Dataset Information

Convergence, Divergence, and Reconvergence in a Feedforward Network Improves Neural Speed and Accuracy.

ABSTRACT: One of the proposed canonical circuit motifs employed by the brain is a feedforward network where parallel signals converge, diverge, and reconverge. Here we investigate a network with this architecture in the Drosophila olfactory system. We focus on a glomerulus whose receptor neurons converge in an all-to-all manner onto six projection neurons that then reconverge onto higher-order neurons. We find that both convergence and reconvergence improve the ability of a decoder to detect a stimulus based on a single neuron's spike train. The first transformation implements averaging, and it improves peak detection accuracy but not speed; the second transformation implements coincidence detection, and it improves speed but not peak accuracy. In each case, the integration time and threshold of the postsynaptic cell are matched to the statistics of convergent spike trains.

SUBMITTER: Jeanne JM

PROVIDER: S-EPMC5488793 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Convergence, Divergence, and Reconvergence in a Feedforward Network Improves Neural Speed and Accuracy.

Jeanne James M JM Wilson Rachel I RI

Neuron 20151112 5

One of the proposed canonical circuit motifs employed by the brain is a feedforward network where parallel signals converge, diverge, and reconverge. Here we investigate a network with this architecture in the Drosophila olfactory system. We focus on a glomerulus whose receptor neurons converge in an all-to-all manner onto six projection neurons that then reconverge onto higher-order neurons. We find that both convergence and reconvergence improve the ability of a decoder to detect a stimulus ba ...[more]

PMID: 26586183

Similar Datasets

Project description:INTRODUCTION:Alternative RNA splicing is a critical regulatory mechanism during tumorigenesis. However, previous oncological studies mainly focused on the splicing of individual genes. Whether and how transcript isoforms are coordinated to affect cellular functions remain underexplored. Also of great interest is how the splicing regulome cooperates with the transcription regulome to facilitate tumorigenesis. The answers to these questions are of fundamental importance to cancer biology. RESULTS:Here, we report a comparative study between the transcript-based network (TN) and the gene-based network (GN) derived from the transcriptomes of paired tumor-normal tissues from 77 lung adenocarcinoma patients. We demonstrate that the two networks differ significantly from each other in terms of patient clustering and the number and functions of network modules. Interestingly, the majority (89.5%) of multi-transcript genes have their transcript isoforms distributed in at least two TN modules, suggesting regulatory and functional divergences between transcript isoforms. Furthermore, TN and GN modules share onl?50%-60% of their biological functions. TN thus appears to constitute a regulatory layer separate from GN. Nevertheless, our results indicate that functional convergence and divergence both occur between TN and GN, implying complex interactions between the two regulatory layers. Finally, we report that the expression profiles of module members in both TN and GN shift dramatically yet concordantly during tumorigenesis. The mechanisms underlying this coordinated shifting remain unclear yet are worth further explorations. CONCLUSION:We show that in lung adenocarcinoma, transcript isoforms per se are coordinately regulated to conduct biological functions not conveyed by the network of genes. However, the two networks may interact closely with each other by sharing the same or related biological functions. Unraveling the effects and mechanisms of such interactions will significantly advance our understanding of this deadly disease.

Project description:The ability to dynamically and rapidly adjust task performance based on its outcome is fundamental to adaptive, flexible behavior. Over trials of a task, responses speed up until an error is committed and after the error responses slow down. These dynamic adjustments serve to optimize performance and are well-described by the speed-accuracy trade-off (SATO) function. We hypothesized that SATOs based on outcomes reflect reciprocal changes in the allocation of attention between the internal milieu and the task-at-hand, as indexed by reciprocal changes in activity between the default and dorsal attention brain networks. We tested this hypothesis using functional MRI to examine the pattern of network activation over a series of trials surrounding and including an error. We further hypothesized that these reciprocal changes in network activity are coordinated by the posterior cingulate cortex (PCC) and would rely on the structural integrity of its white matter connections. Using diffusion tensor imaging, we examined whether fractional anisotropy of the posterior cingulum bundle correlated with the magnitude of reciprocal changes in network activation around errors. As expected, reaction time (RT) in trials surrounding errors was consistent with predictions from the SATO function. Activation in the default network was: (i) inversely correlated with RT, (ii) greater on trials before than after an error and (iii) maximal at the error. In contrast, activation in the right intraparietal sulcus of the dorsal attention network was (i) positively correlated with RT and showed the opposite pattern: (ii) less activation before than after an error and (iii) the least activation on the error. Greater integrity of the posterior cingulum bundle was associated with greater reciprocity in network activation around errors. These findings suggest that dynamic changes in attention to the internal versus external milieu in response to errors underlie SATOs in RT and are mediated by the PCC.

Dataset Information

Convergence, Divergence, and Reconvergence in a Feedforward Network Improves Neural Speed and Accuracy.

Publications

Convergence, Divergence, and Reconvergence in a Feedforward Network Improves Neural Speed and Accuracy.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure