@article {68595, title = {Hacking brain development to test models of sensory coding}, journal = {bioRxiv}, year = {2023}, month = {2023 Jan 26}, abstract = {

Animals can discriminate myriad sensory stimuli but can also generalize from learned experience. You can probably distinguish the favorite teas of your colleagues while still recognizing that all tea pales in comparison to coffee. Tradeoffs between detection, discrimination, and generalization are inherent at every layer of sensory processing. During development, specific quantitative parameters are wired into perceptual circuits and set the playing field on which plasticity mechanisms play out. A primary goal of systems neuroscience is to understand how material properties of a circuit define the logical operations{\textemdash} computations--that it makes, and what good these computations are for survival. A cardinal method in biology{\textemdash}and the mechanism of evolution--is to change a unit or variable within a system and ask how this affects organismal function. Here, we make use of our knowledge of developmental wiring mechanisms to modify hard-wired circuit parameters in the\ Drosophila melanogaster\ mushroom body and assess the functional and behavioral consequences. By altering the number of expansion layer neurons (Kenyon cells) and their dendritic complexity, we find that input number, but not cell number, tunes odor selectivity. Simple odor discrimination performance is maintained when Kenyon cell number is reduced and augmented by Kenyon cell expansion.

}, doi = {10.1101/2023.01.25.525425}, url = {https://doi.org/10.1101/2023.01.25.525425}, author = {Maria Ahmed and Adithya E. Rajagopalan and Yijie Pan and Ye Li and Donnell L. Williams and Erik A. Pedersen and Manav Thakral and Angelica Previero and Kari C. Close and Christina P. Christoforou and Dawen Cai and Glenn C. Turner and Josephine Clowney} } @article {69127, title = {Model-Based Inference of Synaptic Plasticity Rules}, journal = {bioRxiv}, year = {2023}, month = {2023 Dec 12}, abstract = {

Understanding learning through synaptic plasticity rules in the brain is a grand challenge for neuroscience. Here we introduce a novel computational framework for inferring plasticity rules from experimental data on neural activity trajectories and behavioral learning dynamics. Our methodology parameterizes the plasticity function to provide theoretical interpretability and facilitate gradient-based optimization. For instance, we use Taylor series expansions or multilayer perceptrons to approximate plasticity rules, and we adjust their parameters via gradient descent over entire trajectories to closely match observed neural activity and behavioral data. Notably, our approach can learn intricate rules that induce long nonlinear time-dependencies, such as those incorporating postsynaptic activity and current synaptic weights. We validate our method through simulations, accurately recovering established rules, like Oja{\textquoteright}s, as well as more complex hypothetical rules incorporating reward-modulated terms. We assess the resilience of our technique to noise and, as a tangible application, apply it to behavioral data from\ Drosophila\ during a probabilistic reward-learning experiment. Remarkably, we identify an active forgetting component of reward learning in flies that enhances the predictive accuracy of previous models. Overall, our modeling framework provides an exciting new avenue to elucidate the computational principles governing synaptic plasticity and learning in the brain.

}, doi = {10.1101/2023.12.11.571103}, url = {https://doi.org/10.1101/2023.12.11.571103}, author = {Yash Mehta and Danil Tyulmankov and Adithya E. Rajagopalan and Glenn C. Turner and James E. Fitzgerald and Jan Funke} } @article {68537, title = {Reward expectations direct learning and drive operant matching in Drosophila}, journal = {Proceedings of the National Academy of Sciences of the U.S.A.}, volume = {120}, year = {2023}, month = {2023 Sep 26}, pages = {e2221415120}, abstract = {

Foraging animals must use decision-making strategies that dynamically adapt to the changing availability of rewards in the environment. A wide diversity of animals do this by distributing their choices in proportion to the rewards received from each option, Herrnstein{\textquoteright}s operant matching law. Theoretical work suggests an elegant mechanistic explanation for this ubiquitous behavior, as operant matching follows automatically from simple synaptic plasticity rules acting within behaviorally relevant neural circuits. However, no past work has mapped operant matching onto plasticity mechanisms in the brain, leaving the biological relevance of the theory unclear. Here we discovered operant matching in\ Drosophila\ and showed that it requires synaptic plasticity that acts in the mushroom body and incorporates the expectation of reward. We began by developing a novel behavioral paradigm to measure choices from individual flies as they learn to associate odor cues with probabilistic rewards. We then built a model of the fly mushroom body to explain each fly{\textquoteright}s sequential choice behavior using a family of biologically-realistic synaptic plasticity rules. As predicted by past theoretical work, we found that synaptic plasticity rules could explain fly matching behavior by incorporating stimulus expectations, reward expectations, or both. However, by optogenetically bypassing the representation of reward expectation, we abolished matching behavior and showed that the plasticity rule must specifically incorporate reward expectations. Altogether, these results reveal the first synaptic level mechanisms of operant matching and provide compelling evidence for the role of reward expectation signals in the fly brain.

}, doi = {10.1073/pnas.2221415120}, url = {https://doi.org/10.1073/pnas.2221415120}, author = {Adithya E. Rajagopalan and Ran Darshan and Karen L. Hibbard and James E. Fitzgerald and Glenn C. Turner} }