New preprint [HERE], on work led by postdoc Marino Pagan, together with a great team of collaborators.
For flexible, context-dependent decision-making, the space of network solutions is much larger than previously appreciated.
Moreover, variability across and even within individuals on where they are in this space is the underlying variable that jointly explains neural and behavioral variability.
Congratulations to Edward Nieh, who has accepted a faculty position at the University of Virginia, in the Department of Pharmacology, starting May 1, 2023!!! 🎉
Graduate student Diksha Gupta’s talk, on ”An explanatory link between history biases and lapses” has won the Best Paper Award at the RLDM 2022 Conference! Congratulations Diksha!
Congratulations to postdoc Brian DePasquale, who has accepted a faculty position at Boston University, to start in January 2023!!! 🎉
Congratulations to Emily Dennis, who is starting up her own group at Janelia!
A super-cool paper by Sue Ann Koay, who did this work as a joint postdoc with the Tank lab, is now out in Neuron. The paper contains many beautiful analyses. Among them, Sue Ann shows how efficient coding is implemented at the level of populations.
Recent work has highlighted that many types of variables are represented in each neocortical area. How can these many neural representations be organized together without interference and coherently maintained/updated through time? We recorded from excitatory neural populations in posterior cortices as mice performed a complex, dynamic task involving multiple interrelated variables. The neural encoding implied that highly correlated task variables were represented by less-correlated neural population modes, while pairs of neurons exhibited a spectrum of signal correlations. This finding relates to principles of efficient coding, but notably utilizes neural population modes as the encoding unit and suggests partial whitening of task-specific information where different variables are represented with different signal-to-noise levels. Remarkably, this encoding function was multiplexed with sequential neural dynamics yet reliably followed changes in task-variable correlations throughout the trial. We suggest that neural circuits can implement time-dependent encodings in a simple way using random sequential dynamics as a temporal scaffold.
Congratulations to Ahmed el Hady, who is starting his group in the Department of Collective Behavior at the Max Planck Institute for Animal Behavior in Germany!
Congratulations to postdoc Marino Pagan, who is one of six new SFARI Bridge to Independence Fellows! This is a very competitive award. As the SFARI web sites describes, “Launched in 2015, the program is aimed at Ph.D.- and M.D.-holding scientists with an interest in autism research who are currently in training positions but intend to seek tenure-track faculty positions at a U.S. or Canadian research institution during the upcoming academic year. Fellows will receive a commitment of $495,000 over three years, activated upon assumption of their faculty positions.”
Very excited for Edward and Manuel, joint postdocs with the Tank Lab and part of our BRAIN CoGS big collaboration, who have this paper now out in Nature.
Abstract: Hippocampal neurons encode physical variables1,2,3,4,5,6,7 such as space1 or auditory frequency6 in cognitive maps8. In addition, functional magnetic resonance imaging studies in humans have shown that the hippocampus can also encode more abstract, learned variables9,10,11. However, their integration into existing neural representations of physical variables12,13 is unknown. Here, using two-photon calcium imaging, we show that individual neurons in the dorsal hippocampus jointly encode accumulated evidence with spatial position in mice performing a decision-making task in virtual reality14,15,16. Nonlinear dimensionality reduction13 showed that population activity was well-described by approximately four to six latent variables, which suggests that neural activity is constrained to a low-dimensional manifold. Within this low-dimensional space, both physical and abstract variables were jointly mapped in an orderly manner, creating a geometric representation that we show is similar across mice. The existence of conjoined cognitive maps suggests that the hippocampus performs a general computation—the creation of task-specific low-dimensional manifolds that contain a geometric representation of learned knowledge.
We’re very excited to see this paper out! How does SC contribute to executive control? Our data suggests that the SC can be at the very heart of executive control, responsible for combining cognitive control signals with sensory inputs to compute context-appropriate responses. Remarkably, the fully context-appropriate responses can be decoded from SC neurons almost 200 ms faster than from cortical neurons (wow!), suggesting the computation may be led, and happen, in the SC itself. Circuit modeling supports this idea, with large-scale searches revealing a wide variety of possible SC circuits that would achieve this and that are compatible with the experimental data.
Abstract: Context-based sensorimotor routing is a hallmark of executive control. Pharmacological inactivations in rats have implicated the midbrain superior colliculus (SC) in this process. But what specific role is this, and what circuit mechanisms support it? Here we report a subset of rat SC neurons that instantiate a specific link between the representations of context and motor choice. Moreover, these neurons encode animals’ choice far earlier than other neurons in the SC or in the frontal cortex, suggesting that their neural dynamics lead choice computation. Optogenetic inactivations revealed that SC activity during context encoding is necessary for choice behavior, even while that choice behavior is robust to inactivations during choice formation. Searches for SC circuit models matching our experimental results identified key circuit predictions while revealing some a priori expected features as unnecessary. Our results reveal circuit mechanisms within the SC that implement response inhibition and context-based vector inversion during executive control.