Instruct: Given a question, retrieve relevant documents that can help answer the question.
Query: How fast do neuron signals travel?
Nerve impulses are extremely slow compared to the speed of electricity, where electric current can flow on the order of 50–99% of the speed of light; however, it is very fast compared to the speed of blood flow, with some myelinated neurons conducting at speeds up to 120m/s (432km/h or 275 mph).
A neuron (also known as a neurone or nerve cell) is an excitable cell in the nervous system that processes and transmits information by electrochemical signaling. Neurons are the core components of the brain, the vertebrate spinal cord, the invertebrate ventral nerve cord and the peripheral nerves. A number of specialized types of neurons exist: sensory neurons respond to touch, sound, light and numerous other stimuli affecting cells of the sensory organs that then send signals to the spinal cord and brain. Motor neurons receive signals from the brain and spinal cord that cause muscle contractions and affect glands. Interneurons connect neurons to other neurons within the brain and spinal cord. Neurons respond to stimuli, and communicate the presence of stimuli to the central nervous system, which processes that information and sends responses to other parts of the body for action. Neurons do not go through mitosis and usually cannot be replaced after being destroyed, although astrocytes have been observed to turn into neurons, as they are sometimes pluripotent.
As it pertains to long-term potentiation (LTP), retrograde signaling is a hypothesis describing how events underlying LTP may begin in the postsynaptic neuron but be propagated to the presynaptic neuron, even though normal communication across a chemical synapse occurs in a presynaptic to postsynaptic direction. It is used most commonly by those who argue that presynaptic neurons contribute significantly to the expression of LTP.
Muscle spindles are the sensory receptors located within muscles that allow communication to the spinal cord and brain with information of where the body is in space (proprioception) and how fast body limbs are moving with relation to space (velocity). They are mechanoreceptors in that they respond to stretch and are able to signal changes in muscle length. The sensitivity of detecting changes in muscle length are adjusted by fusimotor neurons – gamma and beta motor neurons. Muscle spindles can be made up of three different types of muscle fibers: dynamic nuclear bag fibers (bag fibers), static nuclear bag fibers (bag fibers), nuclear chain fibers, and the axons of sensory neurons.
Synaptic signaling, an integral part of nervous system activity, occurs between neurons and target cells. These target cells can also be neurons or other cell types (i.e. muscle or gland cells). Protocadherins, a member of the cadherin family, mediate the adhesion of neurons to their target cells at synapses otherwise known as synaptic junctions. In order to for communication to occur between a neuron and its target cell, a wave of depolarization travels the length of the neuron and causes neurotransmitters to be released into the synaptic junction. These neurotransmitters bind and activate receptors on the post-synaptic neuron thereby transmitting the signal to the target cell. Thus, a post-synaptic membrane belongs to the membrane receiving the signal, while a pre-synaptic membrane is the source of the neurotransmitter. In a neuromuscular junction, a synapse is formed between a motor neuron and muscle fibers. In vertebrates, acetylcholine released from the motor neuron acts as a neurotransmitter which depolarizes the muscle fiber and causes muscle contraction. A neuron’s ability to receive and integrate simultaneous signals from the environment and other neurons allows for complex animal behavior.
Some neurons do not generate action potentials, but instead generate a graded electrical signal, which in turn causes graded neurotransmitter release. Such non-spiking neurons tend to be sensory neurons or interneurons, because they cannot carry signals long distances.
An ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer), to the last layer (the output layer), possibly after traversing the layers multiple times.
Computational neuroscience aims to address a wide array of questions. How do axons and dendrites form during development? How do axons know where to target and how to reach these targets? How do neurons migrate to the proper position in the central and peripheral systems? How do synapses form? We know from molecular biology that distinct parts of the nervous system release distinct chemical cues, from growth factors to hormones that modulate and influence the growth and development of functional connections between neurons.
A neuron's ability to generate and propagate an action potential changes during development. How much the membrane potential of a neuron changes as the result of a current impulse is a function of the membrane input resistance. As a cell grows, more channels are added to the membrane, causing a decrease in input resistance. A mature neuron also undergoes shorter changes in membrane potential in response to synaptic currents. Neurons from a ferret lateral geniculate nucleus have a longer time constant and larger voltage deflection at P0 than they do at P30. One consequence of the decreasing action potential duration is that the fidelity of the signal can be preserved in response to high frequency stimulation. Immature neurons are more prone to synaptic depression than potentiation after high frequency stimulation.
The neurons for voluntary thigh contraction originate near the summit of the medial side of the precentral gyrus (the primary motor area of the brain). These neurons send a nerve signal that is carried by the corticospinal tract down the brainstem and spinal cord. The signal starts with the upper motor neurons carrying the signal from the precentral gyrus down through the internal capsule, through the cerebral peduncle, and into the medulla. In the medullary pyramid, the corticospinal tract decussates and becomes the lateral corticospinal tract. The nerve signal will continue down the lateral corticospinal tract until it reaches spinal nerve L4. At this point, the nerve signal will synapse from the upper motor neurons to the lower motor neurons. The signal will travel through the anterior root of L4 and into the anterior rami of the L4 nerve, leaving the spinal cord through the lumbar plexus. The posterior division of the L4 root is the Femoral nerve. The femoral nerve innervates the quadriceps femoris, a fourth of which is the rectus femoris. When the rectus femoris receives the signal that has traveled all the way from the medial side of the precentral gyrus, it contracts, extending the knee and flexing the thigh at the hip.
In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called 'edges'. Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer), to the last layer (the output layer), possibly after traversing the layers multiple times.
Motor information travels from the brain down the spinal cord via descending spinal cord tracts. Descending tracts involve two neurons: the upper motor neuron (UMN) and lower motor neuron (LMN). A nerve signal travels down the upper motor neuron until it synapses with the lower motor neuron in the spinal cord. Then, the lower motor neuron conducts the nerve signal to the spinal root where efferent nerve fibers carry the motor signal toward the target muscle. The descending tracts are composed of white matter. There are several descending tracts serving different functions. The corticospinal tracts (lateral and anterior) are responsible for coordinated limb movements.
Lidocaine alters signal conduction in neurons by prolonging the inactivation of the fast voltage-gated Na channels in the neuronal cell membrane responsible for action potential propagation. With sufficient blockage, the voltage-gated sodium channels will not open and an action potential will not be generated. Careful titration allows for a high degree of selectivity in the blockage of sensory neurons, whereas higher concentrations also affect other types of neurons.
The central issues being addressed are how dendrites integrate synaptic signals in neurons, how networks of neurons generate dynamical patterns of activity, how sensory information is represented in the cerebral cortex, how memory representations are formed and consolidated during sleep, and how visuo-motor transformations are adaptively organized. His laboratory has developed new methods for analyzing the sources for electrical and magnetic signals recorded from the scalp and hemodynamic signals from functional neuroimaging by blind separation using ICA. The EEGLAB public software which was as of 2012 the most popular software for processing EEG data was originally developed in his laboratory.
Proprioceptors are receptors located in muscles, tendons, joints and gut, which send signals to the brain in proportion to accelerations to which the body is subject. An example of a "popular" proprioceptor mentioned by aircraft pilots, is the feeling in the "seat of the pants". Proprioceptors respond to stimuli generated by muscle movement and muscle tension. Signals generated by exteroceptors and proprioceptors are carried by sensory neurons or nerves and are called electrochemical signals. When a neuron receives such a signal, it sends it on to an adjacent neuron through a bridge called a synapse. A synapse "sparks" the impulse between neurons through electrical and chemical means. These sensory signals are processed by the brain in response to signals that travel along motor nerves. Motor neurons, with their special fibres, carry these signals to muscles, which are instructed to either contract or relax.
The scientific study of the nervous system increased significantly during the second half of the twentieth century, principally due to advances in molecular biology, electrophysiology, and computational neuroscience. This has allowed neuroscientists to study the nervous system in all its aspects: how it is structured, how it works, how it develops, how it malfunctions, and how it can be changed. For example, it has become possible to understand, in much detail, the complex processes occurring within a single neuron. Neurons are cells specialized for communication. They are able to communicate with neurons and other cell types through specialized junctions called synapses, at which electrical or electrochemical signals can be transmitted from one cell to another. Many neurons extrude a long thin filament of axoplasm called an axon, which may extend to distant parts of the body and are capable of rapidly carrying electrical signals, influencing the activity of other neurons, muscles, or glands at their termination points. A nervous system emerges from the assemblage of neurons that are connected to each other.
In 1991, scientists became aware that self-avoidance was also present in non-neuronal cell types, such as leech comb cells, which might similarly form discrete domains. Later, this was also observed in mammalian astrocytes.
Wang and Macagno, in 1998, again recurring to "Hirudo medicinalis" mechanosensory neurons, performed an elegant experiment in order to try to answer the still remaining question: "How does a cell recognize self and respond by not growing over or along itself?"
The authors then proposed two general types of mechanisms: I) External signals: Sibling neurites display surface identifying molecular factors, unique to each cell, that are capable of homotypic binding and therefore repel sibling neurites, or II) Internal signals: synchronous cell activity, such as voltage, which is transmitted within the cell mediating a dynamic mechanism of sibling growth inhibition.
Contrarily to the first hypothesis, the second would require the continuity and communication between all parts of the cell for self-avoidance to occur.
So the experiment consisted of detaching one of the neurons dendrites and see how the remaining attached dendrites reacted towards the detached fragment, "do they still avoid overlapping?"
The result was that the detached branch would stop being recognized as "self" by the other branches of the neuronal, leading to dendrite overlap.
The clear conclusion of the study was that continuity between all parts of the neuron is critical for self-avoidance to operate. The authors then suggest various mechanisms that require continuity and could function as recognition signal, and thus might be the responsible ones, such as "electrical activity, active or passive, as well as the diffusion of cytoplasmic signals either passively or by fast axonal transport".
In the late 1990s and beyond, model organisms started to be used in the studies and the molecular mechanisms of self-avoidance started to be unraveled. In 1999 Wu and Maniatis discovered a striking organization of a large family of human neural protocadherin cell adhesion genes which formed a gene cluster encoding 58 protocadherins. The members of the protocadherin gene cluster were compelling candidates to provide the molecular code required for the maintenance of the self/non-self discrimination that led to self-avoidance. It was later (2012) confirmed, by Lefebvre et al., in a study with amacrine cells and Purkinje cells of "Mus musculus", that these proteins are expressed in different combinations in individual neurons, thus providing "barcodes" with that distinguish one neuron from another.
Jack studies how nerve cells, or neurons, communicate with one another in the nervous system. He is also interested in understanding how chemical and electrical signals move through neural networks, such as the spinal cord or cerebral cortex. Although neurons form large networks, these cells do not actually touch each other. Instead, when the end of a nerve is activated it releases ions or chemicals known as neurotransmitters. Subsequently, these move across the gap, or synapse, between the neuron and the adjacent cell in the network, activating its receptors and perpetuating the signal. Jack applies theoretical and experimental approaches to research this process of synaptic transmission. This includes the use of neurophysiology methods to record bioelectrical activity and mathematical models to analyse the central and peripheral nervous systems. His work on neurotransmission is offering insight into disorders of the nervous system, such as Alzheimer’s disease and multiple sclerosis, and has the potential to improve their diagnosis.
Since the 1950s, evidence has existed that neurons in the central nervous system generate an action potential, or voltage spike, that travels both through the axon to signal the next neuron and backpropagates through the dendrites sending a retrograde signal to its presynaptic signaling neurons. This current decays significantly with travel length along the dendrites, so effects are predicted to be more significant for neurons whose synapses are near the postsynaptic cell body, with magnitude depending mainly on sodium-channel density in the dendrite. It is also dependent on the shape of the dendritic tree and, more importantly, on the rate of signal currents to the neuron. On average, a backpropagating spike loses about half its voltage after traveling nearly 500 micrometres.
In sensory neurons, an external signal such as pressure, temperature, light, or sound is coupled with the opening and closing of ion channels, which in turn alter the ionic permeabilities of the membrane and its voltage. These voltage changes can again be excitatory (depolarizing) or inhibitory (hyperpolarizing) and, in some sensory neurons, their combined effects can depolarize the axon hillock enough to provoke action potentials. Some examples in humans include the olfactory receptor neuron and Meissner's corpuscle, which are critical for the sense of smell and touch, respectively. However, not all sensory neurons convert their external signals into action potentials; some do not even have an axon. Instead, they may convert the signal into the release of a neurotransmitter, or into continuous graded potentials, either of which may stimulate subsequent neuron(s) into firing an action potential. For illustration, in the human ear, hair cells convert the incoming sound into the opening and closing of mechanically gated ion channels, which may cause neurotransmitter molecules to be released. In similar manner, in the human retina, the initial photoreceptor cells and the next layer of cells (comprising bipolar cells and horizontal cells) do not produce action potentials; only some amacrine cells and the third layer, the ganglion cells, produce action potentials, which then travel up the optic nerve.
This change in membrane permeability in the dendrites is known as a local graded potential and causes the membrane voltage to change from a negative resting potential to a more positive voltage, a process known as depolarization. The opening of sodium channels allows nearby sodium channels to open, allowing the change in permeability to spread from the dendrites to the cell body. If a graded potential is strong enough, or if several graded potentials occur in a fast enough frequency, the depolarization is able to spread across the cell body to the axon hillock. From the axon hillock, an action potential can be generated and propagated down the neuron's axon, causing sodium ion channels in the axon to open as the impulse travels. Once the signal begins to travel down the axon, the membrane potential has already passed threshold, which means that it cannot be stopped. This phenomenon is known as an all-or-nothing response. Groups of sodium channels opened by the change in membrane potential strengthen the signal as it travels away from the axon hillock, allowing it to move the length of the axon. As the depolarization reaches the end of the axon, or the axon terminal, the end of the neuron becomes permeable to calcium ions, which enters the cell via calcium ion channels. Calcium causes the release of neurotransmitters stored in synaptic vesicles, which enter the synapse between two neurons known as the presynaptic and postsynaptic neurons; if the signal from the presynaptic neuron is excitatory, it will cause the release of an excitatory neurotransmitter, causing a similar response in the postsynaptic neuron. These neurons may communicate with thousands of other receptors and target cells through extensive, complex dendritic networks. Communication between receptors in this fashion enables discrimination and the more explicit interpretation of external stimuli. Effectively, these localized graded potentials trigger action potentials that communicate, in their frequency, along nerve axons eventually arriving in specific cortexes of the brain. In these also highly specialized parts of the brain, these signals are coordinated with others to possibly trigger a new response.
Neurons in the olfactory system are unmyelinated and densely packed and thus the often small effects of ephaptic coupling are more easily seen. A number of studies have shown how inhibition among neurons in the olfactory system work to fine tune integration of signals in response to odor. This inhibition has been shown to occur from changes in electrical potentials alone. The addition of ephaptic coupling to olfactory neuron models adds further support to the "dedicated-line" model in which each olfactory receptor sends its signal to one neuron. The inhibition due to ephaptic coupling would help account for the integration of signals that gives rise to more nuanced perception of smells.
Noise in neurons is due to intrinsic and extrinsic sources. It can disrupt activity and interfere with how well a neuron can encode a signal. Noise is observed as changes in the membrane potential of a cell. The change in potential causes the accuracy of a neuron to be limited in its transmission. This limited transmission has been coined the signal-to-noise ratio. As noise levels increase, one would assume a lowered ratio and therefore diminished signals. A diminished signal can be detrimental to a cell if neuronal maintenance is disrupted, or more importantly a necessary inhibitory response is lost. Noise limits the fidelity of a neuron's response to a signal or stimulus. The accuracy of the signal will impact how well higher parts of the brain or sensory system process information from the neurons.
By mid-90s, despite widespread consensus regarding the clinical relevance of striatal dopaminergic signaling, the distribution and seggregation of different classes of dopamine receptors, into either the same or distinct neuronal populations was unclear and remained widely debated. In pioneering experiments, using patch clamp recordings in conjunction with single cell gene profiling through RT-PCR, Surmeier reconciled the seemingly confounding results from anatomical and functional studies by showing that the direct (striatonigral) and indirect pathway (striatopallidal) striatal projection neurons predominantly expressed either the D1 or D2 dopamine receptors. Following this discovery, using pharmacology and spike-timing-dependent plasticity (STDP) protocols in genetically identified D1 or D2 receptor expressing neurons, Surmeier elucidated the distinct roles played by both the receptors in the induction of long-term potentiation and depression at the cortico-striatal synapses. Simultaneously, he also showed that in projection neurons that do not express the D2 receptors, synaptic depression dependent on D2 receptor activation is mediated by D2 receptors in cholinergic neurons, M1 muscarinic receptor activation resulting in reduced calcium channel, CaV1.3 opening in projection neurons and endocannabinoid signaling. Understanding the opposing effects of D1 and D2 receptor signaling and the consequent insights into the dopaminergic modulation of bi-directional synaptic plasticity in the direct and indirect spiny neurons was a conceptual advance that has proven fundamental to understanding striatal function in both behavioral adaptation as well as Parkinson's disease pathology, and continues to provide a foundation for current models of how dopamine controls striatal circuitry.
The biology behind this amazing ability is that when one trigger hair is stimulated, it actually causes all four large motor neurons to become active. These large motor neurons are fully covered in a glial sheath which is a group of specialized cells that nourish and support neurons. A glial sheath is essential for sending signals at a fast rate, because it allows for a single smooth road rather than having the signal jump from neuron to neuron.