[译]神经模拟
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- 34 分钟阅读 - 16628 个词 阅读量 0神经模拟(译文)
原文地址:https://www.codeproject.com/Articles/1035958/Neuro-Sim
原文作者:Marc Clifton
译文由本站 robot-v1.0 翻译
前言
A mid-level simulation of biological neurons
生物神经元的中级模拟
- 下载Neurosim.zip-41.6 KB(Download neurosim.zip - 41.6 KB) 一些预告截图(Some teaser screenshots)
源代码(Source Code)
除了文章中的链接,如果您想获取最新更新或为项目做贡献,则源代码托管在GitHub上:(Besides the link in the article, if you want to get the latest updates or contribute to the project, the source code is hosted on GitHub:) https://github.com/cliftonm/neurosim(https://github.com/cliftonm/neurosim)
介绍(Introduction)
在本文中,我将介绍一个神经元模拟器和研究工具.通常,神经元仿真要么高度理想化,要么由输入层,一个或多个隐藏层以及输出层组成.信号基于加权和求和阈值通过网络传播.此图是一个基本示例:(In this article I am presenting a neuron simulator and study tool. Typically, neuron simulation is either highly idealized, consisting of an input layer, one or more hidden layers, and an output layers. Signals propagate through the network based on weighting and summation thresholds. This diagram is a basic example:)
相反,其他神经元模拟器将模拟钠,钾和钙通道,处于非常低的水平.您可以阅读有关此内容的更多信息,但这是一个开始:(Conversely, other neuron simulators will be very low level, simulating the sodium, potassium, and calcium channels. You can read more about that, but here’s a start:) http://www.ncbi.nlm.nih.gov/books/NBK26910/(http://www.ncbi.nlm.nih.gov/books/NBK26910/)
本文介绍的是一个中间立场,在该立场上,输入刺激(兴奋性或抑制性)的电特性会影响动作电位.此外,还可以模拟神经元特征,例如其不应期(从动作电位恢复所需的时间).典型动作电位的近似图如下所示:(What this article presents is a middle ground, where the electrical characteristics of input stimulus (either excitatory or inhibitory) affects the action potential. Also, neuron characteristics such as its refractory period (the time it takes to recover from an action potential) are simulated. An approximate plot of a typical action potential looks like this:)
(从((from) https://zh.wikipedia.org/wiki/动作潜力(https://en.wikipedia.org/wiki/Action_potential) )())
在此处提供的软件中,一个典型的绘图如下所示(它几乎没有那么弯曲):(In the software presented here, a typical plot looks like this (it’s not nearly as nicely curvy):)
“起搏器"神经元的神经模拟中理想的动作电位(Idealized Action Potential in Neuro-Sim of a “pacemaker” Neuron)
Neuro-Sim让您体验特定的神经配置:(Neuro-Sim let’s you play around with specific neural configurations:)
模拟反射动作(Simulating a reflex action)
以及与完整的网络一起玩耍,观看这些内容可能会非常令人着迷:(as well as playing around with full networks, which can be quite mesmerizing to watch:)
运行中的神经网络快照(Snapshot of a neural network in action)
本文开头引用Yogi Berra出色的原因是,尽管我们对神经元了解很多,但关于神经元簇如何产生结果也有很多理论(坦率地说,未知数)意识,思考能力等等.另外,请记住,大多数神经元研究都是侵入性的,需要使用探针来测量膜电位和/或电刺激神经元.就我而言,实际上实现的实践是能够思考的神经网络,在我看来仍处于理论领域.(The reason for the excellent Yogi Berra quote at the beginning of this article is that, while there is much we know about neurons, there are also a lot of theories out there (and quite frankly, unknowns) as to how a cluster of neurons results in consciousness, the ability to think, and so forth. Also, keep in mind that most studies of neurons are invasive, requiring probes to measure membrane potential and/or electrically stimulate neurons. The practice of actually implementing is neural network capable of, well, thinking, is still, as far as I’m concerned, in the realm of theory.)
历史(History)
我最初以80 Mhz的速度运行80286处理器(30年前,哇)的时候,写了一个非常类似的模拟器(用C ++),这是对80287数学协处理器的一次重大改进!那时,我只能模拟20x20的神经元网格,那时,它一直在分块.这里展示的模拟器可以轻松处理8000多个神经元,并且可以在我的2.3Ghz i7笔记本电脑上的单个线程(包括UI更新)上运行.因此,这是我多年来一直想复活的后援项目之一,我很高兴终于能够解决这个问题.在当今的计算机上实现此功能最让我感到惊讶的是,我曾期望必须在线程中实现神经元"求和解"计算,并将它们分批分配到可用的CPU上.此情况并非如此!所有神经元计算都在应用程序的主线程中运行.对于较大的网络,我希望我需要平衡多个CPU上的工作负载,但是我尚未测试该实现.(I originally wrote a very similar simulator back (in C++) in the days of the 80286 processor (30 years ago, oh wow), running at 8 Mhz, and it was significant improvement to add in an 80287 math co-processor! At that time, I was only able to simulate a 20x20 grid of neurons, and at that, it chunked along. The simulator presented here easily handles over 8000 neurons, and that’s running on a single thread, including UI updates, on my 2.3Ghz i7 laptop. So, this has been one of those back-burner projects I’ve been wanting to resurrect for years and I’m happy to have finally gotten around to it. What surprised me the most in implementing this on today’s computer is that I had expected to have to implement the neuron “sum and fire” computations in threads, batching them across available CPU’s. This was not the case! All the neuron calculations run in the application’s main thread. For larger networks I would expect that I would need to balance the workload across multiple CPU’s, but I haven’t tested that implementation yet.)
什么不是模拟(What is not Simulated)
不会模拟神经元的各种质量.如果此处使用的术语不熟悉,请阅读下一节"神经元:速成班”.最值得注意的是,Neuro-Sim不能解释:(A variety of qualities of a neuron are not simulated. If the terms used here are unfamiliar, read the next section “Neurons: A Crash Course”. Most notably, Neuro-Sim does not account for:)
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沿轴突的传播时间.(Propagation time along the axon.)
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有髓和无髓轴突(影响繁殖时间).(Myelinated vs. un-myelinated axons (affects propagation time).)
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受体在树突或躯体上的位置(影响局部膜电位变化如何影响轴突岗).(Location of receptor on the dendrite or soma (affects how the localized membrane potential change affects the axon hillock).)
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相对不应期(当前,整个不应期是绝对的-神经元不能再次发射).(Relative refractory period (currently, the entire refractory period is absolute – the neuron cannot fire again).)
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突触前促进或抑制-一些突触不是直接连接到树突或细胞体,而是连接到突触前神经元的突触前节点,从而促进或抑制特定的突触.(Pre-synaptic facilitation or inhibition – rather than connecting directly to the dendrite or cell body, some synapses will connect to the pre-synaptic node of the pre-synaptic neuron, facilitating or inhibiting a specific synapse.)
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没有特定的时间基础-程序执行一个无量纲的"滴答声",在该滴答声中进行所有求和,然后进行任何可能的动作"触发".恢复率基于每"滴答"的变化,而与"滴答"的实际时间单位无关.(There is no specific time basis – the program implements a measureless “tick” in which all summations occur, followed by any action potential “firing.” The recovery rates are based on changes per “tick” with no regard to what unit of time a “tick” actually is.)
鉴于这些限制,仍然可以创建非常有趣的网络和研究.(Given these limitations, very interesting networks and studies can still be created.)
学习(Learning)
该模拟器不实现任何"学习"算法.从生物学的角度来看,神经网络学习的概念非常复杂且了解甚少,其中包括:(This simulator does not implement any “learning” algorithms. The concept of neural network learning, from a biological perspective, is very complicated and poorly understood, and includes such concepts as:)
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突触形成-神经元之间突触的形成.这种情况在一生中都会发生,但在大脑早期发育中最主要,并且还涉及"突触修剪"-消除通常在出生和青春期之间发生的突触.(synaptogenesis - the formation of synapses between neurons. This occurs through life but is most predominant in early brain development and also involves “synaptic pruning” – eliminating synapses, which normally occurs between birth and puberty.)
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神经发生-由神经干细胞或祖细胞产生神经元.同样,这在产前发育中最活跃,但是有证据表明,它是在成人学习和记忆中发生的.(neurogenesis - the creation of neurons from neural stem cells or progenitor cells. Again, this is most active in pre-natal development but there’s some evidence that it occurs in adults as part of learning and memory.)
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赫比理论(Hebbian theory)2(2)(神经可塑性)-突触的有效性可以根据突触前和突触后神经元之间动作电位的时间相关性而增加或降低.这意味着,如果突触前神经元在特定时间段内触发,而突触后神经元也在特定时间段内触发,则突触会增强.如果突触前神经元发动,但突触后神经元不发动(或突触后神经元发动,而突触前神经元不发动),则突触强度会降低.((neuro-plasticity) - the effectiveness of a synapse can be increased or decreased depending on the temporal correlation of the action potential between the pre-synaptic and post-synaptic neuron. Meaning, if the pre-synaptic neuron fires and the post-synaptic also fires within a specific time span, then the synapse is strengthened. If the pre-synaptic neuron fires but the post-synaptic neuron does not (or the post-synaptic neuron fires while a pre-synaptic neuron doesn’t) then the synapse strength is decreased.)
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髓鞘形成的变化-一些理论/研究表明,学习也涉及通过髓鞘形成来改善神经元的功效,即使在成年人中也是如此(抱歉,没有参考文献.)(changes in myelination – some theories / studies indicate that learning also involves improving a neuron’s efficacy through myelination, even in adults (sorry, no reference.))
也有其他理论和研究,但是这些是主要的理论和研究.它们都没有充分解释"学习"是什么,以及"记忆"的相关作用如何,但是它们是有趣的一阶概念.也许将来的实现将解决这些想法.(There are other theories and studies as well, but these are the predominant ones. None of them adequately explain what “learning” is, and how the associated role of “memory” works, but they are interesting first-order concepts to play with. Perhaps a future implementation will address these ideas.)
神经元:速成班(Neurons: A Crash Course)
关于神经元,需要了解一些术语.本节是神经元的速成课程,绝不是完整的课程.我将在这里大量引用Wikipedia的各种定义(是的,我已经向他们捐赠了).虽然我在这里大量引用Wikipedia,但是Neuro-Sim的基础来自于本书(There’s a few terms that one needs to know about with regards to neurons. This section is a crash course in neurons, and is by no means complete. I’m going to heavily reference wikipedia for various definitions here (yes, I’ve made a donation to them.) While I reference Wikipedia a lot here, the basis for Neuro-Sim comes from the book) 神经科学原理(Principles of Neuroscience) ,现在是第五版(我正在使用的书是第二版!)(, now in its fifth edition (the book I’m using is the second edition!) This book is so seminal it has its) 自己的网页(own webpage) .(.)
神经元(Neuron)
“神经元(/njʊərɒn/nyewr-on或/ˈnʊərɒn/newr-on;也称为神经元或神经细胞)是一种电兴奋性细胞,通过电和化学信号处理和传输信息.神经元之间的这些信号通过突触发生.与其他细胞的专门连接神经元可以相互连接形成神经网络,神经元是中枢神经系统(CNS)和周围神经系统神经节(PNS)的大脑和脊髓的核心组件.特殊类型的神经元包括:对触摸,声音,光和所有其他刺激产生影响的感觉神经元,这些感觉神经会影响感觉器官的细胞,然后再将信号发送到脊髓和大脑;运动神经元从大脑和脊髓接收信号引起肌肉收缩并影响腺体输出,以及将神经元连接到大脑同一区域内其他神经元或神经网络中脊髓的神经元.(“A neuron (/ˈnjʊərɒn/ nyewr-on or /ˈnʊərɒn/ newr-on; also known as a neurone or nerve cell) is an electrically excitable cell that processes and transmits information through electrical and chemical signals. These signals between neurons occur via synapses, specialized connections with other cells. Neurons can connect to each other to form neural networks. Neurons are the core components of the brain and spinal cord of the central nervous system (CNS), and of the ganglia of the peripheral nervous system (PNS). Specialized types of neurons include: sensory neurons which respond to touch, sound, light and all other stimuli affecting the cells of the sensory organs that then send signals to the spinal cord and brain, motor neurons that receive signals from the brain and spinal cord to cause muscle contractions and affect glandular outputs, and interneurons which connect neurons to other neurons within the same region of the brain, or spinal cord in neural networks.) 典型的神经元由细胞体(体),树突和轴突组成."(A typical neuron consists of a cell body (soma), dendrites, and an axon.")1个(1)
说神经元是可电激发的并不是完全准确的,因为神经元之间的实际交流通常是化学的-(It’s not entirely accurate to say a neuron is electrically excitable because the actual communication between neurons is usually chemical –)*神经递质(neurotransmitters)*被释放在(are released in the)*突触前裂(pre-synaptic cleft)*兴奋的神经元,并在(of an excited neuron and are received at the)*突触后裂(post-synaptic cleft)*接收神经元的电位,其影响接收神经元的局部膜电位.神经元也有很多种类,每种都有不同的行为,包括确实电连接而不是化学连接的神经元.(of a receiving neuron, which affects the local membrane potential of the receiving neuron. There are also many kinds of neurons, each with different behaviors, including neurons that do connect electrically rather than chemically.)
树突状(Dendrite)
“树突(来自希腊语δένδρονdéndron,“树”)(也是树突)是神经元的分支投影,其作用是将从其他神经细胞接收的电化学刺激传播到树突所源自的神经元的细胞体或体细胞.电刺激是通过上游神经元(通常是它们的轴突)通过突触传递到树突上的,这些突触位于整个树突树的各个点上,树突在整合这些突触输入和确定产生动作电位的程度方面起着至关重要的作用.由神经元."(“Dendrites (from Greek δένδρον déndron, “tree”) (also dendron) are the branched projections of a neuron that act to propagate the electrochemical stimulation received from other neural cells to the cell body, or soma, of the neuron from which the dendrites project. Electrical stimulation is transmitted onto dendrites by upstream neurons (usually their axons) via synapses which are located at various points throughout the dendritic tree. Dendrites play a critical role in integrating these synaptic inputs and in determining the extent to which action potentials are produced by the neuron.")3(3)
在树突处的突触连接通常导致局部膜电位变化,无论是兴奋性的还是抑制性的.这种变化传播到轴突岗(见下文)进行求和.根据距离,轴突岗的实际电势变化将小于突触位置的电势变化. Neuro-Sim中没有模拟这种影响.(A synaptic connection at a dendrite usually results in a local membrane potential change, either excitatory or inhibitory. This change propagates to the axon hillock (see below) for summation. Depending on the distance, the actual potential change at the axon hillock will be less than that at the location of the synapse. This affect is not simulated in Neuro-Sim.)
轴突(Axon)
“轴突(来自希腊语ἄξωνáxōn,轴),又称神经纤维,是神经细胞或神经元的细长细长投影,通常将电脉冲传导离开神经元细胞体.轴突的功能是将信息传递到不同的神经元,肌肉和腺体^轴突在称为突触的连接处与其他细胞(通常是其他神经元,但有时还包括肌肉或腺体细胞)接触.在突触中,轴突的膜紧贴着神经膜.目标细胞和特殊的分子结构可在间隙中传递电或电化学信号^单个轴突及其所有分支都聚集在一起,可以支配大脑的多个部分并产生数千个突触末端."(“An axon (from Greek ἄξων áxōn, axis), also known as a nerve fibre, is a long, slender projection of a nerve cell, or neuron, that typically conducts electrical impulses away from the neuron’s cell body. The function of the axon is to transmit information to different neurons, muscles and glands…Axons make contact with other cells—usually other neurons but sometimes muscle or gland cells—at junctions called synapses. At a synapse, the membrane of the axon closely adjoins the membrane of the target cell, and special molecular structures serve to transmit electrical or electrochemical signals across the gap…A single axon, with all its branches taken together, can innervate multiple parts of the brain and generate thousands of synaptic terminals.")4(4)
神经元的一些更奇怪的行为,例如没有在神经元中传递信号至树突的轴突,或以其他轴突突触结尾的轴突,是Neuro-Sim中未模拟的许多例外中的两个.(Some of the more bizarre behaviors of neurons, such as not having an axon where signals transmitting to dendrites, or axons ending in synapses on other axons, are two of many exceptions that are not simulated in Neuro-Sim.)
轴突山(Axon Hillock)
“轴突丘陵是体细胞中从突触输入传播的膜电位被累加后再传递到轴突的最后一个部位^抑制性突触后电位(IPSP)和兴奋性突触后电位(EPSP)都在轴突丘陵和一旦超过触发阈值,动作电位将通过其余的轴突传播(并在神经反向传播中向后向树突传播),这是由于高度拥挤的电压门控钠通道之间的正反馈引起的.出现在轴突丘陵(和更红节的结点)的临界密度处,但不在躯体中.(“The axon hillock is the last site in the soma where membrane potentials propagated from synaptic inputs are summated before being transmitted to the axon…Both inhibitory postsynaptic potentials (IPSPs) and excitatory postsynaptic potentials (EPSPs) are summed in the axon hillock and once a triggering threshold is exceeded, an action potential propagates through the rest of the axon (and “backwards” towards the dendrites as seen in neural backpropagation). The triggering is due to positive feedback between highly crowded voltage-gated sodium channels, which are present at the critical density at the axon hillock (and nodes of ranvier) but not in the soma.)
在其静止状态下,神经元被极化,其内部相对于周围环境约为-70 mV.当兴奋性神经递质由突触前神经元释放并与突触后树突棘结合时,配体门控离子通道打开,从而使钠离子进入细胞.这可能会使突触后膜去极化(阴性).这种去极化将朝着轴突岗行进,并随着时间和距离的增加呈指数减小.如果在短时间内发生几次此类事件,则轴突岗可能会充分去极化,以使电压门控钠通道打开.这会引发一个动作电位,然后向下传播到轴突."(In its resting state, a neuron is polarized, with its inside at about -70 mV relative to its surroundings. When an excitatory neurotransmitter is released by the presynaptic neuron and binds to the postsynaptic dendritic spines, ligand-gated ion channels open, allowing sodium ions to enter the cell. This may make the postsynaptic membrane depolarized (less negative). This depolarization will travel towards the axon hillock, diminishing exponentially with time and distance. If several such events occur in a short time, the axon hillock may become sufficiently depolarized for the voltage-gated sodium channels to open. This initiates an action potential that then propagates down the axon.")5(5)
换句话说,固然更复杂,但轴突岗是对膜电位的局部变化(通常来自沿枝晶的突触,也来自细胞体本身)进行汇总的地方.如果轴突丘陵区(或轴突的第一个无髓节段)的膜电位超过特定阈值,则会发生动作电位-神经元被激发.(In other words, while of course more complicated, the axon hillock is where localized changes in membrane potential (usually from synapses along the dendrite but also on the cell body itself) are summed. If the membrane potential at the axon hillock (or at the first unmyelinated segment of the axon) exceeds a specific threshold, an action potential occurs – the neuron fires.)
突触(Synapse)
“在神经系统中,突触[1]是允许神经元(或神经细胞)将电或化学信号传递到另一个神经元的结构^突触对于神经元功能至关重要:神经元是专门用于将信号传递给单个靶细胞,突触是信号传递的手段,在突触中,信号传递神经元(突触前神经元)的质膜与靶(突触后)细胞的膜紧密贴合突触前和突触后位点均包含大量分子机制,将两个膜连接在一起并执行信号传导过程,在许多突触中,突触前位点位于轴突上,但一些突触后位点位于树突或体上."(“In the nervous system, a synapse[1] is a structure that permits a neuron (or nerve cell) to pass an electrical or chemical signal to another neuron…Synapses are essential to neuronal function: neurons are cells that are specialized to pass signals to individual target cells, and synapses are the means by which they do so. At a synapse, the plasma membrane of the signal-passing neuron (the presynaptic neuron) comes into close apposition with the membrane of the target (postsynaptic) cell. Both the presynaptic and postsynaptic sites contain extensive arrays of molecular machinery that link the two membranes together and carry out the signaling process. In many synapses, the presynaptic part is located on an axon, but some postsynaptic sites are located on a dendrite or soma.")6(6)
突触的建模实际上非常复杂.突触的空间位置很重要,因为随着突触向着轴突岗的移动,突触后神经元上局部发生的去极化会随着时间和距离而减少(这不是Neuro-Sim建模的).此外,取决于神经递质和突触的类型,神经递质可能不会立即被``清除'',从而导致持续的去极化(也不是Neuro-Sim的模型).如前所述,突触前的促进和抑制作用会影响突触前的裂隙,而不是突触后神经元也是可能的(再次不是Neuro-Sim建模的).这些只是突触的一些有趣特征.(The modeling of a synapse is actually quite complex. The spatial location of a synapse is important as the depolarization that occurs locally on post-synaptic neuron diminishes over time and distance as it travels toward the axon hillock (this is not modeled by Neuro-Sim.) In addition, depending on the neurotransmitter and the type of synapse, the neurotransmitter might not be “cleaned up” right away, allowing for continuous depolarization (also not modeled by Neuro-Sim.) As mentioned earlier, pre-synaptic facilitation and inhibition, affecting the pre-synaptic cleft, not the post-synaptic neuron, is possible also (again not modeled by Neuro-Sim.) These are just a few of the interesting characteristics of synapses.)
不应期(Refractory Period)
“不应期是由于电压门控钠通道的失活特性和钾通道关闭的滞后性造成的.电压门控钠通道具有两种门控机制,即通过去极化打开通道的激活机制和关闭去极化的钝化机制.当通道处于非激活状态时,它不会响应去极化而打开;大多数钠通道保持处于非激活状态的时间是绝对不应期,在此之后,有足够的电压激活的钠通道处于闭合(活动)状态以响应去极化,但是,响应于复极而打开的电压门控钾通道的闭合速度不如电压门控钠通道快;返回到活性闭合状态.这次,额外的钾电导率意味着膜处于较高的阈值,将需要更大的刺激来引起可能会引起火灾.这个时期是相对不应期."(“The refractory periods are due to the inactivation property of voltage-gated sodium channels and the lag of potassium channels in closing. Voltage-gated sodium channels have two gating mechanisms, the activation mechanism that opens the channel with depolarization and the inactivation mechanism that closes the channel with repolarization. While the channel is in the inactive state, it will not open in response to depolarization. The period when the majority of sodium channels remain in the inactive state is the absolute refractory period. After this period, there are enough voltage-activated sodium channels in the closed (active) state to respond to depolarization. However, voltage-gated potassium channels that opened in response to repolarization do not close as quickly as voltage-gated sodium channels; to return to the active closed state. During this time, the extra potassium conductance means that the membrane is at a higher threshold and will require a greater stimulus to cause action potentials to fire. This period is the relative refractory period.")7(7)
这里的突出部分是存在一个绝对不应期,其中神经元不能再次发射(通常约1毫秒),而相对不应期中的动作电位阈值较高.后者不是由Neuro-Sim建模的.(The salient part here being that there is an absolute refractory period in which the neuron cannot fire again (usually about 1ms) and a relative refractory period in which the action potential threshold is higher. The latter is not modeled by Neuro-Sim.)
静息电位(Resting Potential)
静态细胞的相对静态膜电位称为静止膜电位(或静止电压),与特定的动态电化学现象相反,称为动作电位和梯度膜电位.(“The relatively static membrane potential of quiescent cells is called the resting membrane potential (or resting voltage), as opposed to the specific dynamic electrochemical phenomena called action potential and graded membrane potential.")8(8)
静息电位可针对特定的神经元"研究"进行配置,并以"网络"模式进行组分配.(The resting potential is configurable for specific neuron “studies” and is group assigned in “network” mode.)
阈值电位(Threshold Potential)
“阈值电位是膜电位必须去极化以引发动作电位的临界水平."(“The threshold potential is the critical level to which the membrane potential must be depolarized in order to initiate an action potential.")9(9)
在某些情况下,神经元的去极化可能会缓慢发生,以至于需要高于正常阈值的阈值才能启动动作电位.实际上,如果去极化发生得足够缓慢,则没有任何水平的去极化将达到动作电位.在Neuro-Sim中未对这种行为进行建模.但是,阈值电位可针对特定的神经元"研究"进行配置,并以"网络"模式进行组分配.(Under certain conditions, the depolarization of a neuron can occur slowly enough that a higher than normal threshold is required to initiate an action potential. In fact, if the depolarization occurs slowly enough, no level of depolarization will achieve an action potential. This behavior is not modeled in Neuro-Sim. However, the threshold potential is configurable for specific neuron “studies” and is group assigned in “network” mode.)
动作电位(Action Potential)
“在生理学中,动作电位是一种持续时间很短的事件,其中细胞的电膜电位沿着一致的轨迹迅速上升和下降.动作电位发生在几种类型的动物细胞中,称为可兴奋细胞,其中包括神经元,肌肉细胞,内分泌细胞以及某些植物细胞.在神经元中,它们在细胞间通讯中起着核心作用;在其他类型的细胞中,它们的主要功能是激活细胞内过程.例如,动作电位是导致收缩的一系列事件的第一步,在胰腺的β细胞中,它们激发胰岛素的释放.[a]神经元中的动作电位也称为"神经冲动"或"突波”.神经元所产生的动作电位的时间序列称为"尖峰序列”.发出动作电位的神经元通常被称为"发射”.(“In physiology, an action potential is a short-lasting event in which the electrical membrane potential of a cell rapidly rises and falls, following a consistent trajectory. Action potentials occur in several types of animal cells, called excitable cells, which include neurons, muscle cells, and endocrine cells, as well as in some plant cells. In neurons, they play a central role in cell-to-cell communication. In other types of cells, their main function is to activate intracellular processes. In muscle cells, for example, an action potential is the first step in the chain of events leading to contraction. In beta cells of the pancreas, they provoke release of insulin.[a] Action potentials in neurons are also known as “nerve impulses” or “spikes”, and the temporal sequence of action potentials generated by a neuron is called its “spike train”. A neuron that emits an action potential is often said to “fire”.)10(10)
动作电位值可针对特定神经元"研究"进行配置,并以"网络"模式进行组分配.(The action potential value is configurable for specific neuron “studies” and is group assigned in “network” mode.)
起搏器神经元(Pacemaker Neuron)
起搏器神经元是定期发出自发信号的神经元. Neuro-Sim在网络仿真中使用起搏器神经元来启动(并维持)网络活动.起搏器神经元的数量可在"网络"模式下配置.(A pacemaker neuron is a neuron that spontaneously fires at regular intervals. Neuro-Sim uses pacemaker neurons in the network simulations to kick-start (and maintain) network activity. The number of pacemaker neurons is configurable in “network” mode.)
一个有趣的简单"研究"网络由两个起搏器神经元组成,其中一个神经元抑制或刺激另一个起搏器神经元.稍后我们将看到几个这样的例子,在目标起搏器神经元中实现简单的分频和频率增加.(An interesting simple “study” network consists of two pacemaker neurons, where one neuron either inhibits or stimulates the other pacemaker neuron. Later on we will see a couple examples of this, achieving simple frequency division and frequency increases in the target pacemaker neuron.)
髓鞘形成(Myelination)
“髓磷脂是一种脂肪白色物质,围绕着轴突介电(电绝缘)材料,形成一层髓鞘,通常仅围绕神经元的轴突.这对于神经系统的正常运转至关重要.一种神经胶质细胞的长出.(“Myelin is a fatty white substance that surrounds the axon dielectric (electrically insulating) material that forms a layer, the myelin sheath, usually around only the axon of a neuron. It is essential for the proper functioning of the nervous system. It is an outgrowth of a type of glial cell.)
髓鞘的产生称为髓鞘形成.在人类中,髓鞘形成始于胎儿发育的第14周,尽管在出生时大脑中几乎没有髓鞘.在婴儿期,髓鞘形成迅速发生,导致儿童快速发育,包括在第一年爬行和行走.髓鞘化一直持续到青春期."(The production of the myelin sheath is called myelination. In humans, myelination begins in the 14th week of fetal development, although little myelin exists in the brain at the time of birth. During infancy, myelination occurs quickly, leading to a child’s fast development, including crawling and walking in the first year. Myelination continues through the adolescent stage of life.")11(11)
除了无法正确拼写该单词外,髓鞘化还使电信号能够快速传播,并且不会沿轴突下降到突触前末端.如果没有髓鞘化,我们的神经系统将无法正常工作.(Besides not being able to ever spell this word correctly, myelination is what allows an electrical signal to travel quickly and without degradation down the axon to the pre-synaptic terminals. Without myelination, our nervous system simply would not work.)
Neuro-Sim中使用的其他术语(Additional Terms used in Neuro-Sim)
耐火材料回收率(Refractory Recovery Rate)
对于神经元从动作电位或抑制输入后发生的超极化恢复到静止电位的速度有多快,这是一个可配置的选项.(This is a configurable option for how quickly a neuron returns to its resting potential from the hyperpolarization that occurs after an action potential or an inhibitory input.)
超极化超调(Hyperpolarization Overshoot)
这是在动作电位后将膜电位设定为的水平.(This is the level that the membrane potential is set to after an action potential.)
静息潜在回报率(Resting Potential Return Rate)
这是神经元从未产生动作电位的去极化事件返回到其静止电位的速率.(This is the rate at which a neuron returns from a depolarization event that did not result in an action potential, back to its resting potential.)
仿真模式(Simulation Modes)
Neuro-Sim中实现了两种仿真模式:网络和学习.(There are two simulation modes implemented in Neuro-Sim: network and study.)
网络模式(Network Mode)
网络模式,显示一个动作电位衰减图(Network Mode, showing an Action Potential Decay Plot)
在网络模式下,所有神经元的配置都相同,但是您可以控制:(In network mode, all the neurons are configured the same, however you can control:)
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连接数(the number of connections)
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连接之间的最大距离(或轴突长度)(the maximum distance (or length of axon) between connections)
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连接"半径”-连接来自轴突端点的分散程度(the connection “radius” – how dispersed the connections are from the axon endpoint)
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起搏器神经元的数量-定期发射以启动模拟的神经元.(the number of pacemaker neurons – the neurons that fire at regular intervals to kickstart the simulation.)
还有查看图的方法:(There are also to ways of viewing the plot:)
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动作电位衰减图-如上图所示.当神经元发射时,绘图仪会将神经元显示为白色,并经过10步以上的步伐,使神经元的图谱从黄色,红色到最后变为黑色.(Action Potential Decay Plot – this is illustrated above. When a neuron fires, the plotter will show the neuron as white and over 10 steps decay the neuron’s plot through yellow, red, and finally black.)
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膜电位图-如下图所示,该图在以下位置绘制神经元:(Membrane Potential Plot – as illustrated below, this plot draws a neuron in:)
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发射时呈白色(white when it fires)
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不应期红色(red during its refractory period)
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代表神经元膜电位的绿色阴影(实际上很难看到)(and shades of green (fairly hard to see actually) representing the neuron’s membrane potential)
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黑色表示神经元处于静息状态.(black for neurons at their resting potential.)
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网络模式,显示膜电位图(Network Mode, showing a Membrane Potential Plot)
学习模式(Study Mode)
学习模式:反射模拟(Study Mode: A Reflex Simulation)
在学习模式下,用户可以创建具有特定特征和连接的单个神经元.每个神经元都有一个"探针”,显示其在示波器上的活动.上面的屏幕截图说明了演示之一,即"反射"仿真.稍后将描述配置值和用法.(In study mode, the user can create individual neurons with specific characteristics and connections. Each neuron has a “probe” that shows its activity on the scope. The above screenshot illustrates one of the demos, a “reflex” simulation. The configuration values and usage is described later.)
用户界面(The UI)
请点击(Click) 这里(here) 有关启动时Neuro-Sim的完整截图.(for a full-size screenshot of Neuro-Sim on startup.)
让我们首先处理UI.有两种显示方式:左侧有神经网络或研究网络(如下所示),在右侧有示波器(“示波器”)显示研究网络中的神经元.以下是三个标签:(Let’s first deal with the UI. There are two displays: on the left we have either the neural network or the study network (shown below) and on the right we have an oscilloscope (“scope”) display of neurons in the study network. Below, there are three tabs:)
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Neuron-默认的神经元配置(Neuron - the default neuron configuration)
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研究-用于研究状态显示在示波器上的小型网络(Study - for studying small networks whose state is displayed on the scope)
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网络-用于配置神经元连接和模拟大型神经网络(Network - for configuring neuron connectivity and simulating large neural networks)
Neuron配置选项(Neuron Configuration Options)
配置默认神经元(Configuring the Default Neuron)
可以为神经元配置的六个值是:(The six values that can be configured for a neuron are:)
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静止电位-默认情况下,静止电位为-65mv(毫伏).这表示神经元的默认状态.(Resting Potential – by default, the resting potential is -65mv (millivolts). This represents the acquiescent state of the neuron.)
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AP阈值-这是动作电位阈值-如果求和部位(轴突岗)的膜电位达到或超过此值,则会产生动作电位(神经元"发动”)(AP Threshold – this is the action potential threshold – if the membrane potential at the summation site, the axon hillock, meets or exceeds this value, an action potential occurs (the neuron “fires”))
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AP值-这是动作电位值,默认为40mv.(AP Value – this is the action potential value, by default 40mv.)
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参考建议速率-该值表示作为速率的静息电位的返回率-每"““的膜电位的变化.(Ref. Rec. Rate – this value represents the return to the resting potential as a rate – the change in membrane potential per “tick.")
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HP过冲-这是从静息电位减去的值,代表动作电位后的超极化现象.默认值为20mv,表示动作电位后的超极化值将为-65-20或-85mv.(HP Overshoot – this is the value subtracted from the resting potential to represent the hyperpolarization after an action potential. The default, 20mv, represents that the hyperpolarization value after an action potential will be -65 - 20, or -85mv.)
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RP Return Rate –这个值代表去极化后恢复到静息电位–每"滴答"膜电位的变化.当神经元被去极化而未达到动作电位阈值时,该值从每个” t"的去极化膜电位中减去,最终使神经元恢复到其静止电位.(RP Return Rate – this value represents the return to the resting potential after depolarization – the change in membrane potential per “tick.” When the neuron is depolarized without achieving the action potential threshold, this value is subtracted from the depolarized membrane potential per “tick”, eventually returning the neuron to its resting potential.)
对于仿真的"网络模式”,可以实时更改这些值.(These values can be changed in realtime for the “network mode” of the simulation.)
网络模式配置选项(Network Mode Configuration Options)
网络模式配置选项(Network Mode Configuration Options)
在网络模式下,可以配置以下内容:(In network mode, the following can be configured:)
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连接数(the number of connections)
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连接之间的最大距离(或轴突长度)(the maximum distance (or length of axon) between connections)
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连接"半径”-连接来自轴突端点的分散程度(the connection “radius” – how dispersed the connections are from the axon endpoint)
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起搏器神经元的数量-定期发射以启动模拟的神经元.(the number of pacemaker neurons – the neurons that fire at regular intervals to kickstart the simulation.)
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图类型-动作电位衰减图或膜电位图(如前所述)(the plot type – action potential decay plot or membrane potential plot (as described earlier.))
学习模式(Study Mode)
学习模式神经元配置(Study Mode Neuron Configuration)
在学习模式下,可以将单个神经元添加到网络中或从网络中删除.每个神经元的选项也可以具有特定的配置:(In study mode, individual neurons can be added and removed from the network. Each neuron’s options can also have a specific configuration:)
可以为神经元配置的六个值是:(The six values that can be configured for a neuron are:)
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RP:静止电位-默认情况下,静止电位为-65mv(毫伏).这表示神经元的默认状态.(RP: Resting Potential – by default, the resting potential is -65mv (millivolts). This represents the acquiescent state of the neuron.)
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APT:AP阈值-这是动作电位阈值-如果求和部位(轴突岗)的膜电位达到或超过此值,就会出现动作电位(神经元"发射”)(APT: AP Threshold – this is the action potential threshold – if the membrane potential at the summation site, the axon hillock, meets or exceeds this value, an action potential occurs (the neuron “fires”))
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APV:AP值-这是动作电位值,默认为40mv.(APV: AP Value – this is the action potential value, by default 40mv.)
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$$$$:参考.建议速率-该值表示作为速率的静息电位的返回率-每"““的膜电位的变化.(RRR: Ref. Rec. Rate – this value represents the return to the resting potential as a rate – the change in membrane potential per “tick.")
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HPO:HP过冲-这是从静息电位减去的值,代表动作电位后的超极化现象.默认值为20mv,表示动作电位后的超极化值将为-65-20或-85mv.(HPO: HP Overshoot – this is the value subtracted from the resting potential to represent the hyperpolarization after an action potential. The default, 20mv, represents that the hyperpolarization value after an action potential will be -65 - 20, or -85mv.)
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RPRR:RP回复率-该值表示去极化后恢复到静息电位-每"滴答"膜电位的变化.当神经元被去极化而未达到动作电位阈值时,该值从每个” t"的去极化膜电位中减去,最终使神经元恢复到其静止电位.(RPRR: RP Return Rate – this value represents the return to the resting potential after depolarization – the change in membrane potential per “tick.” When the neuron is depolarized without achieving the action potential threshold, this value is subtracted from the depolarized membrane potential per “tick”, eventually returning the neuron to its resting potential.)
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LKG:泄漏-表示去极化率(每"刻度”),用于创建起搏器神经元(LKG: Leakage – this represents a depolarization rate (per “tick”) and is used to create a pacemaker neuron)
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PCOLOR:范围图颜色-此值是自动分配的(PCOLOR: the scope plot color – this value is auto-assigned)
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Conn:神经元的联系(Conn: the neuron’s connections)
连接数(Connections)
连接是逗号分隔的"连接到"神经元列表以及括号内突触后膜电位变化的列表.例如:(The connections is a comma delimited list of “connect to” neuron and the post-synaptic membrane potential change in parenthesis. For example:)
3(40),4(40),6(40)(3(40),4(40),6(40))
表示突触前神经元连接到神经元3\4和6(上面的屏幕快照中的神经元编号” n”),并在每种情况下导致40mv的突触后去极化.兴奋性突触由正值表示,抑制性突触由负值表示.例如,这里是一个抑制性突触:(indicates that the pre-synaptic neuron connects to neurons 3, 4, and 6 (the neuron number “n” in the screenshot above) and results in a post-synaptic depolarization of 40mv in each case. Excitatory synapses are represented by positive values and inhibitory synapses are represented by negative values. Here, for example, is an inhibitory synapse:)
5(-40)(5(-40))
网络(The Network)
学习模式网络图(Study Mode Network Plot)
在学习模式下,使用神经元的理想表示来绘制网络图:(In study mode, the network is plotted using an idealized representation of a neuron:)
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细胞体由圆圈表示.(The cell body is represented by a circle.)
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兴奋性突触表示为白色三角形.(Excitatory synapses are represented as white triangles.)
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抑制性突触表示为黑色三角形.(Inhibitory synapses are represented as black triangles.)
同样,在运行模拟时,当神经元激发时,“细胞体”(圆圈)会变成红色并像动作电位衰减图一样经历衰减.(Also, when running the simulation, when a neuron fires, the “cell body” (the circle) turns red and goes through the decay as in an action potential decay plot.)
调整学习模式图(Adjusting the Study Mode Plot)
在研究图中移动神经元后(After Moving Neurons in the Study Plot)
用户可以单击并拖动神经元"身体”(圆圈)以创建所需的视觉图.上面的屏幕截图显示了相同的网络,但神经元的位置不同.同样,当单击神经元的"身体"时,将选择神经元配置网格中的相应条目,这使得更容易确定哪个神经元是什么. (当然,此UI可以改进很多!)(The user can click and drag a neuron “body” (the circle) to create the desired visual plot. The above screenshot shows the same network but with the neurons in different positions. Also, when clicking on a neuron’s “body”, the corresponding entry in the neuron configuration grid will be selected, which makes it easier to figure out which neuron is what. (Granted, this UI could be improved a lot!))
暂停/恢复/步(Pause / Resume / Step)
通过单击"恢复"开始仿真.您也可以通过单击暂停来暂停模拟.暂停时,将启用"步进"按钮.步骤将运行模拟器,直到神经元激发.(You start a simulation by clicking on “Resume”. You can also pause the simulation by clicking on Pause. When paused, the Step button is enabled. Step will run the simulator until a neuron fires.)
学习模式示例(Study Mode Examples)
我整理了几个例子.(I’ve put together several examples to play with.)
起搏器神经元(Pacemaker Neuron)
起搏器神经元示例(A Pacemaker Neuron Example)
这是一个非常简单的示例,说明了起搏器神经元的活动.起搏器神经元可用于播种网络或模拟外部刺激(如反射示例中所用).(This is very simple example illustrating the activity of a pacemaker neuron. A pacemaker neuron is useful for seeding a network or simulating external stimulus (as is used in the reflex example).)
分频器示例(Frequency Divider Example)
分频器神经元示例(A Divider Neuron Example)
在此示例中,简单的分频器神经"电路"说明了重复的动作电位将如何导致突触后神经元以分级方式将动作电位相加,并最终在达到突触后神经元的动作电位阈值时触发.(In this example, a simple frequency divider neural “circuit” illustrates how a recurring action potential will result in the post-synaptic neuron summing the action potentials in a graded manner and eventually firing when the post-synaptic neuron’s action potential threshold is met.)
频率降低器示例(Frequency Decreaser Example)
降低频率的例子(Decreasing Frequency Example)
在此示例中,基线起搏器神经元(红色迹线)用于说明一个起搏器神经元(青色迹线)如何用于降低第二个起搏器神经元(白色迹线)的放电速率.这是抑制信号作用的一个很好的例子,因为只要突触前神经元(青色迹线)激发,就可以看到对目标神经元的抑制作用(极化).(In this example, a baseline pacemaker neuron (the red trace) is used to illustrate how one pacemaker neuron (the cyan trace) can be used to decrease the firing rate of a second pacemaker neuron (the white trace). This is a nice example of the action of an inhibitory signal, as one can see the inhibitory action (polarization) on the target neuron whenever the pre-synaptic neuron (the cyan trace) fires.)
增频器示例(Frequency Increaser Example)
频率增加示例(Increasing Frequency Example)
与前一个示例类似,基线神经元(红色迹线)用于显示突触前起搏器神经元(青色迹线)如何增加突触后起搏器神经元(白色迹线)的发射频率.突触前起搏器神经元以兴奋的方式作用于突触后神经元,通过使神经元进一步去极化来提高其放电速度.(Similar to the previous example, a baseline neuron (the red trace) is used to show how a pre-synaptic pacemaker neuron (the cyan trace) increases the frequency of firing of the post-synaptic pacemaker neuron (the white trace.) Here, the pre-synaptic pacemaker neuron acts in an excitatory manner upon the post-synaptic neuron, increasing its firing rate by further depolarizing the neuron.)
反射动作示例(Reflex Action Example)
这是最有趣的示例,因为它模拟了膝盖跳动反射:(This is the most interesting example, as it models the knee jerk reflex:)
膝盖混蛋反射(由(Knee Jerk Reflex (courtesy of) http://cbsetextbooks.weebly.com/21-neural-control-and-coordination.html(http://cbsetextbooks.weebly.com/21-neural-control-and-coordination.html) )())
在Neuro-Sim中,将其建模为:(In Neuro-Sim, this is modeled as:)
反射动作模型(Model of Reflex Action)
在此示例中,连接到"肌肉纺锤”(红色圆圈)的神经元具有传入路径,该路径连接到"肌肉"中的神经元,该神经元响应刺激(绿色)而发生抽动.信号也被发送到"大脑”,它试图通过激发试图激活运动终板肌肉的神经元来防止收缩.但是,这是不可能的,因为传入神经元通过中间神经元产生抑制性反应,阻止"大脑"在附着于运动终板的神经上产生肌肉收缩.(In this example, the neuron attached to the “muscle spindle” (circled in red) has an afferent pathway, connecting to the neuron in the “muscle” that jerks in response to stimulus (green). A signal is also sent to the “brain” which tries to prevent the contraction by exciting a neuron that attempts to activate the muscles at the motor endplate. However, this is not possible because the afferent neuron, via an interneuron, creates an inhibitory reaction, prevent the “brain” from creating a muscle contraction on the nerve attached to the motor endplate.)
反射动作的范围和网络跟踪(Scope and Network Trace of the Reflex Action)
上图说明了示波器轨迹和发射的神经元.请注意,右下神经元不会发射,因为中间神经元正在抑制它,因此,作为一个人,您不能阻止腿部抽搐!(The above diagram illustrates the scope trace and the neurons that fire. Note the bottom right neuron does not fire because the interneuron is inhibiting it, thus you, as a person, cannot prevent your leg from jerking up!)
幕后花絮(Behind the Scenes)
模拟器真的没有那么多!(There really isn’t that much to the simulator!)
神经元状态(Neuron State)
神经元实现一种状态机,该状态机确定神经元是否正在整合(总计兴奋性和抑制性输入),执行动作电位或处于不应期:(Neurons implement a state machine that determines whether the neuron is integrating (summing excitatory and inhibitory inputs), performing an action potential, or is in the refractory period:)
public void Tick()
{
switch (actionState)
{
case State.Integrating:
Integrate();
Fired = FireOnActionPotential();
if (!Fired)
{
// Incrementally return to resting potential.
int dir = Math.Sign(config.RestingPotential - CurrentMembranePotential);
// Get the min delta so that we return exactly to the resting potential on the last step.
if (dir == -1) // current membrane potential > resting potential, so return at some rate to the resting potential.
{
CurrentMembranePotential += config.RestingPotentialReturnRate;
}
break;
case State.Firing:
Fired = false;
++actionState; // hold for one 1 tick
break;
case State.RefractoryStart:
// refactory period start
CurrentMembranePotential = config.RestingPotential - config.HyperPolarizationOvershoot;
++actionState;
break;
case State.AbsoluteRefractory:
// refactory period, linear ramp back to the resting potential.
CurrentMembranePotential += config.RefractoryRecoveryRate;
if (CurrentMembranePotential >= config.RestingPotential)
{
// Done with absolute refractory period.
inputs.Clear(); // The neuron doesn't integrate any inputs during the absolute refractory period.
actionState = State.Integrating;
}
break;
case State.RelativeRefractory:
// Not implemented.
break;
}
}
神经元数学(Neuron Math)
数学都是基于整数的.整数的"电位"用整数的高24位表示,而小数部分用低8位表示.从理论上讲,这可以加快积分的处理速度,因为我们可以完全处于整数数学之内.通过查看默认神经元的配置方式来说明该概念:(The math is all integer based. Whole number “potentials” are represented in the upper 24 bits of the integer, while the fractional component is represented in the lower 8 bits. This (theoretically) speeds up the processing of the integration, as we can stay completely within integer math. The concept is illustrated by seeing how the default neuron is configured:)
public NeuronConfig()
{
RestingPotential = -65 << 8;
ActionPotentialThreshold = -35 << 8;
ActionPotentialValue = 40 << 8;
RefractoryRecoveryRate = 1 << 8;
HyperPolarizationOvershoot = 20 << 8;
RestingPotentialReturnRate = -8;
}
网络图(Network Plots)
通过使用CPian” ratamoa"于2006年在2006年证明的FastPixel实现,改进了网络图的性能.(The performance of network plots is improved by using the FastPixel implementation that CPian “ratamoa” proved in, yes, 2006, in) 本文(this article) .这样可以快速写入像素:(. This allows for fast pixel writes:)
public void Plot(FastPixel fp, Point location, Color color)
{
fp.SetPixel(location, color);
fp.SetPixel(location + new Size(1, 0), color);
fp.SetPixel(location + new Size(0, 1), color);
fp.SetPixel(location + new Size(1, 1), color);
}
每个神经元绘制为2x2方框.(where each neuron is plotted as a 2x2 box.)
研究图(Study Plots)
使用标准渲染研究网络中的神经元(The neurons in the study network are rendered using the standard) Graphics
功能.这里是最复杂的代码,用于计算如何绘制连接的"轴突"和"突触”:(functions. The most complicated code is here, in calculating how to draw the connecting “axon” and “synapse”:)
protected void DrawSynapse(Pen pen, Graphics gr, Point start, Point end, Connection.CMode mode)
{
double t = Math.Atan2(end.Y - start.Y, end.X - start.X);
double endt = Math.Atan2(start.Y - end.Y, start.X - end.X);
// start at the circumference of the circle.
Size offset = new Size((int)(5 * Math.Cos(t)), (int)(5 * Math.Sin(t)));
// end at the circumference of the target neuron circle.
Point endOffset = new Point((int)(15 * Math.Cos(endt)), (int)(15 * Math.Sin(endt)));
end.Offset(endOffset);
gr.DrawLine(pen, start + offset, end);
// the synapse is a triangle whose base is opposite and perpendicular to the endpoint.
double v1angle = t - 0.785398163;
double v2angle = t + 0.785398163;
Point v1 = new Point((int)(10 * Math.Cos(v1angle)), (int)((10 * Math.Sin(v1angle))));
v1.Offset(end);
Point v2 = new Point((int)(10 * Math.Cos(v2angle)), (int)((10 * Math.Sin(v2angle))));
v2.Offset(end);
switch (mode)
{
case Connection.CMode.Excitatory:
// A white triangle.
gr.DrawLine(pen, end, v1);
gr.DrawLine(pen, end, v2);
gr.DrawLine(pen, v1, v2);
break;
case Connection.CMode.Inhibitory:
// A black triangle.
gr.FillPolygon(brushBlack, new Point[] { end, v1, v2 }, System.Drawing.Drawing2D.FillMode.Winding);
break;
}
}
这涉及计算突触前体和突触后体之间的角度,以及保持连接偏移代表神经元体的圆的宽度.(This involves calculating the angle between the pre-synaptic body and the post-synaptic body, as well as keeping the connection offset by the width the of circle representing the neuron body.)
轴突是"虚拟化的”,因为多个连接都从突触前的身体来源沿不同的方向延伸,因此看起来好像神经元具有多个轴突:(Axons are “virtualized”, in that multiple connections all extend in different directions from the pre-synaptic body source, so it appears as if the neuron has multiple axons:)
神经元有一个轴突,但看起来像三个(Neuron has One Axon But Looks Like Three)
如上所示,看起来左侧的神经元具有连接到其他三个神经元的三个轴突-这是绘制网络的便利.(As the above illustrates, it looks like the neuron on the left has three axons connecting to three other neurons – this is a convenience for drawing the network.)
结论(Conclusion)
作为通过整合,动作和不应期对神经元膜电位的初步模拟,Neuro-Sim是一个有趣且有趣的工具,可用于简单的神经元网络.在寻找"网络模式"时,非常令人着迷,但目前它还不代表任何具体含义.添加外部刺激(声音,图像等)以及探索学习和记忆可能是下一步的逻辑步骤.(As a preliminary simulation of a neuron’s membrane potential through integration, action, and refractory periods, Neuro-Sim is a fun and interesting tool to use to play with simple neuron networks. In find the “network mode” is quite mesmerizing to watch but for the moment it doesn’t represent anything concrete. Adding external stimuli (sound, images, etc) is perhaps a next logical step, as well as exploring learning and memory.)
参考文献(References)
1-神经元:(1 - Neuron:) https://zh.wikipedia.org/wiki/Neuron(https://en.wikipedia.org/wiki/Neuron) 2-Hebbian理论:(2 - Hebbian Theory:) https://en.wikipedia.org/wiki/Hebbian_theory(https://en.wikipedia.org/wiki/Hebbian_theory) 3-树突(3 - Dendrite:) https://zh.wikipedia.org/wiki/树枝状(https://en.wikipedia.org/wiki/Dendrite) 4-轴突:(4 - Axon:) https://zh.wikipedia.org/wiki/轴突(https://en.wikipedia.org/wiki/Axon) 5-轴突山(Axon Hillock):(5 - Axon Hillock:) https://zh.wikipedia.org/wiki/Axon_hillock(https://en.wikipedia.org/wiki/Axon_hillock) 6-突触:(6 - Synapse:) https://zh.wikipedia.org/wiki/突触(https://en.wikipedia.org/wiki/Synapse) 7-不应期:(7 - Refractory Period:) https://en.wikipedia.org/wiki/Refractory_period_(生理学)(*https://en.wikipedia.org/wiki/Refractory_period_(physiology)*) 8-静息电位:(8 - Resting Potential:) https://zh.wikipedia.org/wiki/Resting_potential(https://en.wikipedia.org/wiki/Resting_potential) 9-阈值电位:(9 - Threshold Potential:) https://zh.wikipedia.org/wiki/阈值电位(https://en.wikipedia.org/wiki/Threshold_potential) 10-动作电位:(10 - Action Potential:) https://zh.wikipedia.org/wiki/动作潜力(https://en.wikipedia.org/wiki/Action_potential) 11-髓鞘形成:(11 - Myelination:) https://zh.wikipedia.org/wiki/米林(https://en.wikipedia.org/wiki/Myelin)
许可
本文以及所有相关的源代码和文件均已获得The Code Project Open License (CPOL)的许可。
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