A pre-initiation complex at the 3′-end of genes drives antisense transcription independent of divergent sense transcription

The precise nature of antisense transcripts in eukaryotes such as Saccharomyces cerevisiae remains elusive. Here we show that the 3′ regions of genes possess a promoter architecture, including a pre-initiation complex (PIC), which mirrors that at the 5′ region and which is much more pronounced at genes with a defined antisense transcript. Remarkably, for genes with an antisense transcript, average levels of PIC components at the 3′ region are ∼60% of those at the 5′ region. Moreover, at these genes, average levels of nascent antisense transcription are ∼45% of sense transcription. We find that this 3′ promoter architecture persists for highly transcribed antisense transcripts where there are only low levels of transcription in the divergent sense direction, suggesting that the 3′ regions of genes can drive antisense transcription independent of divergent sense transcription. To validate this, we insert short 3′ regions into the middle of other genes and find that they are capable of both initiating antisense transcripts and terminating sense transcripts. Our results suggest that antisense transcription can be regulated independently of divergent sense transcription in a PIC-dependent manner and we propose that regulated production of antisense transcripts represents a fundamental and widespread component of gene regulation

A Brief History of Connectionism

Connectionist research is firmly established within the scientific community, especially within the multi-disciplinary field of cognitive science. This diversity, however, has created an environment which makes it difficult for connectionist researchers to remain aware of recent advances in the field, let alone understand how the field has developed. This paper attempts to address this problem by providing a brief guide to connectionist research. The paper begins by defining the basic tenets of connectionism. Next, the development of connectionist research is traced, commencing with connectionism's philosophical predecessors, moving to early psychological and neuropsychological influences, followed by the mathematical and computing contributions to connectionist research. Current research is then reviewed, focusing specifically on the different types of network architectures and learning rules in use. The paper concludes by suggesting that neural network research---at least in cognitiv..

Using Contrastive Hebbian Learning to Model Early Auditory Processing

We present a model of early auditory processing using the Symmetric Diffusion Network (SDN) architecture, a class of multi-layer, parallel distributed processing model based on the principles of continuous, stochastic, adaptive, and interactive processing [Movellan & McClelland, 1993]. From a computational perspective, a SDN can be viewed as a continuous version of the Boltzmann machine; that is, time is intrinsic to the dynamics of the network. Furthermore, SDNs embody Bayesian principles in that they develop internal representations based on the statistics of the environment. One of the main advantages of SDNs is that they are able to learn probabilistic mappings (i.e., mapping from m→n, where m<<n) for a single input pattern, a task impossible for many other classes of neural networks. SDNs are trained using the Contrastive Hebbian Learning (CHL) algorithm which is based on positive and negative learning phases. The basic model has been trained on two separate tasks: (i) a signal detection task, and (ii) a phonetic/nonphonetic discrimination task. In the signal detection task, the model was able to capture the accuracy data of human participants, but only grossly approximated participants ’ reaction time data. Reanalysis of the human data, however, showed that the network correctly predicted the reaction times in the early phases of the experiment. In the phonetic/nonphonetic discrimination task, the network was able to show both categorical and continuous perception of the stimuli. Importantly, the model predicted learning curves for categorical perception of nonphonetic stimuli that was subsequently confirmed in a human learning study. It is concluded that this simple type of network based on correlational learning is able to effectively model early auditory processing. 1