Biology subjects

DiTullio, R. W.

Publications and source records attributed to DiTullio, R. W..

5 recordsLinked to original sources

bioRxiv2024

Trading Place for Space: Increasing Location Resolution Reduces Contextual Capacity in Hippocampal Codes

Many animals learn cognitive maps of their environment - a simultaneous representation of context, experience, and position. Place cells in the hippocampus, named for their explicit encoding of position, are believed to be a neural substrate of these maps, with place cell "remapping" explaining how this system can represent different contexts. Briefly, place cells alter their firing properties, or "remap", in response to changes in experiential or sensory cues. Substantial sensory changes, produced, e.g., by moving between environments, cause large subpopulations of place cells to change their tuning entirely. While many studies have looked at the physiological basis of remapping, we lack explicit calculations of how the contextual capacity of the place cell system changes as a function of place field firing properties. Here, we propose a geometric approach to understanding population level activity of place cells. Using known firing field statistics, we investigate how changes to place cell firing properties affect the distances between representations of different environments within firing rate space. Using this approach, we find that the number of contexts storable by the hippocampus grows exponentially with the number of place cells, and calculate this exponent for environments of different sizes. We identify a fundamental trade-off between high resolution encoding of position and the number of storable contexts. This trade-off is tuned by place cell width, which might explain the change in firing field scale along the dorsal-ventral axis of the hippocampus. We demonstrate that clustering of place cells near likely points of confusion, such as boundaries, increases the contextual capacity of the place system within our framework and conclude by discussing how our geometric approach could be extended to include other cell types and abstract spaces.

neuroscience↗

bioRxiv2024

Slow and steady: auditory features for discriminating animal vocalizations

We propose that listeners can use temporal regularities - spectro-temporal correlations that change smoothly over time - to discriminate animal vocalizations within and between species. To test this idea, we used Slow Feature Analysis (SFA) to find the most temporally regular components of vocalizations from birds (blue jay, house finch, American yellow warbler, and great blue heron), humans (English speakers), and rhesus macaques. We projected vocalizations into the learned feature space and tested intra-class (same speaker/species) and inter-class (different speakers/species) auditory discrimination by a trained classifier. We found that: 1) Vocalization discrimination was excellent (> 95%) in all cases; 2) Performance depended primarily on the [~]10 most temporally regular features; 3) Most vocalizations are dominated by [~]10 features with high temporal regularity; and 4) These regular features are highly correlated with the most predictable components of animal sounds.

neuroscience↗

bioRxiv2023

Neural signatures of natural behavior in socializing macaques

Our understanding of the neurobiology of primate behavior largely derives from artificial tasks in highly-controlled laboratory settings, overlooking most natural behaviors primate brains evolved to produce1-3. In particular, how primates navigate the multidimensional social relationships that structure daily life4 and shape survival and reproductive success5 remains largely unexplored at the single neuron level. Here, we combine ethological analysis with new wireless recording technologies to uncover neural signatures of natural behavior in unrestrained, socially interacting pairs of rhesus macaques. Single neuron and population activity in prefrontal and temporal cortex unveiled robust encoding of 24 species-typical behaviors, which was strongly modulated by the presence and identity of surrounding monkeys. Male-female partners demonstrated near-perfect reciprocity in grooming, a key behavioral mechanism supporting friendships and alliances6, and neural activity maintained a running account of these social investments. When confronted with an aggressive intruder, behavioral and neural population responses reflected empathy and were buffered by the presence of a partner. By employing an ethological approach to the study of primate neurobiology, we reveal a highly-distributed neurophysiological ledger of social dynamics, a potential computational foundation supporting communal life in primate societies, including our own.

neuroscience↗

bioRxiv2022

Time as a supervisor: temporal regularity and auditory object learning

Sensory systems appear to learn to transform incoming sensory information into perceptual representations, or "objects", that can inform and guide behavior with minimal explicit supervision. Here, we propose that the auditory system can achieve this goal by using time as a supervisor, i.e., by learning features of a stimulus that are temporally regular. We will show that this procedure generates a feature space sufficient to support fundamental computations of auditory perception. In detail, we consider the problem of discriminating between instances of a prototypical class of natural auditory objects, i.e., rhesus macaque vocalizations. We test discrimination in two ethologically relevant tasks: discrimination in a cluttered acoustic background and generalization to discriminate between novel exemplars. We show that an algorithm that learns these temporally regular features affords better or equivalent discrimination and generalization than conventional feature-selection algorithms, i.e., principal component analysis and independent component analysis. Our findings suggest that the slow temporal features of auditory stimuli may be sufficient for parsing auditory scenes and that the auditory brain could utilize these slowly changing temporal features.

neuroscience↗

bioRxiv2022

Neural cognitive signals during spontaneous movements in the macaque

The single neuron basis of cognitive processing in primates has mostly been studied in laboratory settings where movements are severely restricted. It is unclear, therefore, how natural movements might affect neural signatures of cognition in the brain. Moreover, studies in mice indicate that body movements, when measured, account for most of the neural dynamics in the cortex. To examine this issue, we recorded from single neuron ensembles in the prefrontal cortex in moving monkeys performing a cognitive task and characterized eyes, head, and body movements using video tracking. Despite significant trial-to-trial movement variability, single neuron tuning could be precisely measured and decision signals accurately decoded on a single-trial basis. Creating or abolishing spontaneous movements through head restraint and task manipulations had no measurable impact on neural responses. However, encoding models showed that uninstructed movements explained as much neural variance as task variables, with most of them aligned to task events. These results demonstrate that cognitive signals in the cortex are robust to natural movements, but also that unmeasured movements are potential confounds in cognitive neurophysiology experiments.

neuroscience↗