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What are different firing rates in different regions in the human brain?

What are different firing rates in different regions in the human brain?


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Could it for example be that neurons that are concerned with high-order thought and medium-term planning fire more slowly, or is it expected that all neurons rather fire more or less at the same rate?

Here is an article on the average firing rate: http://aiimpacts.org/rate-of-neuron-firing/

Is the assumption that the measurements tend to be biased by accounting only for visually responsive cells warranted?


The article, has a classical engineering approach (might be wrong I just skimmed it), not that it's a bad thing. The general criticism of bias against sparsely coding neurons is correct, don't know how they can say its a factor of 10 or anything else though.

Average firing rate of one neuron only makes sense in relation to a different condition. For example in sensory neuroscience studies, we say a neuron likes a stimulus if the average firing rate of this neuron while the stimulus is presented is higher than in an equal period in the absence of the that stimulus. Conversely a neuron dislikes a stimulus when the average firing rate goes down.

Average firing rate across all neurons also is a weird measure as neurons have different baseline firing, different morphologies, are part of different circuits and have different functions in these circuits. It would be like saying that the average of a normal distribution is enough to describe it ignoring its variance.

Regarding the question itself a couple of things should be better defined: what is higher order thought? and medium time planning of what?

Neurons certainly do not all fire at the same rate, however rate implies a time constant and most importantly when one averages across multiple neurons one must have a criterion of selecting them which begs the question: Which neurons? How does one defines an area of the brain? Morphologically? Functionally? Genetically? Furthermore, what is its relevant time constant? These seem like basic obvious questions however they don't seem to have a clear and defined answer yet.

You seem to have a particular architecture in mind with multiple areas that have different functions, I assume you are modeling it. All models are wrong (yeah yeah I know its a cliche) which is to say that every model has a particular set of more or less valid assumptions. You just have to know what these assumptions are and what you can conclude given these assumptions. So basically there isn't really an answer to your question from the biology side we're still blind scientists in a dark room looking for the brain dead cat that is hiding the light switch.

Once I figure out the function of an arbitrarily defined brain area I'll let you know its time constant, then again it is arbitrarily defined and I cannot possibly figure out its function in all possible conditions without having assumptions of my own of how things should work…


Relating Neuronal Firing Patterns to Functional Differentiation of Cerebral Cortex

Affiliations Department of Integrative Brain Science, Graduate School of Medicine, Kyoto University, Sakyo-ku, Kyoto, Japan, Division of Sensory and Cognitive Information, National Institute for Physiological Sciences, Myodaiji, Okazaki, Aichi, Japan

Affiliation Division of Sensory and Cognitive Information, National Institute for Physiological Sciences, Myodaiji, Okazaki, Aichi, Japan

Affiliation Tamagawa University Brain Science Institute, Machida, Tokyo, Japan

Affiliation ATR Computational Neuroscience Laboratories, Seika-cho, Soraku-gun, Kyoto, Japan


The DNA regions in our brain that contribute to make us human

With only 1% difference, the human and chimpanzee protein-coding genomes are remarkably similar. Understanding the biological features that make us human is part of a fascinating and intensely debated line of research. Researchers at the SIB Swiss Institute of Bioinformatics and the University of Lausanne have developed a new approach to pinpoint, for the first time, adaptive human-specific changes in the way genes are regulated in the brain. These results open new perspectives in the study of human evolution, developmental biology and neurosciences. The paper is published in Science Advances.

Gene expression, not gene sequence

To explain what sets human apart from their ape relatives, researchers have long hypothesized that it is not so much the DNA sequence, but rather the regulation of the genes (i.e. when, where and how strongly the gene is expressed), that plays the key role. However, precisely pinpointing the regulatory elements which act as 'gene dimmers' and are positively selected is a challenging task that has thus far defeated researchers (see box).

Marc Robinson-Rechavi, Group Leader at SIB and study co-author says: "To be able to answer such tantalizing questions, one has to be able identify the parts in the genome that have been under so called 'positive' selection [see box]. The answer is of great interest in addressing evolutionary questions, but also, ultimately, could help biomedical research as it offers a mechanistic view of how genes function."

A high proportion of the regulatory elements in the human brain have been positively selected

Researchers at SIB and the University of Lausanne have developed a new method which has enabled them to identify a large set of gene regulatory regions in the brain, selected throughout human evolution. Jialin Liu, Postdoctoral researcher and lead author of the study explains: "We show for the first time that the human brain has experienced a particularly high level of positive selection, as compared to the stomach or heart for instance. This is exciting, because we now have a way to identify genomic regions that might have contributed to the evolution of our cognitive abilities!"

To reach their conclusions, the two researchers combined machine learning models with experimental data on how strongly proteins involved in gene regulation bind to their regulatory sequences in different tissues, and then performed evolutionary comparisons between human, chimpanzee and gorilla. "We now know which are the positively selected regions controlling gene expression in the human brain. And the more we learn about the genes they are controlling, the more complete our understanding of cognition and evolution, and the more scope there will be to act on that understanding," concludes Marc Robinson-Rechavi.

Positive selection: a hint of the functional relevance of a mutation

Most random genetic mutations neither benefit nor harm an organism: they accumulate at a steady rate that reflects the amount of time that has passed since two living species had a common ancestor. In contrast, an acceleration in that rate in a particular part of the genome can reflect a positive selection for a mutation that helps an organism to survive and reproduce, which makes the mutation more likely to be passed on to future generations. Gene regulatory elements are often only a few nucleotides long, which makes estimating their acceleration rate particularly difficult from a statistical point of view.


BRAIN WORK AND BRAIN IMAGING

AbstractFunctional brain imaging with positron emission tomography and magnetic resonance imaging has been used extensively to map regional changes in brain activity. The signal used by both techniques is based on changes in local circulation and metabolism (brain work). Our understanding of the cell biology of these changes has progressed greatly in the past decade. New insights have emerged on the role of astrocytes in signal transduction as has an appreciation of the unique contribution of aerobic glycolysis to brain energy metabolism. Likewise our understanding of the neurophysiologic processes responsible for imaging signals has progressed from an assumption that spiking activity (output) of neurons is most relevant to one focused on their input. Finally, neuroimaging, with its unique metabolic perspective, has alerted us to the ongoing and costly intrinsic activity within brain systems that most likely represents the largest fraction of the brain's functional activity.


RESULTS

ClockΔ19 Mice have an Increase in VTA Dopamine Cell Firing In Vitro, which is Reversed by Chronic Lithium Treatment

Previously we found using in vivo recordings of anesthetized animals that there was an increase in dopaminergic activity and bursting events in the VTA of ClockΔ19 mice compared with wild-type littermates (McClung et al, 2005). To determine if the increased activity remains in the absence of input from other regions, we utilized patch-clamp recordings in coronal slices containing the VTA. In agreement with our previous findings, there is an increase in the firing rate of individual dopaminergic neurons and cumulative probability of firing rate frequency in slices from the ClockΔ19 mice compared with wild-type controls, suggesting that the increased firing rates are the result of local changes in the VTA (Figure 1b and c).

Chronic treatment with lithium normalizes the increased excitability in the ventral tegmental area (VTA) dopamine neurons in ClockΔ19 mice. (a) Sample traces and spikes for extracellular recordings in the VTA slice. (b) Cumulative probability of frequency, and (c) the quantitative data show that VTA firing rate were significantly increased in ClockΔ19 mice, which was decreased by treatment with lithium. Treatment with lithium did not change the VTA firing rate in wild-type mice. *** P<0.0001 (one-way analysis of variance (ANOVA), n=34–58 cells from 5 to 7 mice per group).

To determine if chronic lithium treatment restores proper dopamine cell firing rates to ClockΔ19 mice, we treated these mice, along with wild-type littermates, with 10 days of lithium chloride (600 mg/l) as performed previously (Roybal et al, 2007). We then took slices containing the VTA and measured dopamine cell activity. We found that chronic lithium treatment significantly reduced the firing rates of individual dopamine neurons to wild-type levels in the ClockΔ19 mice, but had no significant effect on the firing rates of dopamine neurons in wild-type littermates (Figure 1a-c). These results show that ClockΔ19 mice have an increase in dopaminergic activity in the VTA, which is restored to wild-type levels by chronic lithium treatment.

Lithium Treatment Reduces NAc Dopamine Levels in ClockΔ19 Mice

We measured the levels of dopamine and its metabolites, DOPAC, 3-MT, and HVA in NAc tissue of ClockΔ19 and wild-type mice either with or without treatment with chronic lithium. We found that ClockΔ19 mice have a ∼ 19% increase in dopamine levels compared with wild-type littermates (Table 1). In agreement with the electrophysiological data, treatment with chronic lithium significantly decreased the levels of dopamine in the ClockΔ19 mice, but had no effect on wild-type mice (Table 1). Furthermore, lithium treatment leads to a significant reduction in the levels of DOPAC, 3-MT, and HVA only in the ClockΔ19 mice.

ClockΔ19 Mice have a Decrease in Dopamine Cell Volume, which is Rescued by Chronic Lithium Treatment

We wanted to determine if there were any cellular or morphological phenotypes that are perhaps responsible for producing the increased dopaminergic activity and manic-like behavior in the ClockΔ19 mice, which are also reversed by chronic lithium treatment. We performed immunohistochemistry on VTA containing sections of ClockΔ19 mice and wild-type littermates either with or without 10 days of lithium treatment (600 mg/l) in the drinking water to stain for TH to identify dopamine neurons and histone H3 to stain the nucleus. We then used confocal microscopy and stereology software to measure various properties of the cells. We found that the soma of dopamine cells in the VTA of ClockΔ19 mice had a reduced volume ( ∼ 20%) compared with the soma of dopamine cells from wild-type littermates (Figure 2a and b). Interestingly, treatment with chronic lithium restored the volume of the cells to wild-type levels. To determine if these changes were indicative of a decrease in the volume of all neurons in the brain, we measured the volume of a non-dopaminergic population of neurons. We selected the subiculum of the hippocampus as there are no dopaminergic neurons in this region, and it is anatomically distinct and identifiable in individual brain sections. We found no difference in the volume of neurons in the subiculum of the hippocampus between ClockΔ19 mice and wild-type littermates (Figure 2c), suggesting that the change in volume is not universal and perhaps is specific to dopamine neurons.

ClockΔ19 mutant mice have smaller dopamine cell soma size, which is restored with lithium treatment (a) Representative dopamine neurons stained with histone H3 (red) and TH antibodies (yellow: red+green). (b) The soma of dopamine neurons of ClockΔ19 mutant mice are significantly smaller than the cell somas of the same neuron type in wild-type littermates ( * P<0.05 by analysis of variance (ANOVA), n=5–8 mice per group, 7–10 cells per animal). Under lithium treatment, the size of the somas of cells from Clock mutant mice significantly goes back to untreated wild-type level ( * P<0.05 by ANOVA, n=5 mice per group, 7–10 cells per animal). There is no significant difference between wild-type mice treated with water or lithium. (b) Non-dopaminergic neurons of the subiculum of the hippocampus have the same volume in ClockΔ19 mutant mice compared with wild-type littermates (n=5 mice per group, 10 cells per animal).

Expression of HSV-Kir2.1 in the VTA Reverses the Firing Rate and Morphological Abnormalities of Dopamine Neurons in ClockΔ19 Mice

As chronic lithium treatment reverses both the increased dopamine cell firing and manic-like behaviors in the ClockΔ19 mice, this suggests that changes in dopaminergic activity may regulate these behavioral responses. However, lithium is known to have many effects in many different brain regions, and these changes in dopaminergic activity may not be relevant to lithium's actions. To test the importance of dopaminergic activity in mood- and anxiety-related behavior directly, we utilized an HSV vector, which contains the Kir2.1 potassium channel subunit. This virus has been used as a tool in previous studies by other groups to manipulate the firing rate of neurons in distinct brain regions including the VTA (Dong et al, 2006 Krishnan et al, 2007). When expressed in the VTA of ClockΔ19 mice, this virus should reduce the firing rate of dopamine neurons, and mimic the effects of chronic lithium treatment on these mice. Indeed, when we expressed this virus directly into the VTA of adult ClockΔ19 mice (Figure 3a) and measured dopamine cell activity (Figure 3b and c), we found that there was a significant decrease in the firing rate that is similar in magnitude to previously published studies using wild-type animals (Krishnan et al, 2007), and this reduction in firing is similar to that produced by lithium treatment (as shown in Figure 1).

Expression of herpes simplex virus (HSV)-Kir2.1 decreases the firing rate of dopamine neurons and restores proper cell size in the ventral tegmental area (VTA) of the ClockΔ19 mice. (a) Schematic diagram showing a coronal section of mouse brain containing the VTA, along with a representative image (bottom left) showing a × 4 magnification of the VTA-specific targeting of HSV by stereotaxic surgery after 4 days. On the bottom right are representative dopamine neurons immunostained with enhanced green fluorescent protein (EGFP) and anti-tyrosine hydroxylase (TH) antibodies and images were merged to see colocalization using a confocal microscope. (b) Sample traces and spikes for extracellular recordings in the VTA slice. (c) HSV-Kir2.1 infection reduces the firing rate of dopamine neurons when compared with both ClockΔ19 animals infected with HSV-GFP and neighboring uninfected neurons in ClockΔ19 animals infected with the HSV-Kir2.1. ** P<0.01 (one-way ANOVA, n=7–15 cells from 5 to 7 mice per group). (d) The size of the soma of dopamine cells of ClockΔ19 mutant mice injected with HSV-Kir2.1 in the VTA are larger than the cells of mutants that are injected with HSV-GFP ( ** P<0.01 by t-test, n=4–5 mice per group, 25–35 cells per group).

To determine the effects of the HSV-Kir2.1 channel on dopamine cell morphology in the ClockΔ19 mice, we performed immunohistochemistry followed by stereology measures of ClockΔ19 brain slices expressing either the HSV-Kir2.1 channel or HSV-GFP. Viral-infected dopamine neurons were readily detectable by their co-labeling of TH and GFP. Similar to chronic lithium treatment, we found that expression of the Kir2.1 gene lead to an increase in the volume of dopamine neurons in the ClockΔ19 mice (Figure 3d). These results show that HSV-Kir2.1 expression produces a change in both dopamine cell morphology and activity that effectively mimics the actions of lithium on the ClockΔ19 mice.

Reduced Dopaminergic Activity in the ClockΔ19 Mice is Associated with a Decreased Locomotor Response to Novelty

To determine if a reduction in the firing rate of dopamine neurons in the VTA of ClockΔ19 mice is sufficient to alter their hyperactivity, we injected the HSV-Kir2.1 virus into the VTA of the mutant mice and measured their locomotor response to a novel environment. We found that ClockΔ19 mice injected with the HSV-Kir2.1 had less locomotor activity over a 2 h period in response to a novel environment (Figure 4b). When examining the beam breaks over the full 2 h, it is apparent that the locomotor response is no different than the HSV-GFP-infected mice over the first 20 min however, the Kir2.1-infected ClockΔ19 mice appear to habituate to the environment more readily over the course of the experiment than mice injected with an HSV-GFP control virus (Figure 4a). Wild-type mice infected with either the HSV-GFP or HSV-Kir2.1 virus were less active overall compared with ClockΔ19 mice, as has been described previously (Easton et al, 2003 Roybal et al, 2007), and the Kir2.1 virus had no effect on their locomotor activity (Figure 4). Importantly, when the ClockΔ19 mice injected with the HSV-Kir2.1 virus are tested on the rotorod, they are indistinguishable from mice injected with the control virus, which shows that they do not have a deficit in motor coordination (data not shown), but have a selective reduction in exploratory activity.

Dopaminergic cell firing rates directly correlate with the hyperactive response to novelty in ClockΔ19 mice. (a) Locomotor activity and the habituation to novelty was determined by number of beam breaks made by herpes simplex virus (HSV)-injected ClockΔ19 and wild-type mice in a novel environment measured in 5 min bins over 2 h. (b) HSV-Kir2.1-injected ClockΔ19 mice exhibited significantly lower levels of locomotor activity than the HSV-green fluorescent protein-injected mice ( ** P<0.001 analysis of variance (ANOVA), n=5–10). There is a significant difference in locomotor activity between wild-type and ClockΔ19 mice injected with HSV-GFP (P<0.001).

Normalization of Dopaminergic Activity in the ClockΔ19 Mice Leads to a Reduction in Anxiety-Related Behavior

We wanted to determine if HSV-Kir2.1 expression in ClockΔ19 mice would lead to a normalization of anxiety-related behavior, which was similar to chronic lithium treatment (Roybal et al, 2007). Here, we utilized both the dark/light and elevated plus-maze tests, which are standard measures of anxiety-related behavior, and both have been validated in several previous studies with anxiolytic medications. Similar to our previous results, we found that the HSV-GFP-injected ClockΔ19 mice show greater exploratory or anxiolytic behavior in both of these tests compared with wild-type mice (Figure 5). In addition, we found that the ClockΔ19 mice injected with the HSV-Kir2.1 spent less time on light side of the dark/light test (Figure 5a) and less time on the open arm of the elevated plus-maze (Figure 5b) than HSV-GFP-injected ClockΔ19 mice, suggesting that a reduction in dopaminergic activity is anxiogenic in these mice. We have great confidence that these results represent true changes in anxiety-related behavior and are not due to changes in locomotor activity as they are consistent between both measures, and because our tests of the locomotor response to novelty (as mentioned above) found no difference in the initial locomotor response to a new environment between the HSV-Kir2.1- and HSV-GFP-injected animals. Both of these anxiety-related measurements were performed within 10–15 min, a time when there is no difference between groups, and the HSV-Kir2.1-injected animals display even a slight, although nonsignificant, increase in the locomotor response to novelty. Interestingly, HSV-Kir2.1 expression in wild-type animals had no significant effect in the time in the open arms of the elevated plus maze (Figure 5b) however, there was a significant increase in the time spent in the light in the dark/light test (Figure 5a), suggesting that decreased dopaminergic activity in wild-type animals perhaps leads to a slight anxiolytic response.

Decreasing the firing rate of dopaminergic cells leads to decreased ‘risk taking’ behavior in ClockΔ19 mice. (a) Herpes simplex virus (HSV)-injected ClockΔ19 and wild-type (WT) mice were subjected to a dark/light test and the time spent on the light side was measured. HSV-Kir2.1-injected mutant mice spent less time in the light side as compared with HSV-green fluorescent protein-injected mice. Wild-type HSV-Kir2.1-injected mice spent significantly more time in the light side than WT HSV-GFP-injected mice (n=9–15, * P&lt0.05 by analysis of variance (ANOVA)). There is a significant difference in time spent in the lighted area between wild-type and ClockΔ19 mice injected with HSV-GFP (P<0.05). (b) HSV-injected ClockΔ19 and WT mice were subjected to the elevated plus-maze test. The time spent on the open arms was determined by video tracking software. HSV-Kir2.1-injected mutant mice spent significantly less time in the open arm as compared with HSV-GFP-injected mice (n=10–20, * P<0.05 by ANOVA). There is a significant difference in time spent on the open arm in between wild-type and ClockΔ19 mice injected with HSV-GFP (P<0.001).

Reduced Dopaminergic Activity in the VTA of ClockΔ19 Mice is Not Sufficient to Restore Proper Mood-Related Behavior

To determine if a reduction in dopamine cell firing in the VTA of ClockΔ19 mice will recapitulate the effects of chronic lithium on depression-related behaviors in the ClockΔ19 mice, we subjected the HSV-Kir2.1 mice and HSV-GFP-injected mice to the FST and learned helplessness (LH) paradigms. These tests are both measures of behavioral despair or ‘helpless’ behavior in a stressful environment and have been extensively validated with antidepressant medications. In the FST, both the latency to immobility and total time immobile were calculated, and in the LH both the latency to escape and failure to escape were calculated. Although the expression of HSV-Kir2.1 was sufficient to alter the locomotor response to novelty- and anxiety-related behavior in the ClockΔ19 mice (Figures 4 and 5), it was not sufficient to alter depression-related behavior in the FST or LH in the same animals (Figure 6). Interestingly, although results did not reach statistical significance, Kir2.1 expression in wild-type animals produced a trend towards an antidepressant response in the LH test (Figure 6).

Depression-like behavior of ClockΔ19 mice is not affected by herpes simplex virus (HSV)-Kir2.1 infection (a, b) HSV-injected ClockΔ19 and wild-type (WT) mice were subjected to the forced swim test (FST). (a) Latency to immobility was determined when the first cessation of all movements occurred for 3 s. There was no difference in latency to immobility observed between HSV-Kir2.1- and HSV-green fluorescent protein-infected mice (n=9–20). (b) Total immobility in the FST was measured and there were no difference in total time immobile between HSV-Kir2.1- and HSV-GFP-injected mice (n=10–20). Both latency to immobility and total time immobile is significantly different between wild-type and ClockΔ19 mice injected with HSV-GFP (P<0.01 by two-way analysis of variance (ANOVA)). (c, d) ClockΔ19 and WT mice were subjected to the learned helplessness paradigm. (c) The latency to escape was not significantly different between the HSV-Kir2.1- and HSV-GFP-injected mice. (d) The number of escape failures was calculated and there was no significant difference between HSV-Kir2.1- and HSV-GFP-injected mice. (n=6–10).


Brain Scans Reveal Difference Between Neanderthals and Us

Neanderthal newborns had similar brains to human infants, though just after birth stark changes began to set in, so that by 1 year old the two children would've had very different noggins and may have even viewed the world differently, researchers now say.

These new findings could shed light on how our closest extinct relatives might have thought differently than us, and reveal details about the evolution of our brain.

Past studies of Neanderthal skulls revealed their brains were comparable in size to ours. This suggested they might have possessed mental capabilities similar to modern humans.

Still, the brains of adult Neanderthals were a different shape than ours &mdash theirs were less globular and more elongated. This elongated shape was actually the norm for more than 2 million years of human evolution, and is seen in chimpanzees as well. [10 Things You Didn't Know About the Brain ]

Comparing scans

To learn more about when differences in brain shape first started appearing in development, researchers created virtual imprints of 11 Neanderthal brains, including a newborn, based on CT scans of their skulls.

The brains of newborn Neanderthals and human infants are about the same size, and both had relatively elongated braincases, likely to help fit through the birth canal, which is roughly similar in shape in both species. After birth, however, and especially in the first year of life, our brains and theirs start to diverge, with those of modern humans becoming more globular.

"I was surprised to see how strong that difference was, even though modern humans and Neanderthals are so closely related, and the genetic differences are so minor," researcher Philipp Gunz, a paleoanthropologist at Max Planck Institute for Evolutionary Anthropology at Leipzig, Germany, told LiveScience.

Modern humans therefore depart from an ancestral pattern of brain development that separates our own species from chimpanzees and all fossil humans, including Neanderthals. The overall shape of the brain probably does not have too much significance in and of itself toward brain function, "but I would say that it does reflect changes in the pattern and timing of the growth of the underlying brain circuitry," Gunz said. This internal organization of the brain is what matters most for mental ability.

"In modern humans, the connections between diverse brain regions that are established in the first years of life are important for higher-order social, emotional, and communication functions," Gunz said. "It is therefore unlikely that Neanderthals saw the world as we do."

Gene differences

This new view on human brain development might help to explain the results of a recent comparison of Neanderthal and modern human genomes.

"Only a few genes separate modern humans from Neanderthals, some of which are related to the brain," Gunz said "What our results suggest is that these genes might be linked with the speed and pattern of brain development."

It is important to note "that all interpretations about Neanderthal cognition will always be somewhat speculative," Gunz cautioned. "What our research could allow is to study what separates modern humans from Neanderthals, to learn something about ourselves and maybe something about Neanderthals as well."

The scientists detailed their findings in the Nov. 9 issue of the journal Current Biology.


Bottom-up and top-down attention: different processes and overlapping neural systems

The brain is limited in its capacity to process all sensory stimuli present in the physical world at any point in time and relies instead on the cognitive process of attention to focus neural resources according to the contingencies of the moment. Attention can be categorized into two distinct functions: bottom-up attention, referring to attentional guidance purely by externally driven factors to stimuli that are salient because of their inherent properties relative to the background and top-down attention, referring to internal guidance of attention based on prior knowledge, willful plans, and current goals. Over the past few years, insights on the neural circuits and mechanisms of bottom-up and top-down attention have been gained through neurophysiological experiments. Attention affects the mean neuronal firing rate as well as its variability and correlation across neurons. Although distinct processes mediate the guidance of attention based on bottom-up and top-down factors, a common neural apparatus, the frontoparietal network, is essential in both types of attentional processes.

Keywords: intraparietal sulcus monkey neurophysiology posterior parietal prefrontal.


1 Answer 1

You've received a few great comments from our neuroscience-savvy users that indicate ways in which this question can be construed as particularly challenging or maybe even unanswerable. However, I think there's a much more basic and limited understanding of your question that takes into account your self-professed unfamiliarity with neuropharmacology and permits a perfectly good intro-level answer. I don't specialize in this – I'm a psychologist, but more of a dabbler in neuroscience – but I'll take a crack at it anyway.

Agonists activate cellular receptors. Excitatory receptors produce excitatory postsynaptic potentials (EPSPs) i.e., they encourage neurons (of which they are components) to "fire". Hence the simple answer: any agonist of a neuron's excitatory receptors increases its firing rate by definition.

Glutamate is an excitatory neurotransmitter. It binds to glutamate receptors, which increase the likelihood or frequency of action potentials (i.e., firing events) in their neurons when activated. NMDA receptors (a type of glutamate receptor) play a role in long-term potentiation (effectively increasing the probability of firing and other criteria of synaptic strength), so the effects of glutamate sometimes outlast its direct activity.

Wikipedia lists several NMDA agonists, some of which are prescribed as ingested drugs. D-Cycloserine has applications in anxiety and addiction treatments. However, there is a balance to maintain, and the objective of medication is generally to restore a normative balance given some abnormality or damage.

Excitotoxicity due to excessive glutamate release and impaired uptake occurs as part of the ischemic cascade and is associated with stroke, [4] autism, some forms of intellectual disability, and diseases like amyotrophic lateral sclerosis, lathyrism, and Alzheimer's disease. [4][14]

Evidently treating Alzheimer's isn't as simple as increasing EPSPs – that might even be a move in the wrong direction. Nicotine may help with Alzheimer's, but the mechanism is more complex:

While tobacco smoking is associated with an increased risk of Alzheimer's disease, [85] there is evidence that nicotine itself has the potential to prevent and treat Alzheimer's disease. [86] . A study has shown a protective effect of nicotine itself on neurons due to nicotine activation of α7-nAChR and the PI3K/Akt pathway which inhibits apoptosis-inducing factor release and mitochondrial translocation, cytochrome c release and caspase 3 activation. [90]

It would seem nicotine's direct action is agonistic, but its mediated effect is antagonistic, and that's the effect that matters for Alzheimer's outcomes. Another wrinkle to nicotine's pharmacology is its agonistic role with acetylcholine, a neurotransmitter that plays excitatory and inhibitory roles for different purposes. This demonstrates the practical complexity of action potentials' causes and consequences.


3 Formulation of Slopes of the Firing Rate

Let us begin by quantifying two slopes of the firing rate curve with respect to the parameters of and I in the 2DHR model in order to study changes of the repetitive firing rate in the bifurcation transitions. The slopes of the firing rate curve are represented as functions related to the PRC. The PRC, one of the firing properties widely used in computational neuroscience, is theoretically derived with the so-called phase reduction method. This method can derive a phase equation in which perturbations of and I on the slow timescale ( ⁠⁠ ) are averaged over a period of the unperturbed dynamics on the fast timescale ( ⁠⁠ ). The derivation of the phase equation is referred to in Hoppensteadt and Izhikevich (1997), Mehrotra and Sangiovanni-Vincentelli (2004), and Schwemmer and Lewis (2012).

lQEtAvAWg__&Key-Pair-Id=APKAIE5G5CRDK6RD3PGA" /> and with and ⁠ . This is due to the fact that in numerical calculations, it is difficult to obtain the exact values of both slopes of the firing rate curve, which will be raised at the beginning of the next section.


Watch the video: Ο ανθρώπινος εγκέφαλος σε μορφή 3D - science (December 2022).