These files contain code used to segment D. virilis acoustic duets, quantification of courtship behaviors during acoustic duets, and measurements of duet song features.
Mondal, Shanka Subhra; Webb, Taylor; Cohen, Jonathan
Abstract:
A dataset of Raven’s Progressive Matrices (RPM)-like problems using realistically rendered
3D shapes, based on source code from CLEVR (a popular visual-question-answering dataset) (Johnson, J., Hariharan, B., Van Der Maaten, L., Fei-Fei, L., Lawrence Zitnick, C., & Girshick, R. (2017). Clevr: A diagnostic dataset for compositional language and elementary visual reasoning. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2901-2910)).
This archive contains spike trains simultaneously recorded from ganglion cells in the tiger salamander retina with a multi-electrode array while viewing a repeated natural movie clip. These data have been analyzed in previous papers, notably Puchalla et al. Neuron 2005 and Schneidman et al. Nature 2006.
Monitoring the attention of others is fundamental to social cognition. Most of the literature on the topic assumes that our social cognitive machinery is tuned specifically to the gaze direction of others as a proxy for attention. This standard assumption reduces attention to an externally visible parameter. Here we show that this assumption is wrong and a deeper, more meaningful representation is involved. We presented subjects with two cues about the attentional state of a face: direction of gaze and emotional expression. We tested whether people relied predominantly on one cue, the other, or both. If the traditional view is correct, then the gaze cue should dominate. Instead, people employed a variety of strategies, some relying on gaze, some on expression, and some on an integration of cues. We also assessed people’s social cognitive ability using two, independent, standard tests. If the traditional view is correct, then social cognitive ability, as assessed by the independent tests, should correlate with the degree to which people successfully use the gaze cue to judge the attention state of the face. Instead, social cognitive ability correlated best with the degree to which people successfully integrated the cues together, instead of with the use of any one specific cue. The results suggest a rethink of a fundamental component of social cognition: monitoring the attention of others involves constructing a deep model that is informed by a combination of cues. Attention is a rich process and monitoring the attention of others involves a similarly rich representation.
Chang, Claire H. C.; Lazaridi, Christina; Yeshurun, Yaara; Norman, Kenneth A.; Hasson, Uri
Abstract:
This study examined how the brain dynamically updates event representations by integrating new information over multiple minutes while segregating irrelevant input. A professional writer custom-designed a narrative with two independent storylines, interleaving across minute-long segments (ABAB). In the last (C) part, characters from the two storylines meet and their shared history is revealed. Part C is designed to induce the spontaneous recall of past events, upon the recurrence of narrative motifs from A/B, and to shed new light on them. Our fMRI results showed storyline-specific neural patterns, which were reinstated (i.e. became more active) during storyline transitions. This effect increased along the processing timescale hierarchy, peaking in the default mode network. Similarly, the neural reinstatement of motifs was found during part C. Furthermore, participants showing stronger motif reinstatement performed better in integrating A/B and C events, demonstrating the role of memory reactivation in information integration over intervening irrelevant events.
Pacheco, Diego A; Thiberge, Stephan; Pnevmatikakis, Eftychios; Murthy, Mala
Abstract:
Sensory pathways are typically studied starting at receptor neurons and following postsynaptic neurons into the brain. However, this leads to a bias in analysis of activity towards the earliest layers of processing. Here, we present new methods for volumetric neural imaging with precise across-brain registration, to characterize auditory activity throughout the entire central brain of Drosophila and make comparisons across trials, individuals, and sexes. We discover that auditory activity is present in most central brain regions and in neurons responsive to other modalities. Auditory responses are temporally diverse, but the majority of activity is tuned to courtship song features. Auditory responses are stereotyped across trials and animals in early mechanosensory regions, becoming more variable at higher layers of the putative pathway, and this variability is largely independent of spontaneous movements. This study highlights the power of using an unbiased, brain-wide approach for mapping the functional organization of sensory activity.
Extrapolation -- the ability to make inferences that go beyond the scope of one's experiences -- is a hallmark of human intelligence. By contrast, the generalization exhibited by contemporary neural network algorithms is largely limited to interpolation between data points in their training corpora. In this paper, we consider the challenge of learning representations that support extrapolation. We introduce a novel visual analogy benchmark that allows the graded evaluation of extrapolation as a function of distance from the convex domain defined by the training data. We also introduce a simple technique, context normalization, that encourages representations that emphasize the relations between objects. We find that this technique enables a significant improvement in the ability to extrapolate, considerably outperforming a number of competitive techniques.
Pereira, Talmo D.; Aldarondo, Diego E.; Willmore, Lindsay; Kislin, Mikhail; Wang, Samuel S.-H.; Murthy, Mala; Shaevitz, Joshua W.
Abstract:
Recent work quantifying postural dynamics has attempted to define the repertoire of behaviors performed by an animal. However, a major drawback to these techniques has been their reliance on dimensionality reduction of images which destroys information about which parts of the body are used in each behavior. To address this issue, we introduce a deep learning-based method for pose estimation, LEAP (LEAP Estimates Animal Pose). LEAP automatically predicts the positions of animal body parts using a deep convolutional neural network with as little as 10 frames of labeled data for training. This framework consists of a graphical interface for interactive labeling of body parts and software for training the network and fast prediction on new data (1 hr to train, 185 Hz predictions). We validate LEAP using videos of freely behaving fruit flies (Drosophila melanogaster) and track 32 distinct points on the body to fully describe the pose of the head, body, wings, and legs with an error rate of <3% of the animal's body length. We recapitulate a number of reported findings on insect gait dynamics and show LEAP's applicability as the first step in unsupervised behavioral classification. Finally, we extend the method to more challenging imaging situations (pairs of flies moving on a mesh-like background) and movies from freely moving mice (Mus musculus) where we track the full conformation of the head, body, and limbs.
Small changes in word choice can lead to dramatically different interpretations of narratives. How does the brain accumulate and integrate such local changes to construct unique neural representations for different stories? In this study we created two distinct narratives by changing only a few words in each sentence (e.g. “he” to “she” or “sobbing” to “laughing”) while preserving the grammatical structure across stories. We then measured changes in neural responses between the two stories. We found that the differences in neural responses between the two stories gradually increased along the hierarchy of processing timescales. For areas with short integration windows, such as early auditory cortex, the differences in neural responses between the two stories were relatively small. In contrast, in areas with the longest integration windows at the top of the hierarchy, such as the precuneus, temporal parietal junction, and medial frontal cortices, there were large differences in neural responses between stories. Furthermore, this gradual increase in neural difference between the stories was highly correlated with an area’s ability to integrate information over time. Amplification of neural differences did not occur when changes in words did not alter the interpretation of the story (e.g. “sobbing” to “crying”). Our results demonstrate how subtle differences in words are gradually accumulated and amplified along the cortical hierarchy as the brain constructs a narrative over time.
Recent advances in experimental techniques have allowed the simultaneous recordings of
populations of hundreds of neurons, fostering a debate about the nature of the collective
structure of population neural activity. Much of this debate has focused on the
empirical findings of a phase transition in the parameter space of maximum entropy
models describing the measured neural probability distributions, interpreting this phase
transition to indicate a critical tuning of the neural code. Here, we instead focus on the
possibility that this is a first-order phase transition which provides evidence that the
real neural population is in a `structured', collective state. We show that this collective
state is robust to changes in stimulus ensemble and adaptive state. We find that the
pattern of pairwise correlations between neurons has a strength that is well within the
strongly correlated regime and does not require fine tuning, suggesting that this state is
generic for populations of 100+ neurons. We find a clear correspondence between the
emergence of a phase transition, and the emergence of attractor-like structure in the
inferred energy landscape. A collective state in the neural population, in which neural
activity patterns naturally form clusters, provides a consistent interpretation for our
results.
Our daily lives revolve around sharing experiences and memories with others. When different people recount the same events, how similar are their underlying neural representations? In this study, participants viewed a fifty-minute audio-visual movie, then verbally described the events while undergoing functional MRI. These descriptions were completely unguided and highly detailed, lasting for up to forty minutes. As each person spoke, event-specific spatial patterns were reinstated (movie-vs.-recall correlation) in default network, medial temporal, and high-level visual areas; moreover, individual event patterns were highly discriminable and similar between people during recollection (recall-vs.-recall similarity), suggesting the existence of spatially organized memory representations. In posterior medial cortex, medial prefrontal cortex, and angular gyrus, activity patterns during recall were more similar between people than to patterns elicited by the movie, indicating systematic reshaping of percept into memory across individuals. These results reveal striking similarity in how neural activity underlying real-life memories is organized and transformed in the brains of different people as they speak spontaneously about past events.
