Neuromodulated attention and goal-driven perception in uncertain domains.

In uncertain domains, the goals are often unknown and need to be predicted by the organism or system. In this paper, contrastive Excitation Backprop (c-EB) was used in two goal-driven perception tasks - one with pairs of noisy MNIST digits and the other with a robot in an action-based attention scenario. The first task included attending to even, odd, low, and high digits, whereas the second task included action goals, such as "eat", "work-on-computer", "read", and "say-hi" that led to attention to objects associated with those actions. The system needed to increase attention to target items and decrease attention to distractor items and background noise. Because the valid goal was unknown, an online learning model based on the cholinergic and noradrenergic neuromodulatory systems was used to predict a noisy goal (expected uncertainty) and re-adapt when the goal changed (unexpected uncertainty). This neurobiologically plausible model demonstrates how neuromodulatory systems can predict goals in uncertain domains and how attentional mechanisms can enhance the perception for that goal

Neurorobotic Investigation of Biologically Plausible Neural Networks

My dissertation focuses on three research problems to investigate how the robot's behavior leads to a qualitative and quantitative explanation of neural activities, and vice versa, that is, how neural activities lead to behavior. In the first problem, we simulated a rat in a robot simulator to replicate the behavior and neural activity observed in rats during a spatial and working memory task. A recurrent neural network (RNN) with sensory and vision inputs was evolved to control the robot motor wheels and navigate a virtual T-maze. Our current findings suggest that neurons in the RNN are performing mixed selectivity and conjunctive coding. Moreover, the RNN activity resembles spatial information and trajectory-dependent coding observed in the hippocampus. In the second problem, we developed a goal-driven perception algorithm inspired by effects of the cholinergic (ACh) and noradrenergic (NE) neuromodulatory systems on attention and tracking uncertainties. We tested the network architecture, which extended the contrastive excitation backprop (c-EB), in a noisy MNIST-pair task and an action-based human support robot task. The network architecture could quickly learn the context without supervision, flexibly apply attention to the appropriate goal, and rapidly detect and re-adapt to context changes. In the third problem, we developed a reservoir-based spiking neural network (r-SNN) to classify three terrain types in a botanical garden. The input spike trains were generated from the linear accelerometer, gyroscope, and image data collected by a six-wheel Android-based robot (ABR). Our r-SNN terrain prediction can be used to evaluate the cost of traversal for path planning. It is a promising approach to develop a complete neuromorphic robot navigation system capable of operating over long durations with minimal power consumption. We suggest that neurorobotic investigation of biologically plausible neural networks can be a powerful methodology for understanding neuroscience, as well as for artificial intelligence and machine learning

Kína túlképzettségének hatása a munkaerőpiacra

This study focuses on the impact of the expansion and development of higher education on China's economy and labor market, and studies its impact from the perspective of over education. This study finds that the expansion of higher education in China increases the demand for labor, improves the overall quality of labor, and stimulates economic development. China's over education has affected the labor market, reducing the participation rate of youth in labor, and increasing the employment and unemployment rate of labor force with higher education level.By comparing the unemployment rate of workers with different levels of higher education, it is found that the reason for the rise of the unemployment rate is that the supply of workers with higher education level is greater than the demand, which results in internal competition.It is found that employment difficulties do exist, and the education level of the labor force will determine the advantages and disadvantages of the labor market. At the same time, it also explains why the youth labor participation rate has declined.BSc/BABA in Business Administration And ManagementK

Neurorobotic Investigation of Biologically Plausible Neural Networks

My dissertation focuses on three research problems to investigate how the robot's behavior leads to a qualitative and quantitative explanation of neural activities, and vice versa, that is, how neural activities lead to behavior. In the first problem, we simulated a rat in a robot simulator to replicate the behavior and neural activity observed in rats during a spatial and working memory task. A recurrent neural network (RNN) with sensory and vision inputs was evolved to control the robot motor wheels and navigate a virtual T-maze. Our current findings suggest that neurons in the RNN are performing mixed selectivity and conjunctive coding. Moreover, the RNN activity resembles spatial information and trajectory-dependent coding observed in the hippocampus. In the second problem, we developed a goal-driven perception algorithm inspired by effects of the cholinergic (ACh) and noradrenergic (NE) neuromodulatory systems on attention and tracking uncertainties. We tested the network architecture, which extended the contrastive excitation backprop (c-EB), in a noisy MNIST-pair task and an action-based human support robot task. The network architecture could quickly learn the context without supervision, flexibly apply attention to the appropriate goal, and rapidly detect and re-adapt to context changes. In the third problem, we developed a reservoir-based spiking neural network (r-SNN) to classify three terrain types in a botanical garden. The input spike trains were generated from the linear accelerometer, gyroscope, and image data collected by a six-wheel Android-based robot (ABR). Our r-SNN terrain prediction can be used to evaluate the cost of traversal for path planning. It is a promising approach to develop a complete neuromorphic robot navigation system capable of operating over long durations with minimal power consumption. We suggest that neurorobotic investigation of biologically plausible neural networks can be a powerful methodology for understanding neuroscience, as well as for artificial intelligence and machine learning

Enterococcus faecalis Encodes an Atypical Auxiliary Acyl Carrier Protein Required for Efficient Regulation of Fatty Acid Synthesis by Exogenous Fatty Acids

AcpB homologs are encoded by many, but not all, lactic acid bacteria (Lactobacillales), including many members of the human microbiome. The mechanisms regulating fatty acid synthesis by exogenous fatty acids play a key role in resistance of these bacteria to those antimicrobials targeted at fatty acid synthesis enzymes. Defective regulation can increase resistance to such inhibitors and also reduce pathogenesis.Acyl carrier proteins (ACPs) play essential roles in the synthesis of fatty acids and transfer of long fatty acyl chains into complex lipids. The Enterococcus faecalis genome contains two annotated acp genes, called acpA and acpB. AcpA is encoded within the fatty acid synthesis (fab) operon and appears essential. In contrast, AcpB is an atypical ACP, having only 30% residue identity with AcpA, and is not essential. Deletion of acpB has no effect on E. faecalis growth or de novo fatty acid synthesis in media lacking fatty acids. However, unlike the wild-type strain, where growth with oleic acid resulted in almost complete blockage of de novo fatty acid synthesis, the ΔacpB strain largely continued de novo fatty acid synthesis under these conditions. Blockage in the wild-type strain is due to repression of fab operon transcription, leading to levels of fatty acid synthetic proteins (including AcpA) that are insufficient to support de novo synthesis. Transcription of the fab operon is regulated by FabT, a repressor protein that binds DNA only when it is bound to an acyl-ACP ligand. Since AcpA is encoded in the fab operon, its synthesis is blocked when the operon is repressed and acpA thus cannot provide a stable supply of ACP for synthesis of the acyl-ACP ligand required for DNA binding by FabT. In contrast to AcpA, acpB transcription is unaffected by growth with exogenous fatty acids and thus provides a stable supply of ACP for conversion to the acyl-ACP ligand required for repression by FabT. Indeed, ΔacpB and ΔfabT strains have essentially the same de novo fatty acid synthesis phenotype in oleic acid-grown cultures, which argues that neither strain can form the FabT-acyl-ACP repression complex. Finally, acylated derivatives of both AcpB and AcpA were substrates for the E. faecalis enoyl-ACP reductases and for E. faecalis PlsX (acyl-ACP; phosphate acyltransferase)

Neuromodulated attention and goal-driven perception in uncertain domains.

In uncertain domains, the goals are often unknown and need to be predicted by the organism or system. In this paper, contrastive Excitation Backprop (c-EB) was used in two goal-driven perception tasks - one with pairs of noisy MNIST digits and the other with a robot in an action-based attention scenario. The first task included attending to even, odd, low, and high digits, whereas the second task included action goals, such as "eat", "work-on-computer", "read", and "say-hi" that led to attention to objects associated with those actions. The system needed to increase attention to target items and decrease attention to distractor items and background noise. Because the valid goal was unknown, an online learning model based on the cholinergic and noradrenergic neuromodulatory systems was used to predict a noisy goal (expected uncertainty) and re-adapt when the goal changed (unexpected uncertainty). This neurobiologically plausible model demonstrates how neuromodulatory systems can predict goals in uncertain domains and how attentional mechanisms can enhance the perception for that goal