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Thursday, May 23, 2024

Mechanisms Involved in Monkey Neuron Activation

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monkey neuron activation

In this article, we’ll take a look at some of the mechanisms involved in monkey neuron activation. We’ll also take a look at the ways in which the animal prepares for behavioral responses to others’ actions. Additionally, we’ll discuss the variability of these processes depending on how cognitively challenging the tasks are.

Action-Parsing

During grasping actions, monkeys can interpret the actions of others. This ability may be due to the mirror system, which reflects other people’s actions. They can use this information to plan their own behavior. However, little is known about the neural mechanisms that enable such observation and coordination. A few studies have provided insight into the role of the motor system during such action-parsing.

A large cortical network integrates both visual and somatomotor information. Previous studies have shown that neurons in this network code biological stimuli. Several other studies have investigated the role of the mirror mechanism in joint action.

In this study, a set of 36 neurons responded to actions by a monkey. The responses varied according to the type of video stimulus. Each of the videos was designed to either present the presence or absence of an object. Usually, these were presented in the central part of the screen.

An analysis of the resulting neural activity revealed that the most frequent coded stimulus was a monkey grasping an object. This was followed by responses to other behaviors, such as vocalizations, expressive faces, and static faces. Only one neuron, however, responded to the human mimicking movement.

There were also a few neurons that responded to more than one type of video. One neuron, in particular, responded to both human mimicking and grasping. Using a home-made Labview software, the researchers conducted an experiment to assess the function of these neurons.

Two female Rhesus monkeys were used for this study. The animals weighed about 4 kg. They were handled in accordance with European guidelines. Animal care was approved by the University of Parma Animal Use Committee.

Preparing for behavioral responses to others’ actions

To study the neuronal mechanisms that enable us to predict others’ actions, researchers studied the activity of a group of monkey neurons involved in various stages of the behavioral program. Their findings revealed that certain neurons fire at specific moments in the animal’s behavior.

For example, when the monkey was shown to grasp an object, some of the cells in the premotor area, PFG and F5, responded to the action. However, the same cells failed to respond when the object was moved. The presence of this neuron in other areas, however, suggests that the same motor representation is recruited for the planning of the next move.

Similarly, the mirror neuron, located in the premotor cortex, fired to the monkey’s view of the person performing the same act. While this is a good way to learn about another’s actions, it has been suggested that mirror neurons do not perform the same action.

In the premotor cortex, a number of somato-motor and visual information are encoded into the brain’s neurological wiring. This includes the location of the effector in the action and the goal of the upcoming movement.

Using a peripersonal space, the authors of this study were able to train a monkey to produce a certain movement in response to a visual signal. They also recorded the resulting neural activity.

In the central region of the putamen, striatal neurons showed impulsive activity. This was coupled with TTL signals that were fed to a recording system for statistical analysis. A LabView-based software package was used to synchronize the triggering of the aforementioned signals and control the delivery of reward cues.

In addition, researchers found that a subset of neurons fired selectively when the animal was preparing to move to the left. Although this was not a definitive answer to the question of how monkeys prepare for the next move, it did demonstrate that predictive activation was stronger during inaction conditions than during overt action observation.

Mechanisms of monkey neuron activation

When monkeys are trained to move a particular arm to a certain position, their cell activity varies before and after they make the movement. The firing patterns of some neurons correlate with the speed and distance of the movement. Others fire selectively as the animal prepares to move to a different direction.

During untrained movements, stimulation can still excite efferent axons. However, the activation of the cell body may not be enough to activate the nucleus, due to strong GABAergic inhibition. This leads to abnormal firings of the target nucleus.

In a behavioral paradigm, monkeys were trained to make a movement in response to a visual signal. Each condition involved four sessions. For example, when a hand moves an object, the velocity increases gradually and reaches its peak halfway to the target. After 500 msec of stimulation, the monkeys start to move their arms to the front of their eyes. Afterward, they stop moving until they receive the Move signal.

Neurons in the premotor cortex were activated bilaterally during the correct actions trials. They do not burn when the monkey is actually moving, but they fire in the interval between the Prepare instruction and the Move instruction. These findings suggest that the premotor cortex plays a role in determining which motor plans to use for voluntary movements.

The same monkeys were also trained to delay their movement until the Move signal was received. Despite the delay, the cell activity of neurons in the premotor cortex was still significantly increased. Similarly, the premotor cortex was activated during the movement error trials.

Although these studies provide valuable insights into how monkeys activate their neuronal systems, they are limited in their ability to directly replicate the behavior of humans. Further research is needed to understand how self-related information is encoded in the brain.

Increased activity during action observation

Action observation is a dynamic process of observing and imitating an action. It is a process that involves both immediate and long-term effects. Studies have shown that action observation helps in the development of physical skills. In addition, passive observation of movement facilitates motor memory formation and motor execution.

During action observation, the premotor cortex is activated. The activation is due to the expectation of upcoming action. This activity is similar to activation prior to the go/no-go signal. These results are consistent with previous studies on the anticipation effect.

A subsample of 33 infants (mean age, 20 months) was analyzed during later imitation of actions. EEG analysis was performed on the same electrodes as during action observation. Activity was sorted using principal component matching techniques. Raw signals were high-pass filtered at 300 Hz. Each subject was given a single-unit action potential, which was then sorted. Spectral power values were recorded during the whole time window. Table 1 shows mean power (mV2) and standard errors for each electrode.

The central 3-6 Hz range of neural activity was decreased, as was the 7-10 Hz range. Rebound suppression was reduced in both age groups. Object manipulation also decreased rebound.

Despite these decreases, the majority of MNs showed a predictive activation pattern. Interestingly, the predictive activation was more pronounced during overt action observation. Researchers suggest that this phenomenon may index the process of integrating other’s actions into their own.

Several imaging studies have shown that mirror neuron activity is active in the immediate period prior to predictable actions. However, fewer studies have examined the link between activity during action observation and subsequent physical performance.

The present study investigated how rate of self-paced index finger pressing is modified by passive observation of a similar action. Participants performed a series of button-pressing tasks. They did not know that they would be doing the same task twice.

Variability based on cognitive difficulty

This study investigated the effects of spatial attention on neural variability. It showed that by increasing firing rates, spatial attention can reduce correlated variability across pairs of neurons in the local populations of visual cortex neurons. The authors propose that reducing correlated neural variability is important for the behavioral benefits of attention.

Aside from signal enhancement, task demands can also affect cortical neural variability. Moreover, the reduction of correlated neural variability improves the neural signal-to-noise ratio.

The best way to understand this process is to measure the neural signal-to-noise relationship. A good example is the difference between the signal-to-noise ratio of a high- and a low-variability neuron.

To achieve this, the authors performed single-unit recordings in the vestibular nuclei of macaque monkeys. Several classes of neurons were identified. They included mirror neurons, anti-mirror neurons, and neurons involved in self-motion perception. Each class of neurons is functionally divided into high-variability (HV) and low-variability (LV) neurons.

The spectral power for a high-variability neuron was notably higher than that of a low-variability neuron. However, the difference between the spectral power of a high- and a low-variability stimulus was not as large as the difference between the spike train power spectrums of more irregular neurons.

While the spectral power of a high-variability stimulus is significant, the significance of a low-variability stimulus can be derived from its ability to efficiently encode a detailed time course of self-motion stimuli. For instance, a low-variability neuron’s spike train power spectrum is similar to the resting discharge in the absence of a stimulus.

Using this information, the authors reconstructed the reconstructed stimulus and tested its ability to match the signal of a low-variability neuron. Interestingly, the EH neuron and the VO neuron were able to reconstruct the same reconstructed stimulus, but the reconstructed stimulus was better matched for the EH neuron.

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