In social insect colonies, individuals are physically independent but functionally integrated by interaction networks which provide a foundation for communication and drive the emergence of collective behaviors, including nest architecture, division of labor, and potentially also the social regulation of metabolic rates. To investigate the relationship between interactions, metabolism, and colony size, we varied group size for harvester ant colonies (Pogonomyrmex californicus) and assessed their communication networks based on direct antennal contacts and compared these results with proximity networks and a random movement simulation. We found support for the hypothesis of social regulation; individuals did not interact with each other randomly but exhibited restraint. Connectivity scaled hypometrically with colony size, per-capita interaction rate was scale-invariant, and smaller colonies exhibited higher measures of closeness centrality and edge density, correlating with higher per-capita metabolic rates. Although the immediate energetic cost for two ants to interact is insignificant, the downstream effects of receiving and integrating social information can have metabolic consequences. Our results indicate that individuals in larger colonies are relatively more insulated from each other, a factor that may reduce or filter noisy stimuli and contribute to the hypometric scaling of their metabolic rates, and perhaps more generally, the evolution of larger colony sizes.
Neuronal responses during behavior are diverse, ranging from highly reliable 'classical' responses to irregular or seemingly-random 'non-classically responsive' firing. While a continuum of response properties is frequently observed across neural systems, little is known about the synaptic origins and contributions of diverse response profiles to network function, perception, and behavior. Here we use a task-performing, spiking recurrent neural network model incorporating spike-timingdependent plasticity that captures heterogeneous responses measured from auditory cortex of behaving rodents. Classically responsive and non-classically responsive model units contributed to task performance via output and recurrent connections, respectively. Excitatory and inhibitory plasticity independently shaped spiking responses and task performance. Local patterns of synaptic inputs predicted spiking response properties of network units as well as the responses of auditory cortical neurons from in vivo whole-cell recordings during behavior. Thus a diversity of neural response profiles emerges from synaptic plasticity rules with distinctly important functions for network performance.
Neuronal responses during behavior are diverse, ranging from highly reliable ‘classical’ responses to irregular or seemingly-random ‘non-classically responsive’ firing. While a continuum of response properties is frequently observed across neural systems, little is known about the synaptic origins and contributions of diverse response profiles to network function, perception, and behavior. Here we use a task-performing, spiking recurrent neural network model incorporating spike-timing-dependent plasticity that captures heterogeneous responses measured from auditory cortex of behaving rodents. Classically responsive and non-classically responsive model units contributed to task performance via output and recurrent connections, respectively. Excitatory and inhibitory plasticity independently shaped spiking responses and task performance. Local patterns of synaptic inputs predicted spiking response properties of network units as well as the responses of auditory cortical neurons from in vivo whole-cell recordings during behavior. Thus a diversity of neural response profiles emerges from synaptic plasticity rules with distinctly important functions for network performance.
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