
BARRELS XXXIII POSTER ABSTRACTS
periods is essential. Yet, accurately classifying the precise moment of touch is time-consuming and labor intensive.
Current classification techniques rely on semi-automated curation, infrared beam crossings, or hard coded distance to
object thresholds. All involve substantial tradeoffs in analysis time or accuracy. We propose the use of convolutional
neural networks to fully automate the identification of whisker-object touch periods. We leveraged TensorFlow and the
MobileNetV2 pre-trained base model with added Pooling and Dense layers to make fast, accurate, and automated
classification of single-whisker touch events from 1000fps video. We used a manually curated set of 92,000 touch and
near-touch (< 2mm distance to pole) frames to train our model in ~7 minutes on Google Colab GPUs. Our initial model
achieves classification AUC levels of >0.98 for these challenging frames. We plan to implement data augmentation
approaches to further increase the classification accuracy. We have also created an intuitive pipeline for data-loading,
image curation and output of class prediction probabilities with the goal of making automated whisker-touch classification
easily accessible to all labs working in this experimental paradigm. NINDS grant R01NS102808
Krista Marrero (1), Krithiga Aruljothi (2), Chengchun Gao (3), Edward Zagha (1,2)
[1] Dept of Neuroscience; [2] Dept of Psychology; [3] Dept of Bioengineering; University of California, Riverside
Effects of Prestimulus Activity on Performance of a Selective Detection Task
In the field of sensory detection, most studies focus on sensorimotor processing after a stimulus arrives (post-stimulus).
However, the prestimulus neural activity can also alter stimulus encoding and behavioral outcomes. We wondered what
aspects of prestimulus activity could impact task performance. We trained mice in a selective whisker detection task, in
which they learned to respond to target stimuli in one whisker field and ignore distractor stimuli in the contralateral whisker
field. During expert task performance, we used widefield Ca
2+
imaging to assess prestimulus and post-stimulus neural
activity broadly across frontal and parietal cortices. We find that, indeed, prestimulus activity strongly predicts trial
outcome, with lower activity preceding response trials (hits and false alarms). Interestingly, the activity predictive of trial
outcome does not localize to whisker regions but globally distributes throughout dorsal neocortex. Using principal
component analysis, we then demonstrate that response trials are associated with a distinct and less variable prestimulus
neural state. We interpret these findings as supporting an optimal prestimulus neural state for task performance that
presents globally, affects response to both target and distractor stimuli, and corresponds with a behavioral state. Our
findings emphasize the influence of prestimulus activity on trial outcome and the relationships between global neural
dynamics and behavioral state.
Katherine S. Matho (1), Dhananjay Huilgol (1), William Galbavy (1,4), Gukhan Kim (1), Miao He (1α), Xu An (1), Jiangteng
Lu (1b), Priscilla Wu (1), Daniela J. Di Bella (2), Ashwin S. Shetty (2), Ramesh Palaniswamy (1), Joshua Hatfield (1),
Ricardo Raudales (1,4), Arun Narasimhan (1), Eric Gamache (1), Jesse Levine (1,5), Jason Tucciarone (1,5c), Partha
Mitra (1), Pavel Osten (1), Paola Arlotta (2,3), Z. Josh Huang (1)
[1] Cold Spring Harbor Laboratory, Cold Spring Harbor; [2] Dept. of Stem Cell and Regenerative Biology, Harvard
University; [3] Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard University; [4] Program in
Neuroscience, Stony Brook University; [5] Program in Neuroscience and Medical Scientist Training Program, Stony Brook
University; [1α]-Fudan University; [1b] Shanghai Jiaotong University Medical School; [c] Dept. of Psychiatry, Stanford
University School of Medicine
Genetic dissection of glutamatergic neuron subpopulations and developmental trajectories in the cerebral cortex
Diverse types of glutamatergic pyramidal neurons (PyNs) mediate the myriad processing streams and output channels of
the cerebral cortex, yet all derive from neural progenitors of the embryonic dorsal telencephalon. Here, we establish
genetic strategies and tools for dissecting and fate mapping PyN subpopulations based on their developmental and
molecular programs. We leverage key transcription factors and effector genes to systematically target the temporal
patterning programs in progenitors and differentiation programs in postmitotic neurons. We generated over a dozen of
temporally inducible mouse Cre and Flp knock-in driver lines to enable combinatorial targeting of major progenitor types
and projection classes. Intersectional converter lines confer viral access to specific subsets defined by developmental
origin, marker expression, anatomical location and projection targets. These strategies establish an experimental
framework for multi-modal characterization of PyN subpopulations and tracking their developmental trajectories toward
elucidating the organization and assembly of cortical processing networks and output channels. This work was supported
in part by the NIH grants 5R01MH101268-05 and 5U19MH114821-03 to Z.J.H. and P.A., 1S10OD021759-01 to Z.J.H.,
the CSHL Robertson Neuroscience Fund to Z.J.H. P.O. is supported by NIH U01 MH114824-01.