@article {69054, title = {Effects of stochastic coding on olfactory discrimination in flies and mice.}, journal = {PLoS Biology}, volume = {21}, year = {2023}, month = {2023 Oct 31}, pages = {e3002206}, abstract = {

Sparse coding can improve discrimination of sensory stimuli by reducing overlap between their representations. Two factors, however, can offset sparse coding{\textquoteright}s benefits: similar sensory stimuli have significant overlap and responses vary across trials. To elucidate the effects of these 2 factors, we analyzed odor responses in the fly and mouse olfactory regions implicated in learning and discrimination-the mushroom body (MB) and the piriform cortex (PCx). We found that neuronal responses fall along a continuum from extremely reliable across trials to extremely variable or stochastic. Computationally, we show that the observed variability arises from noise within central circuits rather than sensory noise. We propose this coding scheme to be advantageous for coarse- and fine-odor discrimination. More reliable cells enable quick discrimination between dissimilar odors. For similar odors, however, these cells overlap and do not provide distinguishing information. By contrast, more unreliable cells are decorrelated for similar odors, providing distinguishing information, though these benefits only accrue with extended training with more trials. Overall, we have uncovered a conserved, stochastic coding scheme in vertebrates and invertebrates, and we identify a candidate mechanism, based on variability in a winner-take-all (WTA) inhibitory circuit, that improves discrimination with training.

}, keywords = {Animals, Diptera, Learning, Mice, Odorants, Olfactory Pathways, Olfactory Perception, Smell}, issn = {1545-7885}, doi = {10.1371/journal.pbio.3002206}, author = {Srinivasan, Shyam and Daste, Simon and Modi, Mehrab N and Turner, Glenn C and Fleischmann, Alexander and Navlakha, Saket} } @article {65655, title = {The Drosophila mushroom body: From architecture to algorithm in a learning circuit.}, journal = {Annual Review of Neuroscience}, volume = {43}, year = {2020}, month = {2020 Jul 08}, pages = {465-484}, abstract = {

The brain contains a relatively simple circuit for forming Pavlovian associations, yet it achieves many operations common across memory systems. Recent advances have established a clear framework for learning and revealed the following key operations: ) pattern separation, whereby dense combinatorial representations of odors are preprocessed to generate highly specific, nonoverlapping odor patterns used for learning; ) convergence, in which sensory information is funneled to a small set of output neurons that guide behavioral actions; ) plasticity, where changing the mapping of sensory input to behavioral output requires a strong reinforcement signal, which is also modulated by internal state and environmental context; and ) modularization, in which a memory consists of multiple parallel traces, which are distinct in stability and flexibility and exist in anatomically well-defined modules within the network. Cross-module interactions allow for higher-order effects where past experience influences future learning. Many of these operations have parallels with processes of memory formation and action selection in more complex brains.

}, issn = {1545-4126}, doi = {10.1146/annurev-neuro-080317-0621333}, author = {Modi, Mehrab N and Shuai, Yichun and Turner, Glenn C} } @article {65386, title = {The Mushroom Body: From Architecture to Algorithm in a Learning Circuit.}, journal = {Annual Review of Neuroscience}, year = {2020}, month = {2020 Apr 13}, abstract = {

The brain contains a relatively simple circuit for forming Pavlovian associations, yet it achieves many operations common across memory systems. Recent advances have established a clear framework for learning and revealed the following key operations: ) pattern separation, whereby dense combinatorial representations of odors are preprocessed to generate highly specific, nonoverlapping odor patterns used for learning; ) convergence, in which sensory information is funneled to a small set of output neurons that guide behavioral actions; ) plasticity, where changing the mapping of sensory input to behavioral output requires a strong reinforcement signal, which is also modulated by internal state and environmental context; and ) modularization, in which a memory consists of multiple parallel traces, which are distinct in stability and flexibility and exist in anatomically well-defined modules within the network. Cross-module interactions allow for higher-order effects where past experience influences future learning. Many of these operations have parallels with processes of memory formation and action selection in more complex brains. Expected final online publication date for the , Volume 43 is July 8, 2020. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

}, issn = {1545-4126}, doi = {10.1146/annurev-neuro-080317-0621333}, author = {Modi, Mehrab N and Shuai, Yichun and Turner, Glenn C} } @article {65254, title = {Two-photon imaging with silicon photomultipliers.}, journal = {Optics Express}, volume = {27}, year = {2019}, month = {2019 Nov 25}, pages = {35830-35841}, abstract = {

We compared performance of recently developed silicon photomultipliers (SiPMs) to GaAsP photomultiplier tubes (PMTs) for two-photon imaging of neural activity. Despite higher dark counts, SiPMs match or exceed the signal-to-noise ratio of PMTs at photon rates encountered in typical calcium imaging experiments due to their low pulse height variability. At higher photon rates encountered during high-speed voltage imaging, SiPMs substantially outperform PMTs.

}, issn = {1094-4087}, doi = {10.1364/OE.27.035830}, author = {Modi, Mehrab N and Daie, Kayvon and Turner, Glenn C and Podgorski, Kaspar} }