89 research outputs found
Probing clustering in neural network representations
Neural network representations contain structure beyond what was present in
the training labels. For instance, representations of images that are visually
or semantically similar tend to lie closer to each other than to dissimilar
images, regardless of their labels. Clustering these representations can thus
provide insights into dataset properties as well as the network internals. In
this work, we study how the many design choices involved in neural network
training affect the clusters formed in the hidden representations. To do so, we
establish an evaluation setup based on the BREEDS hierarchy, for the task of
subclass clustering after training models with only superclass information. We
isolate the training dataset and architecture as important factors affecting
clusterability. Datasets with labeled classes consisting of unrelated
subclasses yield much better clusterability than those following a natural
hierarchy. When using pretrained models to cluster representations on
downstream datasets, models pretrained on subclass labels provide better
clusterability than models pretrained on superclass labels, but only when there
is a high degree of domain overlap between the pretraining and downstream data.
Architecturally, we find that normalization strategies affect which layers
yield the best clustering performance, and, surprisingly, Vision Transformers
attain lower subclass clusterability than ResNets
Latency and Selectivity of Single Neurons Indicate Hierarchical Processing in the Human Medial Temporal Lobe
Neurons in the temporal lobe of both monkeys and humans show selective responses to classes of visual stimuli and even to specific individuals. In this study, we investigate the latency and selectivity of visually responsive neurons recorded from microelectrodes in the parahippocampal cortex, entorhinal cortex, hippocampus, and amygdala of human subjects during a visual object presentation task. During 96 experimental sessions in 35 subjects, we recorded from a total of 3278 neurons. Of these units, 398 responded selectively to one or more of the presented stimuli. Mean response latencies were substantially larger than those reported in monkeys. We observed a highly significant correlation between the latency and the selectivity of these neurons: the longer the latency the greater the selectivity. Particularly, parahippocampal neurons were found to respond significantly earlier and less selectively than those in the other three regions. Regional analysis showed significant correlations between latency and selectivity within the parahippocampal cortex, entorhinal cortex, and hippocampus, but not within the amygdala. The later and more selective responses tended to be generated by cells with sparse baseline firing rates and vice versa. Our results provide direct evidence for hierarchical processing of sensory information at the interface between the visual pathway and the limbic system, by which increasingly refined and specific representations of stimulus identity are generated over time along the anatomic pathways of the medial temporal lobe
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