Responses to Central Oxotremorine and Scopolamine Support the Cholinergic Control of Male Mating Behavior in Hamsters

The responses of hamsters to intracranial injections of the cholinergic agonist oxotremorine (OXO) implicate cholinergic mechanisms in the medial preoptic area (MPOA) in the control of male mating behavior. To extend these observations, we ran three studies of responses to cholinergic drugs delivered singly or in combination to the vicinity of the MPOA. The first tested responses to OXO, confirming its ability to reduce the postejaculatory interval. The second complemented the first by examining responses to MPOA microinjections of the cholinergic antagonist scopolamine (SCO). These caused several changes revolving around intromission. These included increases in intromission frequency and ejaculation latency. They also included a change in the patterning of intromissions, marked by continuous strings without the usual separation by dismounts. The final study resembled the others in examining the effects of MPOA injections of OXO and SCO but focused on the ability of each drug to antagonize responses to the other. Most of the responses to OXO and SCO individually replicated earlier findings, though the measures examined here also permitted the description of effects on some noncopulatory sexual behaviors, specifically the male's inspection of the female. However, the most interesting results may be those suggesting asymmetry in the responses to the addition of the second drug: Whereas responses to OXO tended to be antagonized by SCO, OXO was less effective at counteracting responses to SCO. Though the explanation of this asymmetry is not completely clear, it is consistent with previous suggestions of differences in the affinities of these drugs for subtypes of muscarinic receptors. Therefore, it suggests that the cholinergic synapses and circuits controlling distinct elements of male behavior could differ in their dependence on these receptors. Copyright 2013 Elsevier Inc. All rights reserved.

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.