Biology seminars

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

Learning entails self-modification of a system under closed-loop dynamics with its environment. Not only the system's components may change, but also the way they interact with one another - like synapses during learning in the brain, that modify interactions between neurons. Such processes, however, are not limited to the brain but can be found also in other areas of biology. I will describe a framework for a primitive form of learning that takes place within the single cell. This type of learning is composed of random modifications guided by global feedback. The capacity to utilize exploratory dynamics, improvisational in nature, provide cells with the plasticity required to overcome extreme challenges and to develop novel phenotypes.

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

We at Neuromatch 4.0 wish to open up science conferences to everyone and that is why we have included a session for kids and the young at heart. The NMC for kids has three excellent speakers from around the globe to talk about the balance system from bird butts to space: 1. Birds balance with their butts” by Bing Wen Brunton (Associate Prof of Biology at University of Washington, Seattle) 2. “The brain in motion” by Jenifer L. Campos (Associate Prof, University of Toronto) 3. “Getting ready for Mars: what happens to the brain in space?” By Elisa R Ferre (Senior Lecturer, Birkbeck University of London)

Dictyostelium Discoideum (DD) are a fascinating single-cellular organism. When nutrients are plentiful, the DD cells act as autonomous individuals foraging their local vicinity. At the onset of starvation, a few (<0.1%) cells begin communicating with others by emitting a spike in the chemoattractant protein cyclic-AMP. Nearby cells sense the chemical gradient and respond by moving toward it and emitting a cyclic-AMP spike of their own. Cyclic-AMP activity increases over time, and eventually a spiral wave emerges, attracting hundreds of thousands of cells to an aggregation center. How DD cells go from autonomous individuals to a collective entity remains an open question for more than 60 years--a question whose answer would shed light on the emergence of multi-cellular life. Recently, trans-scale imaging has allowed the ability to sense the cyclic-AMP activity at both cell and colony levels. Using both the images as well as toy simulation models, this research aims to clarify whether the activity at the colony level is in fact initiated by a few cells, which may be deemed "leader" or "pacemaker" cells. In this talk, I will demonstrate the use of information-theoretic techniques to classify leaders and followers based on trajectory data, as well as to infer the domain of interaction of leader cells. We validate the techniques on toy models where leaders and followers are known, and then try to answer the question in real data--do leader cells drive collective behavior in DD colonies?

The Addgene AAV Data Hub was launched to help scientists share data and protocols obtained from AAV experiments. Our longterm goal is to provide scientists with a resource to help guide AAV selection and use by providing data from individual labs on AAV performance.

Euglena gracilis is an interesting unicellular protist, also because it can adopt different motility strategies: swimming by flagellar propulsion, or crawling thanks to large amplitude shape changes of the whole body (a behavior known as “metaboly”, or “amoeboid motion”). Swimming trajectories are helical. The are powered by the beating of a single emerging flagellum, which spans non-planar waveforms in the shape of a twisted lasso. Finally the harmoniously coordinated shape changes that make metaboly possible, reminiscent of peristaltic waves, arise form the relative sliding of its pellicle strips, resulting in twisted helical bundles. We will report on the most recent findings on these interconnected topics, for which helical shapes provide a striking fil rouge.

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.