The Role of CREB, SRF, and MEF2 in Activity-Dependent Neuronal Plasticity in the Visual Cortex

This research aim to examine the influence of personal background, political background, and council budget knowledge towards the role of DPRD on region financial control. This research is motivated by the fact that individual background will effect to individual behavior on political activity. Dependent variables in this research are personal background, political background, and council budges knowledge towards the role of DPRD on region financial control Independent variables are the role of DPRD on region financial control in planning, implementing, and responsibility steps. The data in this research consist of primary data that taken from questionnaires distributed directly to respondents. The collected are from 34 Respondents that members of DPRD at Pekanbaru. Hypothesis of this research are examine by using Multivariate Analysis of Variances (MANOVA). The result of this research HI personal background political background and budget knowledge have significant influence toward the role of DPRD on region financial control in planning steps.H2 personal background, politico I background and budget knowledge have no significant influence toward the role of DPRD on region financial control in Implementing steps. H3 personal background political background and budget knowledge have no significant influence toward the role of DPRD on region financial control in Controlling steps.

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The role of activity in corpus callosum development in the visual cortex

9th International Conference on Artificial Neural Networks: ICANN '99 ◽

10.1049/cp:19991144 ◽

1999 ◽

Author(s):

T. Hely

Keyword(s):

Visual Cortex ◽

Corpus Callosum

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Adaptive Integration in the Visual Cortex by Depressing Recurrent Cortical Circuits

Neural Computation ◽

10.1162/neco.2008.06-07-546 ◽

2008 ◽

Vol 20 (7) ◽

pp. 1847-1872 ◽

Cited By ~ 24

Author(s):

Mark C. W. van Rossum ◽

Matthijs A. A. van der Meer ◽

Dengke Xiao ◽

Mike W. Oram

Keyword(s):

Visual Cortex ◽

Physiological Data ◽

High Contrast ◽

Cortical Circuits ◽

Response Latencies ◽

Low Contrast ◽

Visual Areas ◽

Higher Visual Areas ◽

Recurrent Connections

Neurons in the visual cortex receive a large amount of input from recurrent connections, yet the functional role of these connections remains unclear. Here we explore networks with strong recurrence in a computational model and show that short-term depression of the synapses in the recurrent loops implements an adaptive filter. This allows the visual system to respond reliably to deteriorated stimuli yet quickly to high-quality stimuli. For low-contrast stimuli, the model predicts long response latencies, whereas latencies are short for high-contrast stimuli. This is consistent with physiological data showing that in higher visual areas, latencies can increase more than 100 ms at low contrast compared to high contrast. Moreover, when presented with briefly flashed stimuli, the model predicts stereotypical responses that outlast the stimulus, again consistent with physiological findings. The adaptive properties of the model suggest that the abundant recurrent connections found in visual cortex serve to adapt the network's time constant in accordance with the stimulus and normalizes neuronal signals such that processing is as fast as possible while maintaining reliability.

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