Basic and Acidic Proteins of the Brain Nuclei and their Possible Role in the Genetic Information Transfer

1972 ◽  
Vol 4 (4) ◽  
pp. 175-185 ◽  
Author(s):  
R. S. Piha ◽  
H. A. Jokela
2006 ◽  
Vol 291 (1) ◽  
pp. R155-R162 ◽  
Author(s):  
Stephanie A. Dean ◽  
Junhui Tan ◽  
Roselyn White ◽  
Edward R. O’Brien ◽  
Frans H. H. Leenen

The present study tested the hypothesis that 17β-estradiol (E2) inhibits increases in angiotensin-converting enzyme (ACE) and ANG II type 1 receptor (AT1R) in the brain and heart after myocardial infarction (MI) and, thereby, inhibits development of left ventricular (LV) dysfunction after MI. Age-matched female Wistar rats were treated as follows: 1) no surgery (ovary intact), 2) ovariectomy + subcutaneous vehicle treatment (OVX + Veh), or 3) OVX + subcutaneous administration of a high dose of E2 (OVX + high-E2). After 2 wk, rats were randomly assigned to coronary artery ligation (MI) and sham operation groups and studied after 3 wk. E2 status did not affect LV function in sham rats. At 2–3 wk after MI, impairment of LV function was similar across MI groups, as measured by echocardiography and direct LV catheterization. LV ACE mRNA abundance and activity were increased severalfold in all MI groups compared with respective sham animals and to similar levels across MI groups. In most brain nuclei, ACE and AT1R densities increased after MI. Unexpectedly, compared with the respective sham groups the relative increase was clearest (20–40%) in OVX + high-E2 MI rats, somewhat less (10–15%) in ovary-intact MI rats, and least (<10–15%) in OVX + Veh MI rats. However, because in the sham group brain ACE and AT1R densities increased in the OVX + Veh rats and decreased in the OVX + high-E2 rats compared with the ovary-intact rats, actual ACE and AT1R densities in most brain nuclei were modestly higher (<20%) in OVX + Veh MI rats than in the other two MI groups. Thus E2 does not inhibit upregulation of ACE in the LV after MI and amplifies the percent increases in ACE and AT1R densities in brain nuclei after MI, despite E2-induced downregulation in sham rats. Consistent with these minor variations in the tissue renin-angiotensin system, during the initial post-MI phase, E2 appears not to enhance or hinder the development of LV dysfunction.


2018 ◽  
Vol 115 (50) ◽  
pp. E11817-E11826 ◽  
Author(s):  
Nina Milosavljevic ◽  
Riccardo Storchi ◽  
Cyril G. Eleftheriou ◽  
Andrea Colins ◽  
Rasmus S. Petersen ◽  
...  

Information transfer in the brain relies upon energetically expensive spiking activity of neurons. Rates of information flow should therefore be carefully optimized, but mechanisms to control this parameter are poorly understood. We address this deficit in the visual system, where ambient light (irradiance) is predictive of the amount of information reaching the eye and ask whether a neural measure of irradiance can therefore be used to proactively control information flow along the optic nerve. We first show that firing rates for the retina’s output neurons [retinal ganglion cells (RGCs)] scale with irradiance and are positively correlated with rates of information and the gain of visual responses. Irradiance modulates firing in the absence of any other visual signal confirming that this is a genuine response to changing ambient light. Irradiance-driven changes in firing are observed across the population of RGCs (including in both ON and OFF units) but are disrupted in mice lacking melanopsin [the photopigment of irradiance-coding intrinsically photosensitive RGCs (ipRGCs)] and can be induced under steady light exposure by chemogenetic activation of ipRGCs. Artificially elevating firing by chemogenetic excitation of ipRGCs is sufficient to increase information flow by increasing the gain of visual responses, indicating that enhanced firing is a cause of increased information transfer at higher irradiance. Our results establish a retinal circuitry driving changes in RGC firing as an active response to alterations in ambient light to adjust the amount of visual information transmitted to the brain.


2009 ◽  
Vol 21 (6) ◽  
pp. 1714-1748 ◽  
Author(s):  
Shiro Ikeda ◽  
Jonathan H. Manton

Information transfer through a single neuron is a fundamental component of information processing in the brain, and computing the information channel capacity is important to understand this information processing. The problem is difficult since the capacity depends on coding, characteristics of the communication channel, and optimization over input distributions, among other issues. In this letter, we consider two models. The temporal coding model of a neuron as a communication channel assumes the output is τ where τ is a gamma-distributed random variable corresponding to the interspike interval, that is, the time it takes for the neuron to fire once. The rate coding model is similar; the output is the actual rate of firing over a fixed period of time. Theoretical studies prove that the distribution of inputs, which achieves channel capacity, is a discrete distribution with finite mass points for temporal and rate coding under a reasonable assumption. This allows us to compute numerically the capacity of a neuron. Numerical results are in a plausible range based on biological evidence to date.


Author(s):  
Jun Yang ◽  
Cao-you Song ◽  
Wen-yan Liu ◽  
Cai Song ◽  
Bao-cheng Lin

2017 ◽  
Vol 1 (3) ◽  
Author(s):  
Vito Di Maio ◽  
Francesco Ventriglia ◽  
Silvia Santillo

Synaptic transmission is the basic mechanism of information transfer between neurons not only in the brain, but along all the nervous system. In this review we will briefly summarize some of the main parameters that produce stochastic variability in the synaptic response. This variability produces different effects on important brain phenomena, like learning and memory, and, alterations of its basic factors can cause brain malfunctioning.


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