Theories of access consciousness address how it is that some mental states but not others are available for evaluation, choice behavior, and verbal report. Farah, O'Reilly, and Vecera (1994) argue that quality of representation is critical; Dehaene, Sergent,and Changeux (2003) argue that the ability to communicate representations iscritical. We present a probabilistic information transmission or PIT model that suggests both of these conditions are essential for access consciousness. Havingsuccessfully modeled data from the repetition priming literature in the past, we use the PIT model to account for data from two experiments on subliminal priming, showing that the model produces priming even in the absence ofaccessibility and reportability of internal states. The model provides a mechanistic basis for understanding the dissociation of priming and awareness. Philosophy has made many attempts to identify distinct aspects of consciousness. Perhaps the most famous effort is Block's (1995) delineation of phenomenal and access consciousness. Phenomenalconsciousness has to do with "what it is like" to experience chocolate or a pin prick. Access consciousness refers to internal states whose content is "(1) inferentially promiscuous,i.e., poised to be used as a premise in reasoning, (2) poised for control of action, and (3) poised for rational control of speech."
Using a synthetic biology approach to vary promoter gene combinations, we generated a total of 17 construct designs of the three pathways with and without the transporter RNAi construct. Initial screens for photoprotection by alternative pathway function under high–photorespiratory stress conditions identified three to five independent transformants of each design for further analysis. Gene and protein expression analyses confirmed expression of the introduced genes and suppression of the native transporter in RNAi plants. In greenhouse screens, pathway 1 increased biomass by nearly 13%. Pathway 2 showed no benefit compared to wild type.
As genomic and proteomic data is collected from highthroughput methods on a daily basis, subcellular components are identified and their in vitro behavior is characterized. However, much less is known of their in vivo activity because of the complex subcellular milieu they operate within. A component's milieu is determined by the biological pathways it participates in, and hence, the mechanisms by which it is regulated. We believe AI planning technology provides a modeling formalism for the task of biological pathway discovery, such that hypothetical pathways can be generated, queried and qualitatively simulated. The task of signal transduction pathway discovery is recast as a planning problem, one in which the initial and final states are known and cellular processes captured as abstract operators that modify the cellular environment. Thus, a valid plan that transforms the initial state into a goal state is a hypothetical pathway that prescribes the order of signaling events that must occur to effect the goal state. The planner is driven by data that is stored within a knowledge base and retrieved from heterogeneous sources (including gene expression, protein-protein interaction and literature mining) by a multi-agent information gathering system. We demonstrate the combined technology by translating the well-known EGF pathway into the planning formalism and deploying the Fast-Forward planner to reconstruct the pathway directly from the knowledge base.
Biological carbon fixation requires several enzymes to turn CO2 into biomass. Although this pathway evolved in plants, algae, and microorganisms over billions of years, many reactions and enzymes could aid in the production of desired chemical products instead of biomass. Schwander et al. constructed an optimized synthetic carbon fixation pathway in vitro by using 17 enzymes--including three engineered enzymes--from nine different organisms across all three domains of life (see the Perspective by Gong and Li). The pathway is up to five times more efficient than the in vivo rates of the most common natural carbon fixation pathway. Further optimization of this and other metabolic pathways by using similar approaches may lead to a host of biotechnological applications.
Motivated by an important insight from neural science, we propose a new framework for understanding the success of the recently proposed "maxout" networks. The framework is based on encoding information on sparse pathways and recognizing the correct pathway at inference time. Elaborating further on this insight, we propose a novel deep network architecture, called "channel-out" network, which takes a much better advantage of sparse pathway encoding. In channel-out networks, pathways are not only formed a posteriori, but they are also actively selected according to the inference outputs from the lower layers. From a mathematical perspective, channel-out networks can represent a wider class of piece-wise continuous functions, thereby endowing the network with more expressive power than that of maxout networks. We test our channel-out networks on several well-known image classification benchmarks, setting new state-of-the-art performance on CIFAR-100 and STL-10, which represent some of the "harder" image classification benchmarks.