Long-term dynamics of CA1 hippocampal place codes Suzy Xu and Emika Lisberger BioNB 4110 April 21, 2014
Nature Neuroscience • Published in Volume 16, Issue 3 • Subset of Nature Publishing Group • Impact Factor (as of 2012) is 15.251 • Ranked 6 th out of 251 journals in the “Neuroscience” category
Dr. Mark Schnitzer (PI) • Education: • BA in physics from Harvard • MA in physics from Princeton • PhD in physics from Princeton • Positions: • Investigator of the Howard Hughes Medical Institute • Associate Professor of Biology and Applied Physics at Stanford University Designed experiments, • Current Research: wrote the paper, and supervised • In vivo two-photon fluorescence imaging studies of cerebellar- the study dependent learning and memory • Fiber optic fluorescence microendoscopy of the hippocampus, thalamus, and inner ear • Massively parallel brain imaging in live fruit flies
Yaniv Ziv • Education: • B.S. in Biology from The Hebrew University of Jerusalem in 2001 • PhD in Neurobiology from the Weizmann Institute of Science in 2007 • Position: • Postdoc under Mark Schnizter in the Designed Department of Biology at Stanford University experiments, acquired data, and wrote the • Current Research: paper • Effects of experience on structural and functional interactions between different types of hippocampal cells in vivo
Laurie D. Burns • Education: • B.S. in Physics from MIT • PhD in Applied Physics from Stanford University in 2012, working on the development of miniature microscope technology • Current Work: Designed experiments, • Consultant at Inscopix Inc. acquired data, analyzed data, • Was previously one of the founding built equipment and wrote the scientists paper
Eric D. Cocker • Education: • B.S., M.S., and PhD in Mechanical Engineering at Stanford University • Current Work: • A member of the Founding Built the equipment used team at Inscopix, as the Founding Principal Engineer
Elizabeth O. Hamel • Acquired the data used in this study. • No other information found online
Kunal K. Ghosh • Education: • B.S. at The Wharton School (University of Pennsylvania) • B.S.E in Electrical Engineering at University of Pennsylvania • M.S. and PhD in Electrical Engineering at Stanford University • Postdoc in the Department of Built the equipment used Biology at Stanford • Current Work: • Founder and CEO of Inscopix Inc.
Lacey J. Kitch • Education: • B.S. in Physics and Math with Computer Science at MIT • M.S. in Electrical Engineering at Stanford University • Current Work: • Pursuing a PhD in Electrical Analyzed and wrote the paper Engineering at Stanford • Interested in neural computation of processes in living brains.
Abbas El Gamal Education: • • B.S. Honors in Electrical Engineering at Cairo University • M.S. in Statistics and Ph.D. in Electrical Engineering at Stanford University • Current Position: • Hitachi America Professor in the School of Engineering • A member of the National Academy of Engineering and a Fellow of the IEEE (Institute of Electrical and Electronics Engineers) Supervised the study • Plays key roles in several Silicon Valley companies. • Research Contributions: • Include information theory, Field Programmable Gate Array and digital imaging devices and systems
The Experiment • Abstract: “ Using Ca 2+ imaging in freely behaving mice that repeatedly explored a familiar environment, we tracked thousands of CA1 pyramidal cells’ place fields.” • Goal: To find out the long-term stability of place fields using one-photon imaging
General Methods • Used GCaMP3 to express Ca 2+ in pyramidal cells by injection of a viral vector into CA1 • Used miniaturized microendoscope for Ca 2+ imaging in four freely behaving mice • Tracked Ca 2+ dynamics of 515 to 1,040 pyramidal cells per mouse on repeated visits to a familiar track • Used water rewards to train the mice to run up and down the track • Recorded for 45 days
One-Photon Microendoscopy • Imaging technique used in this study • Inserted optical fiber that acts as a lens into brain tissue • Lens diffracts light to one point • Base stayed attached on the mouse brain for 45 days
Two-Photon Microscopy Basics: • • Developed by Winfried Denk in the lab of Watt W . Webb at Cornell University in 1990 • Allows imaging of live tissue up to 1.6 mm in depth Two Photon vs. One Photon: • • 2 photons of half the energy are excited simultaneously • More localized excitation • Fluorescence photon is emitted Benefits: • • Images only what is labeled with a fluorescent dye • Less energy � less damage to sample • Longer wavelength � less scattering (better resolution along z-axis) I ∝ 1 E = hv = hc The Schnitzer lab is working to incorporate two- • λ 4 photon microscopy into their microendoscopes λ
Three-Photon Microscopy Developed by Xu lab at Cornell • Can image individual neurons • Can image hippocampus without removing overlying • tissue Figure of biological imaging! •
Hippocampus (Hp) • Under the cerebral cortex, and in the medial temporal lobe • Information travel in the tri- synaptic pathway • Responsible for episodic memory and context processing • Intact Hp is especially important for responding to information about spatial relations
Place Fields Place cells in an intact hippocampus form place fields when an animal is put into a novel environment
Initial Imaging: Calcium activity
Consistent over time • No damage to cells • Microscope is accurate
Distribution of size and location
Direction Preference Data pooled from four mice, • on day 15 Rearranged firing data for this • graph Left place cells fire for leftward • movement only (c) Right place cell fire for • rightward movement only (d)
Which cells fire? Pooled data from 4 mice • Gaussian smoothed • density of overlapping right and left movement Place fields were • consistent throughout the whole experiment, but rearranged 20% of cells that are place • cells for leftward and rightward movement
Decrease of active cells Ca 2+ activity of 826 cells • in one mouse over 45 days (a) The number of sessions a • cell is active for (b) Probability of recurrence • from session to session of place fields declines with time (c) When the place fields are • recurrent, their locations are generally identical (d)
15-25% Recurrence
After the experiment Why is there recurrence? • Determined that it was not because of physiological or coding parameters (Ca 2+ activity patterns) Is the 15-25% overall recurrence sufficient to retain a stable spatial representation? • Using Bayesian decoding, they determined if they could reconstruct the mouse’s location from the Ca 2+ imaging data
Bayesian Decoding • What is it? • Using the arrival times of Ca 2+ activity and the probability of seeing a certain stimulus to develop a neural code
Bayesian Decoding • Figure h • Same-day decoding � used data from the same day to decode place • Time-lapse decoding � used data from day 5 to decode place in days 10, 20, and 35 • Figure i • Shows median error over time • Same day: ~8% error • Time lapse: ~15% error • Figure j • Cumulative percentage of error
Potential Flaws • Data • The authors disregarded the fact that GCaMP3 does not record single spikes, but instead bursts of spikes. • Analysis • How do we know from this data that you can get a stable spatial recognition? How could this experiment be improved?
Discussion Questions • What are place cells? Where are they found in the brain? • What is the optical imaging method used in this study? Briefly describe how it works. • How did the authors test whether the 15-25% place cell recurrence was sufficient to determine the mouse’s location? • Why is this paper so progressive, what does this contribute/ mean to future neuroscience research? • What are some potential flaws in this paper? What future experiments could these neurobiologists do to improve the results?
Recommend
More recommend
