The Impact of Prenatal Nicotine Exposure on Impulsivity and Neural Firing in the Medial Prefrontal Cortex

Prenatal nicotine exposure (PNE) is linked to a large number of psychiatric disorders, including attention deficit hyperactivity disorder (ADHD). Current literature suggests that core deficits observed in ADHD reflect abnormal inhibitory control governed by the prefrontal cortex (PFC) of the brain. The PFC is structurally altered by PNE, but it is still unclear how neural firing is affected during tasks that test behavioral inhibition, such as the stop-signal task, or if neural correlates related to inhibitory control are affected after PNE in awake behaving animals. To address these questions, we recorded from single medial PFC (mPFC) neurons in control rats and PNE rats as they performed our stopsignal task. We found that PNE rats were faster for all trial types and were less likely to inhibit the behavioral response on STOP trials. Neurons in mPFC fired more strongly on STOP trials and were correlated with accuracy and reaction time. Although the number of neurons exhibiting significant modulation during task performance did not differ between groups, overall activity in PNE was reduced. We conclude that PNE makes rats impulsive and reduces firing in mPFC neurons that carry signals related to response inhibition.

Developmental Neural Correlates of Social Interaction

Children develop in a sea of reciprocal social interaction, but their brain development is predominately studied in non-interactive contexts (e.g., viewing photographs of faces). This dissertation investigated how the developing brain supports social interaction. Specifically, novel paradigms were used to target two facets of social experience—social communication and social motivation—across three studies in children and adults. In Study 1, adults listened to short vignettes—which contained no social information—that they believed to be either prerecorded or presented over an audio-feed by a live social partner. Simply believing that speech was from a live social partner increased activation in the brain’s mentalizing network—a network involved in thinking about others’ thoughts. Study 2 extended this paradigm to middle childhood, a time of increasing social competence and social network complexity, as well as structural and functional social brain development. Results showed that, as in adults, regions of the mentalizing network were engaged by live speech. Taken together, these findings indicate that the mentalizing network may support the processing of interactive communicative cues across development. Given this established importance of social-interactive context, Study 3 examined children’s social motivation when they believed they were engaged in a computer-based chat with a peer. Children initiated interaction via sharing information about their likes and hobbies and received responses from the peer. Compared to a non-social control, in which children chatted with a computer, peer interaction increased activation in mentalizing regions and reward circuitry. Further, within mentalizing regions, responsivity to the peer increased with age. Thus, across all three studies, social cognitive regions associated with mentalizing supported real-time social interaction. In contrast, the specific social context appeared to influence both reward circuitry involvement and age-related changes in neural activity. Future studies should continue to examine how the brain supports interaction across varied real-world social contexts. In addition to illuminating typical development, understanding the neural bases of interaction will offer insight into social disabilities such as autism, where social difficulties are often most acute in interactive situations. Ultimately, to best capture human experience, social neuroscience ought to be embedded in the social world.

Neural Modulation of Leg Stiffness in Response to Neuromuscular Fatigue

The spring-mass model is able to accurately represent hopping spring-like behavior (leg and joint stiffness), and leg and joint stiffness changes can reveal overall motor control responses to neural and muscular contributors of neuromuscular fatigue. By understanding leg stiffness modulation, we can determine which variables the nervous system targets to maintain motor performance and stability. The purpose of this study was to determine how neuromuscular fatigue affects hopping behavior by examining leg and joint stiffness before and after a single-leg calf raise fatiguing protocol. Post-fatigue, leg stiffness decreased for the exercised leg, but not for the non-exercised leg. Ankle and knee joint stiffness did not significantly change for either leg. This indicates that leg stiffness decreases primarily from muscular fatigue, but was not explained by ankle and knee joint stiffness. The decrease in leg stiffness may be an attempt to soften landing impact, while at the same time maintaining performance.

Self-Organizing Map Neural Architectures Based on Limit Cycle Attractors

Recent efforts to develop large-scale neural architectures have paid relatively little attention to the use of self-organizing maps (SOMs). Part of the reason is that most conventional SOMs use a static encoding representation: Each input is typically represented by the fixed activation of a single node in the map layer. This not only carries information in an inefficient and unreliable way that impedes building robust multi-SOM neural architectures, but it is also inconsistent with rhythmic oscillations in biological neural networks. Here I develop and study an alternative encoding scheme that instead uses limit cycle attractors of multi-focal activity patterns to represent input patterns/sequences. Such a fundamental change in representation raises several questions: Can this be done effectively and reliably? If so, will map formation still occur? What properties would limit cycle SOMs exhibit? Could multiple such SOMs interact effectively? Could robust architectures based on such SOMs be built for practical applications? The principal results of examining these questions are as follows. First, conditions are established for limit cycle attractors to emerge in a SOM through self-organization when encoding both static and temporal sequence inputs. It is found that under appropriate conditions a set of learned limit cycles are stable, unique, and preserve input relationships. In spite of the continually changing activity in a limit cycle SOM, map formation continues to occur reliably. Next, associations between limit cycles in different SOMs are learned. It is shown that limit cycles in one SOM can be successfully retrieved by another SOM’s limit cycle activity. Control timings can be set quite arbitrarily during both training and activation. Importantly, the learned associations generalize to new inputs that have never been seen during training. Finally, a complete neural architecture based on multiple limit cycle SOMs is presented for robotic arm control. This architecture combines open-loop and closed-loop methods to achieve high accuracy and fast movements through smooth trajectories. The architecture is robust in that disrupting or damaging the system in a variety of ways does not completely destroy the system. I conclude that limit cycle SOMs have great potentials for use in constructing robust neural architectures.