MAIN and Neuromatch Conference Presentations

The Orthogonal Research and Education Lab is on the virtual move! We have been featured at two conferences this week. The first conference is MAIN (Montreal Artificial Intelligence-Neuroscience) conference, a hybrid conference that focused on cutting-edge research in Neuro-AI. Our submission (Developmental Embodied NeuroSimulation) is a group effort and summarizes our work in this area over the past few years. The graphical abstract can be found below.

Graphical Abstract for Developmental Embodied NeuroSimulation.

We also had a presence at Neuromatch 4, with four flash talk presentations on four different topics. Neuromatch 4 was a great time, with two days of keynote talks, short talks, flash talks, and debate panels. 

Each flash talk was 7.5 minutes long, which requires an efficiency of words and ideas not typical of a longer format. The first talk is "The Universal Theory of Switching", which focuses on transitory "switching" phenomena. Switching behavior is ubiquitous across biological, physical, and algorithmic systems, and is controlled by sudden, first-order phase transition-like behavior we characterize as zeroth-order cybernetic regulation. 

Another talk is on "Allostatic Kinds". Allostatic Kinds are a way to regulate the boundaries of meaning and regulation of internal emotional and conscious states. This talk is presented by Jesse Parent, and features a mix of complex systems regulation, philosophy of mind, and consciousness studies. This talk was in conjunction with CEEALAR (Center for Enabling EA Learning and Research), an academic hostel located in Blackpool, UK.

Daniela Cialfi has built upon the lab's work on Meta-brain Models to develop "Economic Meta-brains", which are bio-economic agents that behave according to the free energy principle. Meta-brains are layered computational models that enable different levels of representation in the same agent. These model layers can be configured in geometrically specific ways, which in turn affects their function. The free energy principle enriches the meta-brains approach by adding a mathematically rigorous energetic component to a meta-brain agent. 

Finally, our presentation on "Gibsonian Information" comes with a preprint. Gibsonian Information is the information content of direct perceptual processing (sensu J.J. Gibson). We draw parallels between Shannon and Gibsonian Information, in addition to the role of such information in the dynamic interactions between agents and their environments. See our graphical abstract below, which simplifies the mathematics in the preprint. The talk also features a number of naturalistic settings in which Gibsonian Information can be demonstrated.

Graphical abstract for the Gibsonian Information paper/presentation (direct perception as information content).

The World of Physical Intelligence

As part of the Embodied Intelligence workshop this past week, I saw a presentation by Metin Sitti of the Max Planck Institute for Intelligent Systems on the emerging paradigm of Physical Intelligence. What is Physical Intelligence? It is the intelligent behavior exhibited by motion and takes into account a morphology (embodiment) and action at multiple scales of spatial organization. The talk was wonderful and walked through a number of empirical studies involving both living and non-living systems. I will leave it up to the reader to appreciate all of the points raised in this talk.

Physical Intelligence as an emerging idea.

While Physical Intelligence is not a common paradigm, the general idea is actually not new. Other people have proposed very similar frameworks over the past 15 years or so, including yours truly. Physical Intelligence involves some form of motor or movement behavior, which is generated by an embodied agent, that in turn interacts with the physical environment that can be defined by features such as inertial and gravitational forces, surface textures, and even light energy. 

Physical stimuli for soft robots or other autonomous agents from Figure 1 in Shen et.al, Journal of Materials Chemistry B, 8, 8972-8991 (2020).

At its most human-centric, physical intelligence can be just another word for embodied intelligence, which is where the body shapes and determines what is experienced by the nervous system. In cases where the behaving agent has no nervous system, the body geometry shapes the agent's behavioral output. This is true both in cases of adaptive (intentional) behavior and reactive behavior. In some cases, physical intelligence is identical to Neuromechanics. In other cases, it resembles a range of fields, from Biophysics to Embodied Robotics. The important contribution of the Physical Intelligence paradigm is the principles that guide this diverse topical terrain. Both my talk and Metin's talk provide some of these potential principles, and Scott Grafton's book provides a few more.

One human-centric interpretation of Physical Intelligence (by Scott Grafton).

Metin brings up the example of the Strandbeest, which is a kinetic sculpture with no nervous system or centralized control. This is an example of a purely reactive system that also generates seemingly intelligent behavior. Closer to the human experience is the Passive Dynamic Walker, which produces human-like bipedalism without a central nervous system. This is the essence of the physical: a particular physical configuration can exhibit reactive behavior independently of a central controller.

While serving as part of the peripheral nervous system, and in fact being controlled by a central nervous system, muscles can also play a key role in physical intelligence. While muscle cells can be spontaneously active without being inputs by motor neurons, there is a elaborate coordination between the central nervous system and muscular control. Muscular control can produce both very fast and very slow adaptive movements. In addition, the overall shape of a body in relation to its muscles can constrain the behavior of the agent in question.

The main takeaway is that the brain, body, and environment all work interdependently to shape the behavior that emerges from this complex system. Even in cases where there is no brain (or neural network), the interactions between body and the environment are enough to generate reactive behaviors that appear to be intelligent. This is true for both individual morphologies and the collective behavior of many agents (e.g. swarm intelligence). In fact, the brain can be supplanted by control mechanisms that regulate the conformity and response to physical forces in the environment. Future work should focus on the differences between "neural" and "physical" behavior, as well as the necessity and sufficiency of each component in the triad.

A diagrammatic example of this relationship (with a brain in the feedback loop) from Chiel and Beer, Trends in Neuroscience, 20(12), P553-P557.

In conclusion, DARPA has also engaged in the idea of physical intelligence, and there was a proposer's conference in 2009. This version of Physical Intelligence has a strong cybernetics flavor, particularly in incorporating the EGRT (Every Good Regulator Theorem) into the mix.

Cybernetics of the firm (deemphasizing the role of individual morphology). COURTESY: New World Encyclopedia.

Presentations at Neuromatch (NM)3

Neuromatch 3 (NM3) happened a few weeks ago in virtual space, and it was great! There were hundreds of presentations over five days (October 26-30), many of them already archived on YouTube. This version of Neuromatch took the place of Society for Neuroscience (SfN), which was cancelled due to COVID. In this sense, Neuromatch is proving itself to be an improvement on the legacy conference. Between my two research groups (Representational Brains and Phenotypes and DevoWorm), we had five presentations submitted to NM3. Let's go through them one by one.

Contrast Between Biological and Artificial Neural Networks (BNNs vs ANNs). Download slides from Figshare. Video on YouTube. Click to enlarge.

This presentation was by Krishna Katyal and myself. Krishna is a regular contributor to the DevoWorm group. This presentation demonstrates several contrasts between Biological and Artificial Neural Networks, and how level of abstraction, network structure, and energetics all play a role in distinguishing the information-processing marvels known as biological brains.

Approaching Artificial Intelligence as an Embodied Developmental Process. Download slides from Figshare. Video on YouTube. Click to enlarge.

Thinking more along the lines of biological development, the next presentation features work done in the Representational Brains and Phenotypes Group. Taking a developmental approach to the classic Braitenberg Vehicle, we demonstrate how embodied developmental principles can be used to shape and guide networks for learning. Topics such as developmental contingency and the difference between morphogenesis and learning during the development of an artificial nervous system were also discussed.

The Psychophysics of Non-neuronal Cognition. Download slides from Figshare. Video on YouTube. Click to enlarge.

Are the advantages of cognitive information processing limited to organisms with a brain? This seems like a strange question, but can actually help us understand what a brain does and why it is important for coordinating the behavior of complex multicellular systems. In this presentation, which includes contributors to both the Representational Brains and Phenotypes and DevoWorm groups, we reconsider a model of Diatom movement as cognitive information processing. We also propose a series of potential scenarios in which this information processing occurs as well as Psychophysical-like measures to quantify these phenomena.

Trajectories in Cognition Studies - a focus on embodiment and future frontiers. Click to enlarge.

This abstract was submitted by Jesse Parent and Anson Lim, two regular contributors to the Orthogonal Research and Education Lab. Jesse is also a group leader and community manager in the lab. While they were not able to present during the scheduled time, they continue to work on this topic under the new and emerging Cognition Futures project.

Overcompensation of lens-accommodation in case of Convergence Insufficiency- A theoretical model. Click to enlarge.

The final talk was by Akshara Gopi (a regular contributor to the Orthogonal Research and Education Lab), along with myself and Ashwin Irungovel. This presentation was on convergence insufficiency in human vision, a topical specialty of Akshara and Ashwin. My contribution was to propose an agent-based model for this phenomenon. 

Check out all of the great talks at NM3, including one by Rishabh Chakrabarty (regular research contributor to the Orthogonal Lab) and his co-author called "Seeing through the Mind’s Eye: reconstruction of the visual stimuli using 3D Generative-Adversarial Modeling". This intriguingly titled talk features research that combines Neuroimaging data with Deep Learning.

If you are interested in these topics and want to be involved in this research, join us at our weekly research meetings (Saturday Morning NeuroSim and DevoWorm group), or watch them on YouTube! I also invite you to join the Orthogonal Research and Education Lab Slack or OpenWorm Slack (DevoWorm) for continued discussion. Hope to see everyone at Neuromatch 4!

Virtuality: a new view on virtual experience

The first images when you Google "virtuality". Products from the Virtuality Group. LEFT: Virtual Reality Society, RIGHT: Wikimedia (Dr.  Jonathan D. Waldern/Virtuality Group).

At Neuromatch 2.0, I gave a talk on the concept of virtuality titled "Computational Virtuality as a Form of Artificial Intelligence" (slides available on Figshare). Virtuality is a means to summarize all brain and body systems involved in a virtual experience. This talk features two big ideas, the second of which the Saturday Morning NeuroSim group will be building on in the coming months.  

1) "Being Virtual" as Human Performance (click to enlarge).

2) Differentiating Allostasis machine (click to enlarge). Allostatic load is induced collectively by stimulus [1], leads to dysregulation of system and ultimately to a new homeostatic setpoint.

Overall, virtuality can be characterized as a constructive sensory experience (h/t to Anson Lim). The first big idea stems from the observation that current theories are too limiting to properly characterize virtual experiences at the systems-level. Indeed, the very notion of objective reality is based in part on the synchronization of perception and action. When this synchronization is disrupted or perturbed in any way, we observe virtuality. This does not even require a virtual environment -- virtuality can be experienced in our interaction with the physical world [for an example from fly-human interaction, see 2]. But virtual environments are the most common way to elicit the allostatic load required to observe virtuality.

There are two aspects of embodied performance essential to understanding the virtuality effect. The first is the idea of cognitive gaps, or disruptions to spatiotemporal representations caused by the perturbation of reality. The second involves inducing allostatic load in the internal model the represents perception-action coupling. The latter has wide-ranging effects in the homeostat (nervous system), and can lead to many different effects of varying length scales.

But where does the artificial intelligence part come in? In the second part of the talk, I conclude that the effect sizes for human experiments are too small and formal experimental design is too limiting to demonstrate virtuality. This is particularly true for observing virtuality over long periods of time. Therefore, we can use computational agents! To propose a general approach, we want to identify two attentional and three sensorimotor features essential for any agent to exhibit virtuality.

Naturally, we want our agents to be embodied in some manner. We propose working with two types of agent: Morphogenetic Agents, a novel agent type that can exhibit both pattern recognition and morphogenesis (percepetion-action), and the well-known Braitenberg Vehicle, in this case experiencing incongruous environmental physics which leads to gaps in the perception-action loop. 

NOTES:

[1] this requires a ecological view of perception. For a short introduction, please see: Lobo, L., Heras-Escribano, M. and Travieso, D. (2018). The History and Philosophy of Ecological Psychology. Frontiers in Psychology, doi:10.3389/fpsyg.2018.02228.

[2] Alicea, B. (2013). Perceptual Time and the Evolution of Informational Investment. Synthetic Daisies blog, September 24.

NeuroConferences in Twos

Welcome to April and the era of the virtual conference! Aside from enforcing social distancing, virtual conferences have a number of other social benefits. I have recently presented at two virtual conferences, one on Twitter and one via teleconference software. The Twitter conference (OHBMx) was held previously as the brain.tc conference. This year, I presented on behalf of four co-authors about our new paper "Braitenberg Vehicles as Developmental Neurosimulation".

Our talk (#44) at OHBMx (click to enlarge).

In this talk and paper, we explore using Braitenberg Vehicles (BVs) to study the role of processes related to brain development and ontogenetic emergence of behavior. So-called dBVs are flexible systems that allow for naturalistic explorations of embodied behavior. Our approach utilizes four allied topical areas: evolutionary simulations, multisensory Hebbian learning, simulations of collective behavior, and explorations of embodied cognition.

A little more than a week later, me and one of my co-authors from the Twitter conference (Jesse Parent) presented at the Neuromatch conference. Neuromatch was put together in a only a few weeks by Konrad Kording, Dan Goodman, and Titipat Achakulvisut (congrats to them). The organizers used Crowdcast and Zoom as the presentation media. Two days of talks and over 2500 attendees! While there were various technical challenges to overcome (such as Zoombombing), the conference was a great success. The organizers said that this conference was meant as a template for future all-virtual conferences.

The title of my presentation was "Process as Connectivity: towards biology-specific complex networks". This is an update on presentations given at the Find Your Inner Modeler II workshop and the NetSci 2017 conference. Jesse's presentation was "Embodied Cognition: Using Developmental Braitenberg Vehicles To Model Levels of Representation", and while delayed due to technical difficulties, was a follow-up on our dBVs paper.

Two great talks at Neuromatch! Click to enlarge.

The other part of Neuromatch is matching researchers based on interest area. One interesting outcome to result from this matching process is the new BrainWeb community. BrainWeb is a new collaborative platform aimed at bringing together Neuroscientists and Neuro-adjacent people with expertise to share. They have even visualized an adjacency matrix of the community so far based on expertise. Check out their website for information on hackathon times and virtual locations (URLs).

Now it is I who is caught up in the hairball! Click to enlarge.

UPDATE (4/6): a new paper from several eLife Ambassadors is now out on the bioRxiv with recommendations on how to improve the academic conference experience. Many of their concerns and recommendations for change dovetail with the virtual conference experience. 

Citation: Sarabipour et.al (2020). Evaluating features of scientific conferences: A call for improvements. biorxiv, doi:10.1101/2020.04.02.022079.