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Conceptualization: J. M; Manuscript writing: J. Correspondence to Huibert D. Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Reprints and Permissions. Lateral inhibition by Martinotti interneurons is facilitated by cholinergic inputs in human and mouse neocortex.
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Abstract A variety of inhibitory pathways encompassing different interneuron types shape activity of neocortical pyramidal neurons. Introduction Inhibition of pyramidal neurons by GABAergic interneurons is essential for cortical computation. Results Delayed lateral inhibition is selectively enhanced by basal forebrain cholinergic inputs Pyramidal neurons in the neocortex can inhibit neighboring pyramidal cells PCs by feedforward activation of inhibitory interneurons 5 , Full size image.
Discussion In this study, we addressed the question whether cholinergic projections from the basal forebrain modulate cortical lateral inhibition between pyramidal neurons. Human brain slice preparation All performed procedures on human tissue were in line with the Dutch license procedures and the declaration of Helsinki and approved by the Medical Ethical Committee of the VU University Medical Centre.
Pharmacology All drugs used were dissolved in aCSF at the final concentration and bath applied during the experiments. Analysis and statistics Raw data was analyzed using Clampfit Data availability The data that support the findings of this study are available from the corresponding author on request.
References 1. Article Google Scholar 3. Article Google Scholar Acknowledgements We thank Anton Pieneman, Christiaan de Kock for assistance with biocytin stainings and Hans Lodder and Jaap Timmerman for excellent technical assistance.
View author publications. Ethics declarations Competing interests The authors declare no competing interests. Additional information Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material. Supplementary Information. About this article. Cite this article Obermayer, J. Copy to clipboard. Dienel , David A.
Heistek , Sybren F. Galakhova , Ayoub J. Khalil , Tim Kroon , Allert J. Jonker , Roel de Haan , Wilma D. Goriounova , Christiaan P. Mansvelder Nature Communications Platonov donders. Goossens donders. Journal of Vision May , Vol.
Alerts User Alerts. The role of lateral inhibition in binocular motion rivalry. You will receive an email whenever this article is corrected, updated, or cited in the literature. You can manage this and all other alerts in My Account. This feature is available to authenticated users only.
Get Citation Citation. Get Permissions. Binocular rivalry is a phenomenon which occurs when our eyes receive a pair of stereo-incompatible inputs at the same retinal location. This apparent dissociation between the visual input and the perceptual output is of interest because it may provide insight into the origin of visual awareness. For example, the fact that under most circumstances the two stimuli are never seen together e. However, the exact mechanisms underlying binocular rivalry are not fully understood.
Most current models of binocular rivalry assume that the alternations between dominance and suppression result from the interaction between feedback cross-inhibition and slow self-adaptation e. Feedback cross-inhibition implies that neurons representing the competing percepts inhibit each other through their output, resulting in suppression of the initially weaker percept while the other one becomes dominant.
The inhibitory influence of the dominant population on the suppressed cells then slowly decays as a result of adaptation of the dominant population, allowing the suppressed cells to re gain dominance. This, in turn, allows the previously dominant population to recover from adaptation. In this way, the adapting reciprocal-inhibition model of binocular rivalry explains both suppression and alternation but see, e.
Figure 1. View Original Download Slide. Changes in binocular rivalry dynamics as predicted by changes in cross-inhibition strength. A Adaptation reciprocal-inhibition model after Noest et al. Parameter values were taken from Noest et al.
B — C Simulation of low-contrast gray curves and high-contrast black curves stimulus conditions. Note that the mean dominance durations decrease systematically with decreasing strength of the cross-inhibition, and that this decrease is stronger for stimuli in the preferred eye solid curves versus the non-preferred eye dashed curves.
Both effects are stronger for weak gray curves versus strong black curves inputs. C Corresponding changes in predominance, where predominance is defined as the percentage of total stimulus time during which a given unit was dominant i. Figure 1 Changes in binocular rivalry dynamics as predicted by changes in cross-inhibition strength.
Evidence for reciprocal inhibition originates from Levelt's influential study on binocular rivalry dynamics, which showed that increasing the contrast of the image in one eye did not increase the dominance duration of that image but instead decreased the dominance duration of the image in the other eye Levelt's Second Proposition. This relationship seems counterintuitive at first glance but is easily explained within the framework of reciprocal inhibition where a given stimulus generates not an isolated response but one linked to the response generated by another competing stimulus.
So far, however, the nature of this reciprocal inhibition remains elusive. Lateral inhibition is a nearly universal component of sensory processing, permitting populations of coarsely tuned neurons to support discrimination more accurately than would otherwise be possible.
Motion repulsion is the illusory enlargement of the angular separation between two targets moving in two different, but almost similar, directions. Since neither fatigue nor adaptation can explain the simultaneous influence of two motion directions on each other, motion repulsion is generally interpreted as resulting from lateral inhibition between neighboring direction-tuned channels, where the strength of the inhibition increases with decreasing angular distance between the two motion patterns.
On the other hand, there is also clear evidence that motion opponency plays a role in visual motion processing e. We reasoned that manipulating the angle between the directions of motion in the two eyes would provide a means to change the strength of the inhibitory link between the two competing channels in binocular rivalry and test the nature of the competition.
More specifically, if the competition relies on lateral inhibition between neighboring direction-tuned channels, decreasing the angle between two competing motion directions is expected to enhance the strength of the cross-inhibition between their neuronal representations.
Alternatively, if the competition relies on opponent motion processing i. Feedback cross-inhibition models predict that enhanced cross-inhibition between two perceptual representations leads to increases in dominance durations of the two competing percepts. This prediction holds because the suppressed population would need more time to recover sufficiently from its adapted state to overcome the stronger but decaying due to adaptation inhibition from the currently dominant population.
In models without any input asymmetries, the winner could be either one of the two competing populations, with no preference for one or the other across trials. Thus, one would expect that for low-contrast stimuli, the preferred eye will dominate more as the strength of the cross-inhibition increases, lapsing eventually into complete dominance of that eye.
Conversely, this effect is expected to attenuate as a function of decreasing cross-inhibition strength until the system reaches the regime in which conflicting stimuli produce more balanced percept durations.
Predictably, the other way to attenuate the effect of strong cross-inhibition would be to increase the strength of the visual input. Figure 1 illustrates these predictions by means of simulations with a simplified version of the adaptation reciprocal-inhibition model proposed by Noest et al. Thus, as lateral inhibition effectively increases the strength of the cross-inhibition with decreasing interocular difference in motion direction, for weak inputs, decreasing the angle between the two motion directions should lead to larger predominance and longer dominance durations of the motion pattern presented to the preferred eye.
Moreover, an attenuation of these effects should occur for strong inputs. If, on the other hand, the strength of the cross-inhibition changes due to opponent processing, the relationship between changes in eye dominance and motion—direction disparity should be opposite, i. To test these different predictions, we manipulated the angle between the directions of motion in the two eyes as well as the contrast level of the stimuli.
We report that decreasing the angle between the two monocular directions of motion increased the predominance and mean dominance durations of the motion pattern presented in the subjects' preferred eye, and this effect was attenuated if the contrast of the two images was increased.
Simulations with a neural network model showed that these results can be readily understood from lateral inhibition between populations of coarsely tuned motion sensitive units. Interestingly, our subjects not only reported alternations between the two monocular directions of motion. This behavior could—in principle—be explained by extending our population model with positive feedback arising from adapting disinhibitory circuits, but other interactions are also considered.
Four human subjects with normal or corrected to normal visual acuity participated after giving informed consent. Subjects were seated in front of a computer screen ViewSonic, VXw in an otherwise dark room. Their head and chin was supported by a forehead rest and chin cup. Visual motion stimuli were generated by a personal computer equipped with an openGL graphics card and presented to the subjects' left and right eye by means of a front-mirror stereoscope.
The total viewing distance was 67 cm. The image refresh rate was 60 Hz. Subjects indicated the direction of perceived visual motion by pressing mouse buttons. Button presses were recorded by the stimulus program. Eye preference c. The procedures were approved by the Radboud University Medical Centre. Every dot started at a random location within the aperture and then moved at 4. At the end of its 67 ms lifetime, a dot was replaced by a new dot at a new random location within the aperture.
Life times of the individual dots were asynchronous. RDKs presented to the left and right eye were generated independently. In the low- and high-contrast conditions, luminance of the dots were set to 7.
Subjects fixated a 0. One second after the fixation cross appeared, RDKs with two different directions of motion were presented to the left and right eye for 1 min. Figure 2. Illustration of the dichoptic motion stimuli used in the experiments. Figure 2 Illustration of the dichoptic motion stimuli used in the experiments. In case of piecemeal or transparent motion percepts i. If the direction of perceived motion was purely vertical, subjects were instructed to press a middle button.
No button had to be pressed if no coherent visual motion pattern was perceived, e. Stimuli were presented in blocks of 24 trials in which all possible motion directions were presented in pseudo random order. To avoid fluctuations in light—dark adaptation of the retina, each block only included trials of the same contrast. High- and low-contrast blocks were presented in a random order. The time between two subsequent blocks was at least 24 hrs. On average, the duration of each block was 30 min.
Each subject accomplished six blocks. For each trial, we calculated the mean dominance duration of each motion percept as well as its predominance. Predominance was expressed either as a percentage of a total viewing time percent total; Figure 3 or as a percentage of the viewing time during which either one of the oblique i.
The resulting values were sorted according to the eye of origin i. Post hoc testing was done with linear regression analysis and Student's t tests. Figure 3. Predominance A and mean dominance durations B of the pure vertical motion percept solid curves and oblique percept dashed curves in A as a function of motion—direction disparity for the low-contrast gray curves and high-contrast black curves conditions. Predominance quantifies the percentage of total stimulus time during which a given percept was dominant.
Data are averaged across subjects. Subjects started to report pure vertical motion percepts as the monocular directions of motion approached the vertical meridian for small motion—direction disparities.
Figure 3 Predominance A and mean dominance durations B of the pure vertical motion percept solid curves and oblique percept dashed curves in A as a function of motion—direction disparity for the low-contrast gray curves and high-contrast black curves conditions.
Figure 4. Predominance values are expressed as a percentage of the total time during which either one of the two oblique motion percepts were dominant. Predominance values depended systematically on the difference between the two monocular directions motion and stimulus contrast.
For large interocular differences in motion direction i. Transparent motion or lack of a coherent motion percept was rarely indicated.
However, as the two monocular directions of motion got closer and more vertical , the subjects started to report pure vertical motion percepts. Since this behavior influenced the dominancy of the nonvertical motion percepts, especially at the smallest motion—direction disparities where lateral inhibition predicts the biggest effects , we first present an analysis of the subjects' vertical motion percepts in relation to the nonvertical motion percepts.
Predominance and mean dominance durations of the vertical percept. In line with previous reports by Blake, Zimba, and Williams , the occurrence and durations of pure vertical, in-between motion percepts increased as the directions of motion got closer and more vertical thus reducing the overall predominance of the two nonvertical motion percepts.
The unambiguous vertical stimuli were nearly always perceived as pure vertical motion, except by Subject S2. However, the predominance and mean durations of those in-between percepts decreased rapidly with increasing motion—direction disparity and increasing deviations from vertical.
In the following sections we quantify the respective changes in dominance of the two nonvertical motion percepts. If lateral inhibition causes the strength of the cross-inhibition to change with the interocular difference in motion direction, then for weak inputs, decreasing the angle between the two motion directions should lead to larger predominance and longer dominance durations of the motion pattern presented to the preferred eye as compared with the motion pattern presented to the non-preferred eye.
Moreover, an attenuation of these effects should occur for strong inputs c. If rivalry is instead mediated by opponent competition, changes in eye dominancy as a function of motion—direction disparity should be opposite. We analyzed the changes in predominance of the two nonvertical motion percepts using multifactor ANOVA. Independent variables in this analysis were subject, motion—direction disparity, and stimulus contrast.
Figure 4 quantifies these effects by comparing the predominance of stimuli in the preferred solid curves and non-preferred dashed curves eye for the low- gray and high-contrast black condition. As predicted by our lateral-inhibition hypothesis, the mean predominance of the subjects' preferred eye increased systematically with decreasing motion—direction disparity, especially in the low-contrast condition.
Moreover, increasing the stimulus contrast in both eyes attenuated the dominancy of the preferred eye over the non-preferred eye. Figure 5 quantifies these interactions by plotting the difference in predominance of stimuli in the preferred and non-preferred eye positive values indicate larger predominance of the preferred eye as a function of the motion—direction disparity for the two contrast conditions. Linear regression analysis indicated a systematic increase in predominance of the preferred versus non-preferred eye with decreasing motion—direction disparity.
Moreover, in the low-contrast condition, the slope of the trend line was on average about three times steeper than in the high-contrast condition. To better demonstrate this dissociation between the low- and high-contrast conditions, the inset of Figure 5 plots their difference.
Figure 5. Average difference between predominance of nonvertical motion in the preferred and non-preferred eyes for low-contrast gray and high-contrast black stimuli. Positive values signify larger predominance of the preferred eye. Data are plotted as a function of motion—direction disparity.
Inset shows the difference between the low-contrast and high-contrast data. Results are averaged across subjects. Figure 5 Average difference between predominance of nonvertical motion in the preferred and non-preferred eyes for low-contrast gray and high-contrast black stimuli. Our findings thus indicate an increasing difference between binocular rivalry dynamics in the low- and high-contrast conditions if the angle between the two monocular directions of motion decreases.
We then analyzed the mean dominance durations of the non-vertical motion percepts. Independent variables in the initial ANOVA were subject, eye preference, motion—direction disparity, and stimulus contrast. As predicted by our lateral-inhibition hypothesis, however, these effects were different for the preferred versus non-preferred eye.
To demonstrate these interaction effects, Figure 6 plots the mean dominance durations of the subjects' preferred solid curves and non-preferred dashed curves eye in the low-contrast gray and high-contrast black conditions. Note that the mean dominance durations increased systematically as a function of decreasing motion—direction disparity, especially in the preferred eye.
Moreover, for each eye the mean dominance durations were typically shorter in the high-contrast condition compared with the low-contrast condition. Figure 6.
Mean dominance duration of the preferred solid curves and non-preferred dashed curves eye in the low- gray curves and high-contrast black curves conditions as a function of motion—direction disparity.
Figure 6 Mean dominance duration of the preferred solid curves and non-preferred dashed curves eye in the low- gray curves and high-contrast black curves conditions as a function of motion—direction disparity. In addition, there was a robust difference between mean dominance durations of the preferred and non-preferred eye, which increased with decreasing motion—direction disparity.
To further quantify these effects, Figure 7 plots the difference between the mean dominance durations of the preferred and non-preferred eye positive values indicate larger predominance of the preferred eye as a function of motion—direction disparity. Indeed, in the low-contrast condition, the slope of the regression line was on average about four times steeper than in the high-contrast conditions.
The inset of Figure 7 demonstrates this dissociation between the low- and high-contrast conditions by plotting the difference. For smaller angles, the oblique motion percepts gave way to pure vertical motion percepts c.
Figure 7. Average difference between mean dominance durations of the preferred and non-preferred eye for low-contrast gray and high-contrast black motion stimuli.
Data are plotted as a function of the motion—direction disparity. Insets show the difference between low-contrast and high-contrast data. Thin gray lines in the main panel and inset are linear regression fits to the data. Figure 7 Average difference between mean dominance durations of the preferred and non-preferred eye for low-contrast gray and high-contrast black motion stimuli.
Our experimental findings are consistent with the notion that decreases in motion—direction disparity effectively increase the strength of the mutual inhibition between neural populations that represent the two monocular directions of motion. Here, we explore the possibility that these changes in cross-inhibition strength could be an emergent property of weighted lateral inhibition between populations of coarsely tuned visual motion cells.
Towards that end, we extended the adaptation reciprocal-inhibition model from Figure 1 by incorporating two layers of 36 adapting cells Figure 8A , each coarsely tuned to a different direction of motion Figure 8B. The preferred directions of the cells were uniformly distributed, and cells within each layer received visual input only from one eye. Moreover, each unit inhibited cells in the other layer via an interneuron which had long-range inhibitory connections gray connections.
The fixed strength of these feedback connections decreased as a function of the cells' tuning distance so that cells having the same preferred direction inhibited each other strongest Figure 8C.
As in the model of Figure 1 , the inhibitory interneurons had no dynamics, and also the properties of the adapting cells were kept the same. Figure 8D illustrates the spatial—temporal pattern of activity within each of the two percept-encoding layers in response to opponent horizontal motion stimuli i.
Note the reciprocal activation pattern of the two populations. Mean dominance durations of the two states scale with the adaptation time constant, here set to one for simplicity. Figure 8. Parsimonious population model for binocular motion rivalry. Units in each layer received monocular visual inputs X i and X j , and inhibited cells in the other layer via an inhibitory interneuron gray.
For clarity, the graph only shows the connections for two of those interneurons. The fixed strength of these inhibitory feedback connections decreased as a function of tuning distance, so that cells having the same preferred direction inhibited each other strongest. Black arrows: excitatory connections.
Gray bullets: inhibitory connections two units only. B Input pattern for opponent horizontal motion in the two eyes i. Axons can be rather long extending up to a meter or so in some human sensory and motor nerve cells.
The synapse is the terminal region of the axon and it is here where one neuron forms a connection with another and conveys information through the process of synaptic transmission.
The aqua-colored neuron in Figure 1 click on "Neuron Connected to a Postsynaptic Neuron" is referred to as the postsynaptic neuron. The tan-colored terminal to the left is consequently referred to as the presynaptic neuron. One neuron can receive contacts from many different neurons. Figure 1 click on "Neuron Receiving Synaptic Input" shows an example of three presynaptic neurons contacting the one tan-colored postsynaptic neuron, but it has been estimated that one neuron can receive contacts from up to 10, other cells.
Consequently, the potential complexity of the networks is vast. Similarly, any one neuron can contact up to 10, postsynaptic cells. Note that the tan-colored neuron that was presynaptic to the aqua-colored neuron is postsynaptic to the pink, green, and blue neurons.
Figure 1 click on "The Synapse" also shows an expanded view of the synapse. Note that the presynaptic cell is not directly connected to the postsynaptic cell. The two are separated by a gap known as the synaptic cleft.
Therefore, to communicate with the postsynaptic cell, the presynaptic neuron needs to release a chemical messenger. That messenger is found within the neurotransmitter-containing vesicles the blue dots represent the neurotransmitter.
An action potential that invades the presynaptic terminal causes these vesicles to fuse with the inner surface of the presynaptic membrane and release their contents through a process called exocytosis. The binding to the receptors leads to a change in the permeability of ion channels in the membrane and in turn a change in the membrane potential of the postsynaptic neuron known as a postsynaptic synaptic potential PSP.
So signaling among neurons is associated with changes in the electrical properties of neurons. To understand neurons and neuronal circuits, it is necessary to understand the electrical properties of nerve cells. Resting potentials.
Figure 2 shows an example of an idealized nerve cell. Placed in the extracellular medium is a microelectrode. A microelectrode is nothing more than a small piece of glass capillary tubing that is stretched under heat to produce a very fine tip, on the order of 1 micron in diameter.
The microelectrode is filled with a conducting solution and then connected to a suitable recording device such as an oscilloscope or chart recorder. With the electrode outside the cell in the extracellular medium, zero potential is recorded because the extracellular medium is isopotential. If, however, the electrode penetrates the cell such that the tip of the electrode is now inside the cell, a sharp deflection is seen on the recording device. A potential of about millivolts inside negative with respect to the outside is recorded.
This potential is called the resting potential and is constant for indefinite periods of time in the absence of any stimulation.
If the electrode is removed, a potential of zero is recorded again. Resting potentials are not just characteristics of nerve cells; all cells in the body have resting potentials. What distinguishes nerve cells and other excitable membranes e. In the case of nerve cells, for integrating information and transmitting information, whereas, in the case of muscle cells, for producing muscle contractions.
Action potentials. Figure 3 shows another sketch of an idealized neuron. This neuron has been impaled with one electrode to measure the resting potential and a second electrode called the stimulating electrode. The stimulating electrode is connected through a switch to a battery. If the battery is oriented such that the positive pole is connected to the switch, closing the switch will make the inside of the cell somewhat more positive depending upon the size of the battery.
Such a decrease in the polarized state of a membrane is called a depolarization. Figure 3 is an animation in which the switch is repeatedly opened and closed and each time it is closed a larger battery is applied to the circuit. Initially, the switch closure produces only small depolarizations. However, the potentials become larger and eventually the depolarization is sufficiently large to trigger an action potential , also known as a spike or an impulse.
The peak is followed by an equally rapid repolarization phase. The voltage at which the depolarization becomes sufficient to trigger an action potential is called the threshold. If a larger battery is used to generate a suprathreshold depolarization, a single action potential is still generated and the amplitude of that action potential is the same as the action potential trigged by a just-threshold stimulus.
The simple recording in Figure 3 illustrates two very important features of action potentials. First, they are elicited in an all-or-nothing fashion. Either an action potential is elicited with stimuli at or above threshold, or an action potential is not elicited. Second, action potentials are very brief events of only about several milliseconds in duration. Initiating an action potential is somewhat analogous to applying match to a fuse. A certain temperature is needed to ignite the fuse i.
A match that generates a greater amount of heat than the threshold temperature will not cause the fuse to burn any brighter or faster. Just as action potentials are elicited in an all-or-nothing fashion, they are also propagated in an all-or-nothing fashion.
Once an action potential is initiated in one region of a neuron such as the cell body, that action potential will propagate along the axon like a burning fuse and ultimately invade the synapse where it can initiate the process of synaptic transmission. In the example in Figure 3, only a single action potential was generated because the duration of each of the two suprathreshold stimuli was so brief that sufficient time was only available to initiate a single action potential i.
But, as shown in the animations of Figure 4, longer-duration stimuli can lead to the initiation of multiple action potentials, the frequency of which is dependent on the intensity of the stimulus. Therefore, it is evident that the nervous system encodes information not in terms of the changes in the amplitude of action potentials, but rather in their frequency.
This is a very universal property. The greater the intensity of a mechanical stimulus to a touch receptor, the greater the number of action potentials; the greater the amount of stretch to a muscle stretch receptor, the greater the number of action potentials; the greater the intensity of a light, the greater the number of action potentials that is transmitted to the central nervous system.
Similarly, in the motor system, the greater the number of action potentials in a motor neuron, the greater will be the contraction of the muscle that receives a synaptic connection from that motor neuron. Engineers call this type of information coding pulse frequency modulation. Figure 5 illustrates three neurons.
The one colored green will be referred to as an excitatory neuron for reasons that will become clear shortly.
It makes a connection to the postsynaptic neuron colored blue. The traces below press "Play" illustrate the consequences of initiating an action potential in the green neuron. That action potential in the presynaptic neuron leads to a decrease in the membrane potential of the postsynaptic cell. The membrane potential changes from its resting value of about millivolts to a more depolarized state.
This potential is called an excitatory postsynaptic potential EPSP. Generally and this is an important point , a single action potential in a presynaptic cell does not produce an EPSP large enough to reach threshold and trigger an action potential. But, if multiple action potentials are fired in the presynaptic cell, the corresponding multiple excitatory potentials can summate through a process called temporal summation to reach threshold and triggering an action potential.
The red-colored neuron in Figure 5 is referred to as an inhibitory neuron. Like the green neuron, it also makes a synaptic contact with the blue postsynaptic neuron. It also releases a chemical transmitter messenger, but the consequences of the transmitter from the blue cell binding to receptors on the postsynaptic cell is opposite to the consequences of the transmitter released by the green neuron. The consequence of action potential in the red presynaptic neuron is to produce an increase in the membrane potential of the blue postsynaptic neuron.
The membrane potential is more negative than it was before a hyperpolarization and therefore the membrane potential is farther away from threshold. This type of potential is called an inhibitory postsynaptic potential IPSP because it tends to prevent the postsynaptic neuron from firing an action potential. Now what is the postsynaptic neuron to do? Neurons are like adding machines. They are constantly adding up the excitatory and the inhibitory synaptic input in time temporal summation and over the area of the dendrites receiving synaptic contacts spatial summation , and if that summation is at or above threshold they fire an action potential.
If the sum is below threshold, no action potential is initiated. This is a process called synaptic integration and is illustrated in Figure 5. Initially, two action potentials in the green neuron produced summating EPSPs that fired an action potential in the blue neuron.
But, if an IPSP from the inhibitory neuron occurs just before two action potentials in the excitatory neuron, the summation of the one IPSP and the two EPSPs is below threshold and no action potential is elicited in the postsynaptic cell. The inhibitory neuron and inhibition in general is a way of gating or regulating the ability of an excitatory signal to fire a postsynaptic cell. As indicated earlier in the chapter, a neuron can receive contacts from up to 10, presynaptic neurons, and, in turn, any one neuron can contact up to 10, postsynaptic neurons.
The combinatorial possibility could give rise to enormously complex neuronal circuits or network topologies , which might be very difficult to understand. But despite the potential vast complexity, much can be learned about the functioning of neuronal circuits by examining the properties of a subset of simple circuit configurations. Figure 6 illustrates some of those microcircuit or micronetwork motifs.
Although simple, they can do much of what needs to be done by a nervous system. Feedforward excitation. Allows one neuron to relay information to its neighbor. Long chains of these can be used to propagate information through the nervous system.
Feedforward inhibition. A presynaptic cell excites an inhibitory interneuron an interneuron is a neuron interposed between two neurons and that inhibitory interneuron then inhibits the next follower cell.
This is a way of shutting down or limiting excitation in a downstream neuron in a neural circuit. One postsynaptic cell receives convergent input from a number of different presynaptic cells and any individual neuron can make divergent connections to many different postsynaptic cells.
Divergence allows one neuron to communicate with many other neurons in a network. Convergence allows a neuron to receive input from many neurons in a network. Lateral inhibition. A presynaptic cell excites inhibitory interneurons and they inhibit neighboring cells in the network. As described in detail later in the Chapter, this type of circuit can be used in sensory systems to provide edge enhancement. In Panel E1, a presynaptic cell connects to a postsynaptic cell, and the postsynaptic cell in turn connects to an interneuron, which then inhibits the presynaptic cell.
This circuit can limit excitation in a pathway.
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