August 9, 2022

New Paper on Developmental Braitenberg Vehicles now live!

 

The special issue of Artificial Life on Embodied Intelligence is now live! Inside you will find our paper "Braitenberg Vehicles as Developmental Neurosimulation", which has lived on the arXiv since 2020. This paper lays out an approach to Developmental Neurosimulation, involving three adversarial approaches to the agent-based development of embodied brains and embodied cognition. Here is the abstract:

Connecting brain and behavior is a longstanding issue in the areas of behavioral science, artificial intelligence, and neurobiology. As is standard among models of artificial and biological neural networks, an analogue of the fully mature brain is presented as a blank slate. However, this does not consider the realities of biological development and developmental learning. Our purpose is to model the development of an artificial organism that exhibits complex behaviors. We introduce three alternate approaches to demonstrate how developmental embodied agents can be implemented. The resulting developmental Braitenberg vehicles (dBVs) will generate behaviors ranging from stimulus responses to group behavior that resembles collective motion. We will situate this work in the domain of artificial brain networks along with broader themes such as embodied cognition, feedback, and emergence. Our perspective is exemplified by three software instantiations that demonstrate how a BV-genetic algorithm hybrid model, a multisensory Hebbian learning model, and multi-agent approaches can be used to approach BV development. We introduce use cases such as optimized spatial cognition (vehicle-genetic algorithm hybrid model), hinges connecting behavioral and neural models (multisensory Hebbian learning model), and cumulative classification (multi-agent approaches). In conclusion, we consider future applications of the developmental neurosimulation approach.

There are many themes to follow up on in this paper. Just of few examples include:

* brain/body scaling in an embodied agent.

* the role of multisensory integration in the development of cognition.

* ways to classify shapes and motifs in the emergence of multi-agent collectives. 

* spatial cognition and transfer learning in developmental embodied systems.

Congratulations to Stefan Dvoretskii, Ziyi Gong, Ankit Gupta, Jesse Parent, and Bradly Alicea for their hard work.

June 15, 2022

Google Summer of Code 2022 in the OpenWorm Community (DevoWorm)



Welcome to Google Summer of Code 2022! I am pleased to announce that this year, we have two funded projects: D-GNNs and Digital Microspheres! These projects will both take place in conjunction with the DevoWorm part of the OpenWorm community. DevoWorm is an interdisciplinary group engaged in both computational and biological data analysis. We have weekly meetings on Jit.si, and are a part of the OpenWorm Foundation. 

This year, we were able to fund two students per project. They will be working on complementary solutions to each problem, and we will see how far they get by the end of the Summer. 

D-GNNs (Developmental Graph Neural Networks)

The description for this project is as follows:

Biological development features many different types of networks: neural connectomes, gene regulatory networks, interactome networks, and anatomical networks. Using cell tracking and high-resolution microscopy, we can reconstruct the origins of these networks in the early embryo. Building on our group's past work in deep learning and pre-trained models, we look to apply graph neural networks (GNNs) to developmental biological analysis.

The contributor will create graph embeddings that resemble actual biological networks found throughout development. Potential activities include growing graph embeddings using biological rules, differentiation of nodes in the network, and GNNs that generate different types of movement output based on movement seen in microscopy movies. The goal is to create a library of GNNs that can simulate developmental processes by analyzing time-series microscopy data.

When completed, D-GNNs will become part of the DevoWorm AI library. Ultimately, we will be integrating the GNN work with the DevoLearn (open-source pre-trained deep learning) software. 


Jiahang Li

Jiahang Li is a first year MPhil candidate in Computing Department at Hong Kong Polytechnic University. His research interests cover graph representation learning and its applications. Jiahang's approach to the project is to provide a pipeline that converts microscopic video data of C. elegans and other organisms into graph structures, on which advanced network analysis techniques and graph neural networks will be employed to obtain high-level representation of embryogenesis and to solve applied problems.




Wataru Kawakami

Wataru is a student at Kyoto University with interests in Machine Learning (in particular Graph Neural Networks) and Neuroimaging.

Digital Microspheres

The description for this problem is as follows: 

This project will build upon the specialized microscopy techniques to develop a shell composed of projected microscopy images, arranged to represent the full external surface of a sphere. This will allow us to create an atlas of the embryo’s outer surface, which in some species (e.g. Axolotl) enables us to have a novel perspective on neural development.

The contributor will build a computational tool that allows us to visualize 4D data derived from the surface of an Axolotl embryo. The spatial model and animation (4th dimension) of microscopy image data can be created in a 3-D modeling software of your choice.

This project is based on previous research by DevoWorm contributors Richard Gordon and Susan Crawford-Young. The flipping and ball microscopy research involve the design and fabrication of specialized microscopes to image embryos in a 4-D context (3 dimensions of space plus time).

Spherical Embryo Maps: Gordon, R. (2009). Google Embryo for Building Quantitative Understanding of an Embryo As It Builds Itself. II. Progress Toward an Embryo Surface Microscope. Biological Theory, 4, 396–412.

Flipping Microscopy: Crawford-Young, S., Dittapongpitch, S., Gordon, R., and Harrington, K. (2018). Acquisition and reconstruction of 4D surfaces of axolotl embryos with the flipping stage robotic microscope. Biosystems, 173, 214-220.

Ball Microscopy: Crawford-Young, S.J. and Young Williment, J.L. (2021). A ball microscope for viewing the entire surface of amphibian embryos. Biosystems, 208, 104498.

Karan Lohaan

Karan is a student at Amrita Vishwa Vidyapeetham University, and is a member of the AMFoss program there. He is interested in Machine Learning and Image Processing. 

Harikrishna Pillai

I am Harikrishna pursuing my B.Tech in Computer Science and Artificial Intelligence from Amrita Vishwa Vidyapeetham University. I completed my schooling in Mumbai. I started with python as my first language and eventually developed interest for AI. Due to my interest in Android apps, I have done Android development in Kotlin. Also, I have been interested in open source for some time now and therefore, I wanted to start my open source journey with GSoC.

We also have two GSoC mentors for these projects: Bradly Alicea is a mentor for D-GNNs and Digital Microspheres, and Jesse Parent is a mentor for D-GNNs. Richard Gordon and Susan Crawford-Young are serving as collaborators for the Digital Microspheres project.

If you would like to check on their progress, please check out our weekly meetings available on our YouTube channel.

March 31, 2022

Updates on Open Source Community Tools

All hail the Guidance (and Attribution) Tree!

The guidance tree is a concept that grew out of my days on the OpenWorm Community Committee. I have implemented a guidance tree for the Rokwire Community, and is available in Beta version for other communities to implement (HTML, Markdown) under a CC-BY license. Check out this 19-minute tour of a guidance tree based on the Rokwire Community.


A guidance tree allows new community members to find a starting point in your community easily, while optimally leveraging their skills. This is something I am calling Community Wayfinding. Community managers who want to adopt the guidance tree have to analyze their own set of community resources (Github repositories, web resources, and documentation) to see where members might fit in. Adopting a guidance tree of your own also requires a definition of community roles, which will be unique to different communities. In the Beta version, a user encounters a set of binary choices that bifurcate towards a specific contribution path. Future versions might map this possibility space to a VR (virtual) world where the options are presented as 3-D objects or as activity rooms.

Would you be interested in a system for tracing attribution or authorship on an open-source (or open science) project? When the formal release version of guidance tree is released, it will be accompanied by the Authorship Tree, an idea I worked on circa 2017 in the Orthogonal Research and Education Lab. The Authorship (or attribution tree) solves the problem of authorship order by showing the relative contributions of each individual in the form of a tree structure. This not only allows for primary contributions to be visualized, but also for deeper contributions (informal conversations, data sources, stakeholders) to be recognized. This is particularly good for publicly recognizing different types of contributors and whose who have been active at various levels of contribution.


Recombining your Technical Stack
During the month of March 2021, we considered how we might strive for a Full Stack community. The basic idea was that depending on the task one wants to carry out, there exist a series of possible technologies to achieve your goals. The challenge is to pick the best set of tools, for all aspects of your open source project. These tools should be compatible with one another, easy to learn/use, and provide accessibility to future contributors. 

But tools are constantly changing. Sometimes you outgrow your current set of tools. Some platforms are okay for small numbers of users, but become unmanageable as you community scales up. This is the nature of the constant tradeoffs one makes in managing a dynamic community. In other cases, tools simply cease to exist, forcing you to migrate to another solution. And sometimes a tool becomes unaffordable as your user needs change. So the question becomes: how do you go about changing out your stack? 


My personal preference is to stick to open source tools whenever possible. There are two reasons for this: it eliminates the cost constraint, and it allows for open source solutions to emerge from the user community. Your open source community might also be able to develop customized tools for such platforms, thus helping you keep your technology stack consistent. The Jitsi platform is a good example of this. Jitsi instances can be started through a web browser or mobile app, and Jitsi servers can be customized by specific organizations. 


When you do need to change out elements in your stack, the first step is to make sure that the solution works well with your other tools. For example, if you change your video conferencing tool, be sure to make sure your whiteboard (Jamboard) and file sharing (Ignite RealTime) tools also work well with this shift. Doing a series of contingency tests based on common use cases may help. Adopting OBS for screen recordings and streaming is an example of this. Once you know your tool runs stably and provides the desired output, then you will have fewer glitches down the road. 

Secondly, make sure that you community leaders (people most likely to run a community meeting) can use the new tool. You might offer a primer or training session to get people up to speed on the new tool, in addition to how it connects with other tools in the stack. This primer should then be available in the form of video and written documentation to the community, as your meeting and discussion leaders may change over time. Tools that visualize Github tasks and milestones (ZenHub) is one example of a tool with multiple dependencies.

A third step is to do an audit of how the tool is actually being used, to ensure that there is a match between desired functionality and the functionality that is available to everyday users. Perhaps your community is really interested in sharing files during their video meetings. This may require the addition of a new tool, or an addition to an existing tool. Only a post-implementation audit (or a quarterly solicitation of use cases) will reveal an actionable path.

February 15, 2022

Gyrification of the Tree of Mammals

For this year's Darwin Day post, I will be reviewing the evolutionary origins and developmental emergence of gyrification of the Mammalian brain. Gyrification occurs when the neocortex, or six layered cortex on the dorsal surface of Mammalian brains, exhibits wrinkles and folds rather than a smooth surface (lissencephaly). Gyrification is measured using the gyrification index (or GI). GI can range from 5.6 in Pilot whales (Globicephala) to 3.8 in Elephants (Loxodonta) and 2.6 in Humans (Homo) [1]. A more extensive phylogenetic analysis (Figure 1) shows the evolutionary trajectory for this in Hominids, and a highly gyrified brain is associated with other traits that emerge as early as the divergence of Primates. 


Figure 1. A phylogeny of primate brain evolution (with Mammalian outgroups), with a focus on the origin of traits found in the human brain. COURTESY [2].

The evolutionary origins of gyrification may either be mono- or polyphyletic, as different genes have been identified as potential associated factors. Gyrification might also be a product of convergent evolution, as this trait may simply be a by-product of larger neocortical sheets. Steidter [3] points out that gyrification may simply be due to physical constraints related to fitting a vastly enlarged cortical sheet into a skull scaled to an organism's body size. 

Figure 2. Allometric scaling across select Mammalian brain, showing an increase in gyrification for larger brains. COURTESY [4].

In Figure 2, we see that in general larger brains also have a larger GI value. The curvilinear relationship shown in the figure is known as an allometric scaling. Allometry [5] is a convenient way to quantitatively assess relative growth across different species, and the resulting regression parameters are suggestive of underlying mechanisms that control and predict growth across evolution.

In this case, the allometric relationship is brain size versus tangential expansion. Tangential matter is expansion of gray matter relative to the constraints of white matter, or a grey-to-white matter proportion [4]. As the amount of gray matter increases, brain size also tends to increase, and so does the GI value. However, the proportion of gray to white matter saturates, while brain sizes continue to expand along with increasing GI values. 




Figure 3. Simulating gyrification as a by-product of physical processes. 3-D printed models based on MRI data for brains from different stages of development. COURTESY [6].

Genetic analyses implicate the role of specific genes in controlling brain volume, which then sets the stage for gyrification [7]. Developmental mutations in the human genetic loci collectively known as MCPH 1-18 [8] lead to a condition called microcephaly, where the mature microcephalic brain remains small and lacks gyrification. In a study of 34 species [9], the largest source of explained variance between species can be explained by random Brownian motion. Furthermore, the data within the order Primates shows that fold wavelength is stable (~12mm) despite a 20-fold difference in volume [9]. 

As an alternative hypothesis to evolutionary origins, gyrification can result from various physical processes in developmental morphogenesis (Figure 3). The gyrification process consists of gyral (ridge-like) and sulcal (groove-like) convolutions. In the earliest stages of development, no gyrification is expressed in the phenotype. However, as the neocortex grows faster relative to the rest of the brain, a mechanical instability results that leads to buckling [6]. Buckling thus creates gyrification, although the consistency of their localization and timing in development suggests underlying cellular and molecular mechanisms. Demonstration of biophysical mechanisms does not preclude a phylogenetic explanation, however. As we will see later on, surface physics relies upon the presence of certain cell types and growth conditions.


Figure 4. An overview of the evolution of development (Evo-Devo) of gyrification. Gyrification and lissencephaly occur through mechanisms that affect changes in brain size and GI relative to the last common ancestor (in this figure, transitional form). COURTESY [10].

There are also several cellular and molecular factors that contribute to neocortical growth, and thus towards gyrification. In Figure 4, we see four archetypes that result from increases and decreases of brain size coupled with increases and decreases of GI. For example, increases in basal radial ganglion (bRG) precursor cells and transit-amplifying progenitor cells (TAPs) contribute to increases of both brain size and GI [10]. Decreases in brain size and GI are controlled by changes in cell cycle timing and associated heterochronic changes. Heterochrony has to do with the timing of the rate and termination of growth in development and is but one factor that suggests lissencephaly is actually the derived condition. Thus, smooth brains would be an evolutionary reversal from the ancestral gyrified state that occurred multiple times across the tree of Mammals. 

Once again, an evolutionary conundrum. Happy evolutioning!

NOTES:

[1] Johnson, S. Number and Complexity of Cortical Gyrii. Center for Academic Research and Training in Anthropogeny. La Jolla, CA. Accessed: February 13, 2022. 

[2] Franchini, L.F. (2021). Genetic Mechanisms Underlying Cortical Evolution in Mammals. Frontiers in Cell and Developmental Biology, 9, 591017.

[3] Striedter, G. (2005). Principles of brain evolution. Sinauer, Sunderland, MA.

[4] Tallinen, T., Chung, J.Y. , Biggins, J.S., and Mahadevan, L. (2014). Gyrification from constrained cortical expansion. PNAS, 111(35), 12667-12672.

[5] Shingleton, A. (2010) Allometry: The Study of Biological Scaling. Nature Education Knowledge, 3(10), 2.

[6] Tallinen, T., Chung, J.Y., Rousseau, F., Girard, N., Lefevre, J., and Mahadevan, L. (2016). On the growth and form of cortical convolutions. Nature Physics, 12, 588–593.

[7] Zilles, K., Palomero-Gallagher, N., and Amunts, K. (2013). Development of cortical folding during evolution and ontogeny. Trends in Neurosciences, 36(5), 275-284. 

[8] Jayaraman, D., Bae, B-I., and Walsh, C.A. (2018). The Genetics of Primary Microcephaly. Annual Review of Genomics and Human Genetics, 19, 177-200.

[9] Heuer, K., Gulban, O.F., Bazin, P-L., Osoianu, A., Valabregue, R., Santin, M., Herbin, M., and Toro, R. (2019). Evolution of neocortical folding: A phylogenetic comparative analysis of MRI from 34 primate species. Cortex, 118, 275-291.

[10] Kelava, I., Lewitus, E., and Huttner, W.B. (2013). The secondary loss of gyrencephaly as an example of evolutionary phenotypical reversal. Frontiers in Neuroanatomy, 7, 16.


December 3, 2021

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.



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).


Printfriendly