and four types of inhibitory cells: axo-axonic, basket, bistratified, and oriens lacunosum-moleculare cells. Cells were simplified compartmental models with complex ion channel dynamics. Cells’ firing was timed to a theta oscillation paced by two distinct neuronal populations exhibiting highly regular bursting activity, one tightly coupled to the trough and the other to the peak of theta. During recall excitatory input to network excitatory cells provided context and timing information for retrieval of previously stored memory patterns. Dendritic inhibition acted as a non-specific global threshold machine that removed spurious activity during recall. Simulations showed recall quality improved when the network’s memory capacity increased as the number of active cells per pattern decreased. Furthermore, increased firing rate of a presynaptic inhibitory threshold machine inhibiting a network of postsynaptic excitatory cells has a better success at removing spurious activity at the network level and improving recall quality than increased synaptic efficacy of the same threshold machine on the same network of excitatory cells, while keeping its firing rate fixed.

Fang Lei is a PhD Scholar at the University of Lincoln who is currently carrying out a 12 month ULTRACEPT secondment at project partner Guangzhou University, China. Fang is involved with Work Packages 1 & 2.

Fang Lei attended the IEEE World Congress on Computational Intelligence (WCCI) 2020. Originally due to take place in Glasgow between 19^th and 24^th July 2020, the conference was moved online due to Covid-19.

The WCCI is the world’s largest technical event on computational intelligence and features three conferences from the IEEE Computational Intelligence Society (CIS), the 2020 International Joint Conference on Neural Networks (IJCNN 2020), the 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2020), and the 2020 IEEE Conference on Evolutionary Computation (IEEE CEC 2020).

IEEE WCCI 2020 covered topics in the field of neural networks, from biological networks to artificial computation and was attended by more than 2,350 record attendees from over 75 countries. The event schedule included:

• Public lecture by Yoshua Bengio on the topic of artificial neural networks and deep learning 2.0
• 4 Plenary Speeches by world-renowned scholars: Barbara Hammer, Kay Chen Tan, Carlos Coello Coello, and Jim Bezdek
• 15 Keynotes by top-notch researchers, 5 per Conference
• 4 cutting-edge Panel sessions
• 36 Tutorials
• 10 Workshops
• 170 Special Sessions including 61 for IJCNN, 42 For IEEE CEC, 37 For FUZZ-IEEE, and 30 Cross-Disciplinary sessions
• 13 Challenging and contemporary competitions

Fang Lei presented her research Competition between ON and OFF Neural Pathways Enhancing Collision Selectivity which has been published by the IEEE:

F. Lei, Z. Peng, V. Cutsuridis, M. Liu, Y. Zhang and S. Yue, “Competition between ON and OFF Neural Pathways Enhancing Collision Selectivity,” 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, United Kingdom, 2020, pp. 1-8, doi: 10.1109/IJCNN48605.2020.9207131.

Abstract

The LGMD1 neuron of locusts shows strong looming-sensitive property for both light and dark objects. Although a few LGMD1 models have been proposed, they are not reliable to inhibit the translating motion under certain conditions compare to the biological LGMD1 in the locust. To address this issue, we propose a bio-plausible model to enhance the collision-selectivity by inhibiting the translating motion. The proposed model contains three parts, the retina to lamina layer for receiving luminance change signals, the lamina to medulla layer for extracting motion cues via ON and OFF pathways separately, the medulla to lobula layer for eliminating translational excitation with neural competition. We tested the model by synthetic stimuli and real physical stimuli. The experimental results demonstrate that the proposed LGMD1 model has a strong preference for objects in direct collision course-it can detect looming objects in different conditions while completely ignoring translating objects.

When asked about her experience of the conference, Fang Lei said…

“Although I hoped to attend the conference physically, I was still excited as it was my first time attending the international conference. It started at midnight in China due to the time difference but I was eager to share my research with academic peers and share this experience with them.

I was asked to present at the conference and did so via a pre-uploaded video. I presented within the visual system session of IJCNN regular sessions. The presentation went very smoothly and we discussed problems with presenting papers by asking and answering questions.

I found the conference to be an interesting and meaningful experience for me. It was good to be able to spread our work to peers and gain knowledge of the work others are doing. The only thing I wish was that the conference was face-to-face.”

University of Lincoln researcher Hamid Isakhani returned to China to continue his ULTRACEPT secondment at Huazhong University of Science and Technology in June of 2020. Due to the unprecedented Covid situation, Hamid was required to quarantine for 14 days in the Guangu Hotel. During this time, he virtually attended the Ubiquitous Robots (UR) 2020 Conference held in Kyoto, Japan.

Like many recent conferences, this was UR’s first ever virtual event. Organised by the Korea Robotics Society and co-sponsored by the IEEE Robotics and Automation Society, UR2020 brought together scientists and engineers across the world who are at the forefront of robotics and automation. The week-long conference held 22-26 June 2020 comprised a variety of Keynote speeches, workshops and break-out sessions. To read more about the conference you can visit the Ubiquitous Robots 2020 website, here.

Hamid presented his paper H. Isakhani, C. Xiong, S. Yue and W. Chen, “A Bioinspired Airfoil Optimization Technique Using Nash Genetic Algorithm,” 2020 17th International Conference on Ubiquitous Robots (UR), Kyoto, Japan, 2020, pp. 506-513, doi: 10.1109/UR49135.2020.9144868.

This paper was nominated for the URA 2020 Best Paper Award.

Here is what Hamid had to say about the experience:

“2020 has been no ordinary from day one. Plane crashes, earth quakes, forest fires and now the devastating pandemic has certainly changed all of our lives for good or bad (reader’s perception). What is important is that we learn, adapt, and live on. Although some of our decisions are better than the others; like the AERO 2020 conference in France being postponed by over a year due to the pandemic compelling us to withdraw our participation, KROS on the other hand decided to take the leap and conduct the 17th International Conference on Ubiquitous Robots (UR2020) in Kyoto, Japan on the originally scheduled dates virtually for the first time in its history. It was certainly challenging for both the organisers as well as the participants, yet it was a success with a lot of takeaways for everyone.

Although the core topic of our work is more related to the field of Aerospace, we were extremely pleased to learn that our work was greatly recognised by the robotics community and nominated for the best contributed paper award at UR2020.

On 25th June 2020, 09:00hrs (JST), our 10 minutes long pre-recorded presentation was played back on Zoom application for the audience who later raised their questions via the built-in Q&A tab provided on the platform. Participants had access to the audio and video of the presenter and the session Chair who communicated and sorted the posted questions through a one-on-one video call.

Online conference sceptics might argue that networking and physical meetings at an international conference is a significant advantage missing in a virtual event, especially for early career researchers. Fortunately, this feature was also thoughtfully integrated by the organisers on the Slack application where different channels were created for presenters to communicate and share opinions/contacts for a period of thirty days.

Overall, it was rather an interesting experience, although I was in my 14-days covid-19 quarantine in China, at least I didn’t have to attend my session past midnight in the UK (BST).”

Hamid remains in China to carry on his secondment activities, although not in quarantine anymore. He continues his study on the effects of haemolymph on the flexural stiffness of various flying insect species. You can learn more about Hamid’s research here.

Based on the successful mind-matching session at the Cognitive Computational Neuroscience (CCN) conference, a free web-based unconference for neuroscientists was created called “neuromatch“.

The neuromatch 1.0 conference was held on 30th and 31st March, 2020. The conference agenda included a significant number of international speakers.

Our ULTRACEPT researcher Xuelong Sun presented his work on insect navigation at the conference. Considering the current travel restrictions caused by Covid-19, this was an excellent opportunity to continue to promote the ULTRACEPT project work in an innovative, safe and effective way.

Xuelong presented his work ‘A Decentralised Neural Model Explaining Optimal Integration Of Navigational Strategies in Insects’. Xuelong is carrying out this work with Dr Michael Mangan and Prof Shigang Yue.

A copy of Xuelong’s presentation can be accessed here.

To learn more about this research, please refer to the paper Modelling the Insect Navigation Toolkit: How the Mushroom Bodies and Central Complex Coordinate Guidance Strategies https://doi.org/10.1101/856153 .