As high levels of drug use account for thousands of drug related deaths every week, it’s important to investigate the neural mechanisms behind drug escalation as well as develop possible harm reduction strategies for drug taking. Therefore, the purpose of this experiment was to examine the effects of the Kappa Opioid receptor (KOR) on cocaine escalation in the mesolimbic system. Using a preclinical model to examine this hypothesis, CRISPR/SaCas9 was utilized in the ventral tegmental area (VTA) to selectively repress expression of KOR. After four weeks, the rats underwent cocaine self-administration during short access periods and then escalation was tested in long access periods. Lastly we utilized immunohistochemistry to confirm CRISPR/SaCas9 transduction in dopaminergic neurons. We found that decreased expression of the KOR decreased escalation during long access periods. Future research will examine the role of KOR from and in specific brain regions such that KOR expression will be decreased only in dopaminergic projections from the VTA into the NAc.

The goal of this project is to develop a dynamically evolving connectionist model that more closely resembles the brain through its information-processing. Over the years, AI has shifted from the first generation of feedforward systems to the use of recurrent or convolutional Neural Networks. The third and newest generation of AI models, the brain-based models, and the Spiking Neural Network (SNN), attempts to bridge the gap between Neuroscience and ML using biologically realistic models like Θ-model, LIF, Izhikevich, HR, HH. These models, however, are still a black box leaving very little control or understanding on the learning process within the system without the access to the inner structure of the network. In addition, these systems are highly inefficient, slow, and very complex due to the limitations imposed by the hardware and explicit simulation of partial differential equations. Real world problems require “flexible learning and dynamically adaptive connectionist systems” that are capable to adapt and accommodate new input in real time. Current solutions have focused on varying the weights within a system rather than focusing on how connections within the system are formed. Based on our understanding from organismal brain structures, our approach, called biomimetic information codec, .bic, is a morphologically-adaptive coding hierarchical network that form in accordance with energy minimization - driven by dissipation of "heat" generated by the training data - constructing cortices and connectome for processing of information. My first objective herein is to quantitatively compare detailed structures between biological (fly brain) and .bic. networks using a random matrix approach.

Complex phenomena in nature arise from surprisingly simple sets of rules, such as bee swarm motion, bird flight formations, ant movement and foraging, and traffic flow. Such complex macroscopic systems can all be accurately simulated using Markov models with simple governing rules and probabilities. Certain biomolecules on crystal substrates have been observed to display similar self-organizing capabilities, but understanding the mechanisms and rules that dictate their behavior has been elusive. Here we show that a set of simple computational rules in a Markov chain algorithm can model patterns in the spontaneous self-assembly of short peptides on two-dimensional surfaces with hexagonal lattices. The nature of this interface depends on the adopted molecular conformation of the peptide sequence upon binding. Additionally, the interface is coherent and can be tuned spatiotemporally by the peptide sequence and local environmental factors which affect folding and molecular interactions. We demonstrate, therefore, that self-organized peptides can act as molecular-scale programmable matter with controllable behavior. The ordered assembly of peptides causes instantaneous and discernible electronic responses in the substrate, paving the way for integrating biology with electronic devices. Possible real-world applications include biosensing, integrated prosthetics, brain-computer interfaces, or other biologically compatible electronic devices. The research was supported by the DMREF Program at National Science Foundation (NSF) through the MGI platform (Materials Genome Initiative) under grant numbers DMR# 1629071, 1848911, and 1922020.

The locus coeruleus (LC) is a small nucleus of noradrenergic neurons in the pons, which, despite its size, has broad projections throughout the central nervous system (CNS). Functionally, the LC is believed to be involved in various critical functions, including the physiological response to stress, as well as mediating arousal. Previous investigations have demonstrated that optogenetic activation of the LC at a tonic frequency promotes wakefulness in rodents. While this observation causally implicates LC function in wakefulness, it is still not known how the LC is endogenously controlled to mediate arousal. One potential candidate in this control is the peptide nociceptin and its cognate receptor, the nociceptin opioid peptide receptor (NOPR), both of which are highly expressed around the LC. To investigate, we first conducted two pharmacological experiments using the NOPR agonist Ro64-6918 to assess its effects on locomotion and on the activity of LC noradrenergic neurons. To determine where the endogenous nociceptin signal to the LC originates, we performed an intracranial injection of a Cre-dependent retrograde virus (AAV2-DIO-eYFP) into the LC of a mouse expressing Cre recombinase in nociceptin-expressing neurons. We observed that Ro64-6198 (10 mg/kg) strongly reduced open field locomotor activity compared to vehicle treatment. Using in vivo 2-photon calcium imaging (GCaMP6s), we found that Ro64-6198 (5 mg/kg) profoundly reduced LC noradrenergic neuron activity. Wakefulness appeared reduced in both in vivo experiments. Finally, we identified nociceptin-expressing cells projecting to the LC in the peri-LC as well as a long-range projection from the bed nucleus of the stria terminalis (BNST). Together, these studies suggest that nociceptin acting on LC noradrenergic neurons reduces arousal, and that the endogenous sources of nociceptin may originate in the peri-LC and BNST. Future studies will investigate nociceptin-expressing neuron activity during sleep/wake transitions and whether this activity is sufficient to alter wakefulness.

The primate brain effortlessly segments objects from background stimuli, however, the neuronal underpinnings of this process are largely unknown. Past studies in V1, the primary visual cortex, have demonstrated that neurons encode visual information within the receptive field through edge detection, by which the greatest local contrast evokes the largest response. These studies have typically focused on luminance contrast, but edge detection and object segmentation can rely on a variety of other factors including contrasts in color, texture and blur. The primary goal of my research project is to investigate how variances in contrasts of luminance, color saturation, orientation and blur within a textural image affect neuronal response in primary visual cortex V1 and in subsequent stages V2 and V4. I have created a novel stimulus set that individually incorporates these varying types of contrast. I will record the responses of neurons in the visual cortex of a macaque monkey to these stimuli using Neuropixels, which will allow us to determine whether neurons are selective to object form and whether the strength and latency of form selectivity depends on the stimulus dimensions that define object boundary. We expect that different subgroups of neurons in V1 and V2 may be sensitive to different types of stimulus contrasts, while the same neuron in V4 may be sensitive to object shape regardless of the stimulus modality but the different modalities may be associated with different latencies. Data analysis and discussion of results will be completed and prepared for presentation by May. Our research will build upon and contribute to the greater understanding of visual information processing within the primary visual cortex.