People in Baltimore have been dying of overdoses at a rate never before seen in a major American city.
The city was once hailed for its response to #addiction. But as #fentanyl flooded streets & ofcls shifted priorities, deaths hit unprecedented heights.
This is the first part in a series exploring #Baltimore’s overdose crisis.
In the past 6 yrs, nearly 6k lives have been lost. The death rate from 2018 to 2022 was nearly DOUBLE that of any other large city, & higher than nearly all of Appalachia during the prescription pill crisis, the Midwest during the height of rural meth labs or New York during the crack epidemic.
A decade ago, 700 fewer people here were being killed by drugs each year. And when fatalities began to rise from the synthetic #opioid#fentanyl, so potent that even minuscule doses are deadly, #Baltimore’s initial response was hailed as a national model. The city set ambitious goals, distributed #Narcan widely, experimented w/ways to steer people into treatment & ratcheted up campaigns to alert the public.
It's fitting that on a day when I was on campus I spotted a wild beaver, and while I was enjoying the wildlife I also enjoyed listening to some talks for my #AcademicRunPlaylist! (1/10)
Next was a thought-provoking talk by Suzanne Dikker on the neural basis of real-world social interaction, using sensors to study brain signals outside the laboratory at the University of London School of Advanced Study https://www.youtube.com/watch?v=EO4w_GansP4 (3/10) #neuroscience#psychology
"When and why does motor preparation arise in recurrent neural network models of motor control?"
"we modelled the motor cortex as an input-driven dynamical system, and we asked what the optimal way to control this system to perform fast delayed reaches is. We find that delay-period inputs consistently arise in an optimally controlled model of M1."
Cool findings from the lab of fellow #Pembroke1347 colleague Guillaume Hennequin.
In 2020, Swedish neuroscientists put 30 trans and 30 cis research subjects into a brain scanner and showed them pictures of their bodies. Then they showed them images where their body had been morphed to look more masculine or more feminine. The results support the natural order of gender identity.
How cool is this?
Tethering transgenic fruit flies to a torque meter inside of a 360° display to let them learn how to control a punishing heat beam with their turning attempts.
It looks like we have finally discovered in which neurons the plasticity takes place that is required for this kind of learning:
We may not be 100% sure, yet, but everything is pointing towards plasticity in the motor neurons of the ventral nerve cord that control the wing angles.
Neuroscientists and electrophysiologists of Mastodon, what would your top choice analysis tool be for a novice getting started with working with ECoG data? I have a graduate student I will be co-mentoring starting this Fall who is very bright but has no programming experience, and I'd like to help her get up and running as quickly as possible. I've generally been a roll-my-own-analyses type of electrophysiologist so I don't have a favorite framework to get her started with. I know EEGLAB is popular, but my main experience with it has been helping other people get their Matlab path working properly again after something in EEGLAB clobbers it. I've played with MNE in Python but it doesn't seem to be as purely-GUI as EEGLAB and I don't want her to get bogged down in learning Python before she can do any analyses at all.
So, what's your favorite tool for ECoG analysis? What would you recommend a student who's starting from zero background in electrophysiology or programming begin to learn in 2024?
Boosts for reach appreciated. I also just like hearing folks' opinionated takes on their research tools.
Pleased to share my latest research "Zero-shot counting with a dual-stream neural network model" about a glimpsing neural network model the learns visual structure (here, number) in a way that generalises to new visual contents. The model replicates several neural and behavioural hallmarks of numerical cognition.
Clever neuronal activity labelling strategy: Engineered Ca2+ sensor biotinylates nearby proteins. Those proteins can then be stained - works for single vesicles, organelles, dendritic compartments, all the way to neuronal engrams. Both in culture and in vivo!
I'm excited to share a new publication from my graduate work!
In this opinion piece, we describe a new idea about how the brain represents more than one object - by having neurons switch their activity over time. This new idea has implications across a wide range of other areas of neuroscience, including how parts of objects come together to form a whole, and how we select what to pay attention to in busy environments.
"A petavoxel fragment of human cerebral cortex reconstructed at nanoscale resolution" by Shapson-Coe et al. 2024 (Lichtman lab).
The reconstruction at its current state is already useful and very interesting. Here is to hoping the authors will put in more time and resources to further polish it.
A remarkable finding from Shapson-Coe et al. 2024 paper on human brain #connectomics: the presence of canalized connections in the human brain cortex. Canalized in the Kauffman boolean networks sense [1], which here means: among the many synaptic inputs that any one neuron integrates, some are far stronger (by number of synapses) than the rest.
[1] Canalisation as a term was introduced by Waddington in 1942 in the context of genetics to mean "some phenotypic traits are very robust to small perturbations" https://en.wikipedia.org/wiki/Canalisation_(genetics)