Surviving and thriving requires minimizing energy expenditure while navigating (sometimes harrowing) environments in search of nourishment, mates, and shelter from predators and the elements. Associative learning is one of the tools available to organisms as they work to solve this problem, allowing them to efficiently use scents, sounds, and visuospatial features to predict the likelihood and location of rewards. We combine in vivo electrophysiology, complex behavioral paradigms, and statistical modeling techniques to explore the activity of the CA1 subregion of the hippocampus, which integrates a range of converging inputs that are thought to provide the sensorimotor, visuospatial, and affective information required to associate properties of the environment with inherently valuable stimuli. To organize these streams of information, the CA1 subregion must attenuate activity irrelevant to the organisms’ immediate goals, while simultaneously allowing select neuronal populations to respond to the appropriate inputs. This thesis consequently characterizes the activity patterns of inhibitory interneurons during associative memory processing. Interneurons work cooperatively with constellations of inputs to coordinate select subpopulations of pyramidal cells. In Chapter 2, I show that inhibitory interneurons selectively modulate their firing rate responses to odorants and spatial locations during associative memory processing, suggesting that interneurons can in fact be recruited by distinct, behaviorally relevant inputs. Chapter 3 documents the development of a statistical modeling strategy for identifying rapid shifts in interneuron spike timing relative to oscillatory synaptic currents. This tool facilitated the discovery of interneurons whose spike timing reliably evolved over the course of associative memory processing. Lastly, Chapter 4 investigates CA1 interneuron spike timing in response to distinct combinations of olfactory and visuospatial inputs. We apply the modeling approach described in Chapter 3 to characterize spike-phase relationships with respect to theta (4-12 Hz), low gamma (35-55 Hz), and high gamma (65-90 Hz) local field potential (LFP) oscillations. We show that entrainment into higher frequencies is predicted by theta phase relationships, and we find that interneuron spike-phase relationships can vary according to distinct combinations of inputs. These findings coalesce to offer insights on the role of CA1 inhibition in optimally integrating visuospatial, sensorimotor, and affective information to support associative memory.

Background

New method

Results

Comparison with existing methods

Conclusions

Prosocial behavior is a complex phenomenon that is poorly understood neurobiologically. While it is known that social interactions engage the amygdala, the insular cortex, and the autonomic nervous system, the manner in which these systems interact to cooperatively support prosocial behavior is not known. To investigate the neurobiological underpinnings of prosociality, it is critical to develop a tightly controlled paradigm that is capable of isolating the elements that give rise to prosocial actions. Chapter One describes the design of an effective, low-cost paradigm for investigating prosociality in rats in which one rat has the ability to stop an aggravating air stimulus from affecting another rat. Researchers are provided with a graphical user interface to control, view, and record behavioral epochs in real time. This paradigm has the potential to become a widely-used standard in the field of social neuroscience.

The temporal coordination of neural activity in rhythms has been hypothesized to be critical for communication between interacting brain regions. Chapter Two proposes a novel pipeline for investigating the manner in which the rhythmic activity in one brain region constrains its ability to influence the activity of another structure. The proposed method uses a generalized linear model with Von Mises distributions as phase regressors to assess the extent to which the phase of neural oscillations in one brain region predicts the neural spiking activity in a downstream region. The pipeline described in this chapter is a novel approach to characterizing the relationship between continuous neural oscillations and discrete spiking activity.