Spike Train Coactivity Encodes Learned Natural Stimulus Invariances in Songbird Auditory Cortex

Abstract

The capacity for sensory systems to encode relevant information that is invariant to many stimulus changes is central to normal, real-world, cognitive function. This invariance is thought to be reflected in the complex spatiotemporal activity patterns of neural populations, but our understanding of population-level representational invariance remains coarse. Applied topology is a promising tool to discover invariant structure in large datasets. Here, we use topological techniques to characterize and compare the spatiotemporal pattern of coactive spiking within populations of simultaneously recorded neurons in the secondary auditory region caudal medial neostriatum of European starlings (Sturnus vulgaris). We show that the pattern of population spike train coactivity carries stimulus-specific structure that is not reducible to that of individual neurons. We then introduce a topology-based similarity measure for population coactivity that is sensitive to invariant stimulus structure and show that this measure captures invariant neural representations tied to the learned relationships between natural vocalizations. This demonstrates one mechanism whereby emergent stimulus properties can be encoded in population activity, and shows the potential of applied topology for understanding invariant representations in neural populations.

Sparse ensemble neural code for a complete vocal repertoire

“…These noise correlations have been shown to exist in the avian auditory system and to be involved in the representation of invariant features of learned acoustical categories. 59 , 60 They might also play a role in the representation of call types and a decoder (machine or biological) could exploit them to increase performance. We might therefore again be underestimating the decoder performance.…”

From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings

“…In the subthalamic nucleus, clustering of spike trains may help to understand the pathophysiology of movement disorders (Kaku et al, 2019 ; Sukiban et al, 2019 ), while the basolateral amygdala or the hippocampus can offer ideas about general neural interactions (Hojjatinia et al, 2020 ; Oghazian et al, 2020 ). Correlations calculated after vigorous spike sorting in multichannel data gave rise to a promising neural encoding capacity hypothesis (Isbister et al, 2021 ) and neural populational activity dynamics (Theilman et al, 2021 ).…”

Context-dependent sensory modulation underlies Bayesian vocal sequence perception

“…We computed neural response similarity as the cosine similarity of the Gaussian convolved spike vectors, which has been effectively used to find similarity in spike trains in the past ( 38 ). A number of different similarity metrics could have been used in its place, for example, correlation coefficients ( 39, 40 ) and Euclidean distance between Gaussian convolved spike trains. We compared the cosine similarity to several other similarity metrics used in neural analyses including the correlation coefficient, Euclidean distance, and Manhattan distance, and found broadly similar results (Fig 12).…”

“…A number of different similarity metrics could have been used in its place, for example, cor-relation coefficients (39,40) and Euclidean distance between Gaussian convolved spike trains.…”