Published in Journal of Neuroscience, 2018
We elucidated the function of corticotropin-releasing hormone (CRH)-expressing amacrine cells labeled in Cre-transgenic mice.
Park, S. J., Pottackal, J., Ke, J. B., Jun, N. Y., Rahmani, P., Kim, I. J., Singer, J. H. & Demb, J. B. (2018). Convergence and divergence of CRH amacrine cells in mouse retinal circuitry. Journal of Neuroscience, 38(15), 3753-3766.
Published in bioRxiv, 2019
We found that when two separate grating stimuli are presented simultaneously to the monkey, distinct distributions of positive and negative correlations emerge, depending on whether the two neurons in the pair both respond more strongly to the same vs. different individual stimuli. Neural pairs that shared the same stimulus preference were more likely to show positively correlated spike count variability whereas those with different preferences were more likely to show negative correlations, suggesting that the population response to one particular stimulus may be enhanced over the other on any given trial. This pattern of results was not present when the two gratings were superimposed and formed a single plaid, supporting the interpretation that the pattern of correlated fluctuations is related to the segregation of individual objects in the visual scene.
Jun, N. Y., Ruff, D. A., Kramer, L. E., Bowes, B., Tokdar, S. T., Cohen, M. R., & Groh, J. M. (2019). Patterns of neural correlations in V1 vary with the number of objects. bioRxiv, 777912.
Published in eNeuro, 2019
We assessed overall neural activation by measuring the amplitude and temporal progression of evoked spiking. Using γ-range (30–80 Hz) local field potential (LFP) power as an assay for local network engagement, we examined the recruitment of cortical network activity by each of the three ChR variants with distinct temporal profiles: Chronos, Chrimson, and ChR2. All variants caused light-evoked increases in firing in vivo, but each demonstrated different temporal patterning of evoked activity. In addition, the three ChRs had distinct effects on cortical γ-band activity. Our findings suggest the properties of optogenetic tools can substantially affect their efficacy in vivo, as well their engagement of circuit resonance. Together, our data suggest that the kinetic properties of engineered opsin tools affect optogenetic interactions with local circuit activity and should be a key factor in experimental design.
Jun, N. Y., & Cardin, J. A. (2020). Activation of distinct Channelrhodopsin variants engages different patterns of network activity. Eneuro, 7(1).
Published in eLife, 2020
We combined structural, functional, and optogenetic analyses of the mouse retina to discover that surround inhibition of the AII depends primarily on a single interneuron type, the NOS-1 Amacrine Cell: a multistratified, axon-bearing GABAergic cell, with dendrites in both ON and OFF synaptic layers, but with a pure ON (depolarizing) response to light.
Park, S. J., Lieberman, E. E., Ke, J. B., Rho, N., Ghorbani, P., Rahmani, P., Jun, N. Y., Lee, H. L., Kim, I. J., & Demb, J. B. (2020). Connectomic analysis reveals an interneuron with an integral role in the retinal circuit for night vision. Elife, 9, e56077.
Published in Nature, 2021
We determined how ON and OFF receptive field mosaics should be arranged to optimize the encoding of natural scenes using theory and modeling. We found that information is maximized when these mosaics are anti-aligned. We tested this prediction by using large-scale measurements of RGC population responses to estimate RF mosaics of multiple RGC types from rat and primate. We found that ON and OFF RGC pairs with similar feature selectivity exhibit anti-aligned RF mosaics, consistent with theory. ON and OFF types that encode distinct features exhibit independent mosaics. These results extend the ‘efficient coding’ idea beyond the RFs of individual cells to predict how populations of diverse RGC types are spatially arranged.
Roy, S., Jun, N. Y., Davis, E. L., Pearson, J., & Field, G. D. (2021). Inter-mosaic coordination of retinal receptive fields. Nature, 592(7854), 409-413.
Published in NeurIPS, 2021
We proposed a method that combines fast, stable dimensionality reduction with a soft tiling of the resulting neural manifold, allowing dynamics to be approximated as a probability flow between tiles.
Draelos, A., Gupta, P., Jun, N. Y., Sriworarat, C., & Pearson, J. (2021). Bubblewrap: Online tiling and real-time flow prediction on neural manifolds. arXiv preprint arXiv:2108.13941.
Published in PNAS, 2021
What determines the relative arrangement of receptive field mosaics? Short answer: Scene statistics and noise! In this paper, we showed how efficient coding theory, which hypothesizes that the nervous system minimizes the amount of redundant information it encodes, can predict the relative spatial arrangement of ON and OFF mosaics. The most information-efficient arrangements are determined both by levels of noise in the system and the statistics of natural images.
Jun, N. Y., Field, G.D., & Pearson, J. (2021). Scene statistics and noise determine the relative arrangement of receptive field mosaics. Proceedings of the National Academy of Sciences, 118(39), e2105115118
Published in NeurIPS, 2022
We used efficient coding theory to present a comprehensive account of mosaic organization in the case of natural videos as the retinal channel capacity— the number of simulated RGCs available for encoding—is varied. In addition, we showed theoretically and in simulation that a transition from mosaic alignment to anti-alignment across pairs of cell types is observed with increasing output noise and decreasing input noise. Together, these results offer a unified perspective on the relationship between retinal mosaics, efficient coding, and channel capacity that can help to explain the stunning functional diversity of retinal cell types.
Code available at: https://github.com/pearsonlab/efficientcoding
Jun, N. Y., Field, G., & Pearson, J. (2022). Efficient coding, channel capacity and the emergence of retinal mosaics
Cell types with progressively larger spatial RFs and briefer temporal RFs form serially as more number of neurons are available. Efficient coding can explain cell type diversity in the retina as well!
Many sensory systems consist of ensembles or grids of ON and OFF detectors spanning sensory space. How should grids of ON and OFF detectors be arranged to optimally encode natural stimuli? It turns out that the preferred detector arrangements depends on sensor noise and the statistical structure of the natural stimuli.
In the retina, bipolar cells are located between input photoreceptors and output RGCs, allowing multiple stages of nonlinear processing. There are multiple types of bipolar cells, with each type known to be activated by specific attributes of visual stimuli such as luminance and the presence or absence of colored inputs. What’s the role of these specialized sensor types in information processing? Can architecture and the neural diversity found in the retina make information processing more efficient?
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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