The Cold Spring Harbor Laboratory (CSHL) and Tianqiao & Chrissy Chen Institute (TCCI) Meeting Reports is a collection of first-hand accounts written by a graduate student or postdoctoral scholar attending a CSHL meeting in 2022. It is part of the CSHL & TCCI Science Writers Fellowships which aims to extend the conversation beyond the meeting with the hopes of sparking new ideas and collaborations. Fellowships are awarded to seven early-career researchers; one from each of the following neuroscience meetings:
The first report from the 2022 Neuronal Circuits meeting is written by Jennifer Panlilio, a postdoctoral fellow who uses zebrafish to study the neural circuits that modulate arousal.
The 9th Neuronal Circuits meeting was held in person at Cold Spring Harbor Laboratory on March 16-19, 2022. A whole crew of alert, enthusiastic, primed scientists descended into the space, ready to connect and finally share years’ worth of data.
Constance “Connie” Brukin for Cold Spring Harbor Laboratory Meetings & Courses
Songs across organisms, in different contexts
Benjamin Judkewitz, Einstein Center for Neurosciences. Source
All of us, first in-person,-post-pandemic attendees, filed into the auditorium to hear the kick-off sessions focused on ‘singing across species.’ The talks didn’t disappoint as we learned about the array of species that sing to communicate, from fish to flies to mice. Benjamin Judkewitz from the Einstein Center for Neurosciences Berlin spoke about his lab's work in Danionella, a fish that makes vocalizations that sound like mini-motors with a little mix of bass. These fish are promising models because of their tiny size and see-through brains, allowing us to basically see all the active neurons in fish performing these complex social vocalizations.
Mala Murthy, Princeton Neuroscience Institute. Source
Professor Mala Murthy from the Princeton Neuroscience Institute presented her research about how male flies (Drosophila) alter their courtship songs depending on the female’s behavior: males abruptly switch to longer, more complex songs when the female is 1.3 fly bodies or closer, perhaps a way to woo her when she is close, while preserving his energy when she isn’t. To identify just how song switching happens, researchers manipulated the activity of different neurons involved in making sound to see how this changed behavior. What they found is that when the female is far from the male, the leading neuron is only partially active and another neuron then gets to interrupt the conversation. This results in a simple song. In contrast, if the female is closer to the male, the lead neuron gets more active, recruiting a new neuron to stop the interrupting neuron. This allows for more complex song. What is really interesting about these findings is that the context (female being far or close), changes the way the same neurons talk to each other, leading to completely different behavioral outputs.
Arkarup Banerjee, Cold Spring Harbor Laboratory. Source
Mouse singing also dynamically changes based on social interaction. Assistant Professor Arkarup Banerjee from Cold Spring Harbor Laboratory spoke about his work with Alston’s singing mice which take turns rapidly conversing with each other. What is cool here is that these precisely-timed conversations require a region in the cortex that, when inactive, causes their song timing to be off. To understand what this area does, the group recorded activity of neurons in this region and found that a group of these neurons “stretch” and “compress” their activity to match the length of their song. Locally cooling this region to change the activity in the neurons resulted in increases in the duration of the song, while the song ‘notes’ were unaltered. This supports the idea that the cortex controls the precise timing of the songs, something previously thought was only true in primates! Such work holds promise in understanding how effective social communication happens and how it can break down in disease.
Established models, new tools
To learn about more about how complex behaviors like song work, we need accurate tools that let us identify the neurons involved and dissect how they talk to each other. In the large-scale encoding session, we learned about the novel ways researchers are accomplishing this in well-studied animal models.
Andrew Leifer, Princeton University. Source
Caenorhabditis elegans is a tiny worm with 302 neurons and 7000 connections (synapses) made between them. While we have a map of which neurons are physically connected to each other (like a series of roads mapped out), we still need to know how they use these connections to perform a behavior (actual routes taken). To complicate matters, some neurons don’t need to physically connect to talk to each other and rather use diffusible chemicals (think: neurons texting). Thus, the physical maps can’t always predict real connections. To address these shortcomings, Assistant Professor Andrew Leifer’s lab at Princeton University studies how these neurons talk to each other in real time by activating neurons one by one, forcing them to broadcast their signal and seeing which neighboring neurons respond. The output of this is a detailed map of all the functional connections within the worm, a map that undoubtedly will be very useful for neuroscientists moving forward.
Lin Zhong, Yale University. Source
With the advent of new technology, we now have the capacity to record the activity of tens of thousands of neurons all at once while an animal is performing a complex behavioral task. However, the challenge doesn’t end with recordings. This massive amount of data still needs to be made sense of. Professor Lin Zhong from the Department of Computer Science at Yale University spoke about how they created a sorting algorithm called Rastermap which takes activity traces of thousands of neurons across time and groups neurons that behave similarly. They applied Rastermap to analyze activity data from more than 40,000 (!) neurons while mice were virtually navigating their visual surroundings. Zhong and colleagues showed that mice have a remarkable ability to recognize textures and distinguish patterns in their environment such as leaves vs. pebbles arranged in different ways. Moreover, using Rastermap, Zhong identified specific groups of neurons that are involved in texture recognition. For example, one group of neurons were only active when presented with a new corridor pattern and another group were active only when mice were travelling through a corridor with a pattern from which they expected a reward. These tools allow us to capture the role of many, many more neurons in complex behaviors, including higher cognitive function. Remember, one of the hallmarks of cognition is the ability to form memories, imagine new outcomes, and execute tasks.
Encoding past, present, and future states
Loren Frank, UCSF. Source
“The utility of memories is not in the past, but in the future.” A strong opener from UCSF Department of Physiology Professor Loren Frank on his lab’s work on spatial navigation in rats. Specifically, he talked about the unique firing patterns in neurons called place cells which are most active when the rats visit specific locations. However, sometimes, these cells are unexpectedly active beyond their predicted location. For a long time, neuroscientists considered this unexpected activity “noise” in the system. However, Frank argued that these neuronal patterns may actually represent the rats “imagining” future places! For example, when rats are confronted with a choice to go left or right in a maze, groups of neurons that are normally active in places to the left or to the right would “take turns” being active, as if the rat was imagining these two possibilities. When the rats did choose a side to travel to, neurons that encoded locations from the alternative side were also active. These sparse and complex activity patterns hint at a much richer mechanisms of imagination, possibility, and decision making.
While there were too many fantastic talks to cover, this conference was the best analogy for a neuronal circuit: individual scientists, like neurons, contributing their ideas, making critical connections, and carrying out an important function of advancing the field of circuit neuroscience. I look forward to the next one in 2024.