Audience Effects in COgnitive Task Performance in Capuchin Monkeys (Cebus apella)

Primates as a taxonomic Order have the largest brains corrected for body size in the animal kingdom. These large brains have allowed primates to evolve the capacity to demonstrate advanced cognitive processes across a wide array of abilities. Nonhuman primates are particularly adept at social learning, defined as the modification of behavior by observing the actions of others. Additionally, primates often exploit resources differently depending on their social context. In this study, capuchin monkeys (Cebus apella) were tested on a cognitive task in three social contexts to determine if social context influenced their performance on the task. The three social contexts included: alone, having a dominant individual in an adjacent compartment, and having a subordinate individual in the adjacent compartment. The benefits to this design were thatthe social context was the only variable influencing performance, whereas in previous studies investigating audience effects other animals could physically and directly influence a subject's performance in an open testing situation. Based on past studies, Ipredicted that the presence of a dominant individual would reduce cognitive task performance compared to the other conditions. The cognitive test used was a match-tosample discrimination task in which animals matched combinations of eight geometric shapes. Animals were trained on this task in an isolated context until they reached a baseline level of proficiency and were then tested in the three social contexts in a random order multiple times. Two subjects (Mt and Dv) have successfully completed trials under all conditions. Results indicated that there were no significant difference in taskperformance across the three conditions (Dv x^2 (1) = 0.42, p=0.58; Mt x^2 (1) = 0.02, p=0.88). In all conditions, subjects performed significantly above chance (i.e., 39/60 trials determined by a binomial distribution). Results are contrary to previous studies thatreport low status monkeys 'play dumb' when testing in a mixed social context, possibly because other studies did not account for aggressive interference by dominants while testing. Results of this study suggest that the mere presence of a dominant individualdoes not necessarily affect performance on a cognitive task, but rather the imminence of physical aggression is the most important factor influencing testing in a social context.

THE RESONATE-AND-FIRE NEURON: TIME DEPENDENT AND FREQUENCY SELECTIVE NEURONS IN NEURAL NETWORKS

The means through which the nervous system perceives its environment is one of the most fascinating questions in contemporary science. Our endeavors to comprehend the principles of neural science provide an instance of how biological processes may inspire novel methods in mathematical modeling and engineering. The application ofmathematical models towards understanding neural signals and systems represents a vibrant field of research that has spanned over half a century. During this period, multiple approaches to neuronal modeling have been adopted, and each approach is adept at elucidating a specific aspect of nervous system function. Thus while bio-physical models have strived to comprehend the dynamics of actual physical processes occurring within a nerve cell, the phenomenological approach has conceived models that relate the ionic properties of nerve cells to transitions in neural activity. Further-more, the field of neural networks has endeavored to explore how distributed parallel processing systems may become capable of storing memory. Through this project, we strive to explore how some of the insights gained from biophysical neuronal modeling may be incorporated within the field of neural net-works. We specifically study the capabilities of a simple neural model, the Resonate-and-Fire (RAF) neuron, whose derivation is inspired by biophysical neural modeling. While reflecting further biological plausibility, the RAF neuron is also analytically tractable, and thus may be implemented within neural networks. In the following thesis, we provide a brief overview of the different approaches that have been adopted towards comprehending the properties of nerve cells, along with the framework under which our specific neuron model relates to the field of neuronal modeling. Subsequently, we explore some of the time-dependent neurocomputational capabilities of the RAF neuron, and we utilize the model to classify logic gates, and solve the classic XOR problem. Finally we explore how the resonate-and-fire neuron may be implemented within neural networks, and how such a network could be adapted through the temporal backpropagation algorithm.