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Wednesday, May 22, 2024

Monkey Neuron Activation

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monkey neuron activation

Monkey neuron activation was clinically tested with a set of simple static and dynamic touch stimuli. These clinical tests determined the property and length of the RF for each VPL neuron.

For example, a premotor cortex neuron might discharge strongly at video onset when the monkey prepares to move, and then slowly fade as the movement begins and the hand-object interaction occurs.

Neuron Discharge Patterns

Neurons have different intrinsic membrane properties and discharge patterns that influence their response to synaptic inputs. These responses are often used to identify the source and modality of a sensory stimulus, but can be difficult to interpret when they vary between neurons and even within a single neuron. For example, some neurons exhibit slow bursting patterns while others fire bursts of duplets at high intra-burst frequencies. This variation between neurons could lead to a confusion of input signals.

Fortunately, there are methods for quantifying these differences in the discharge pattern of individual neurons. One of these is to analyze the amplitude and duration of individual spikes within a burst. Another is to examine the autocorrelogram of the neuron, which shows the frequency of the spikes that occur in a given time interval. The combination of these two techniques can give a clear picture of the underlying activity that is producing the neuron’s bursts.

To assess the contribution of these parameters to fMRI signal fluctuations, we recorded the activity of VLPF neurons in monkeys as they watched various videos on a monitor. In some cases, VLPF neurons were highly selective and only responded to a specific video. In other cases, VLPF neurons were more general and showed a response to more than one video (Fig. 2). The most common VLPF neurons displayed the strongest response during the observation of a monkey grasping a piece of food, either from first or third person perspective. The next most common was a human actor moving his arm in front of himself or mimicking a grasping action. For these types of videos, the highest peak of activity occurred before the movement onset and was sustained although decreasing until the end of the reaching motion.

We also observed that some VLPF neurons showed a rhythmic firing pattern during the whole video presentation. This pattern was correlated with changes in blood pressure and heart rate, suggesting that it is related to baroreceptor activation. The monkey was preconditioned for fMRI scanning sessions with an apparatus that simulated the MR environment, including a “mock” gradient and radiofrequency coil as well as a plexiglass cylinder infrastructure through which the magnetic field passed. This allowed us to record the MR signal at sound intensity levels that gradually, over 20 daily sessions (5 per week), came to approximate those of the actual scanning session.

Neuron Responses to Stimuli

Neurons in the ventral premotor cortex (VLPF) of monkeys are specialized to respond to the observation of biological movements or object motion. This class of neurons, called mirror neurons, is also known as the “action recognition” system, and a number of empirical studies have shown that VLPF neurons are highly selective for specific video sequences. For example, some neurons are only activated by videos showing the monkey grasping a piece of food from a third person perspective, while others only respond to the observation of a human actor performing an action.

Neuron responses to stimuli are characterized by two phases: a baseline period, and then a plateau phase of constant firing rate (see Fig. 1). The durations of the baseline and plateau periods depend on the stimulus type and intensity. During the baseline period, the stimulus is presented on a blank gray background, while during the plateau phase the stimulus is displayed on a circular screen in front of the monkey’s eyes.

To elicit the VLPF response, the experimenter positioned the monkey’s hand contralateral to the recorded hemisphere of the brain and asked the monkey to observe a set of different videos, as shown in Fig. 1. The videos consisted of a monkey either holding or not holding a piece of food, and the video sequences were repeated in random order. The durations of the stimulus were 1000 ms, 500 ms, and 1200 ms for the three different ramp conditions used for the trials.

After the baseline and plateau period, a visual fixation point changed color to green or blue, and the monkey was required to move a two-way mechanical lever in the direction of the corresponding lever response in order to receive a reward. Multidimensional scaling analysis showed that the pulvinar neurons encoded face-like patterns for the first 50 ms after stimulus onset, and then classified the stimulus into one of five different categories for the next 50 ms.

The c-fos protein is expressed in many differentiated neurons after stimulation, indicating that the differentiated neurons responded to sound. The differentiation of these neurons is consistent with the hypothesis that the pulvinar acts as a filter for ascending inputs from cortical areas.

Neuron Responses to Context

A number of monkey neurons have been shown to respond to the occurrence of primary rewards in specific behavioral contexts. For example, tonic striatal neurons exhibited responses that correlated with the timing of reward delivery and showed a strong preference for reward that was delivered less than 1 s after the visual cue. These responses were influenced by both the amount of reward and whether it was delivered alone or with an animal partner, indicating that these neurons encode the timing and value of rewards in general, rather than just the specific stimuli in each case.

Single orbitofrontal cortex (OFC) neurons have also been found to be sensitive to the contextual meaning of visual stimuli. In one study, monkeys were trained to perform a visual discrimination task where they could earn rewards for themselves or for two passive monkey partners that were physically present in the testing room. The task consisted of blocks where only the monkey earned a reward, and a block in which rewards were awarded to both monkeys. Single OFC neurons responded differently to phee calls in each of these contexts, and some even exhibited different response properties when responding to a wider repertoire of vocalizations and noise stimuli.

In a separate study, the discharge of VLPF neurons was evaluated in relation to their response to different videos that demonstrated various grasping movements performed by the monkey from first and third person perspective. VLPF neurons were classified as highly selective (HS) if they responded exclusively to one video and as non-selective if they responded to multiple videos. Neurons that exhibited a HS response showed a larger increase in their mean firing rate during the preferred video than during all other conditions.

Moreover, a large proportion of these neurons exhibited specificity in the phase of the preferred video that elicited their greatest discharge, as well as in the phase in which they responded to occluding parts of the video. For example, neurons that were selective for video Epoch 2 exhibited greater responses during this phase than during other phases of the same video. This suggests that these neurons code the overall action being performed by the monkey.

Neuron Responses to Objects

We tested monkey neuron responses to visually identical stimulus pairs, in which the same object reappeared either as expected (expected emergence) or unexpectedly following occlusion (unexpected emergence). For each pair of trials, the monkey performed one grouping task and one discrimination task. Each condition included 100 trials. The monkey’s behavioral response was a lever press, and we recorded the firing rate of inferotemporal neurons over time to this signal.

We first sorted neural responses by the behavior elicited and normalized them. This yielded two indices: the selectivity index and the peak of the response maximum. Neurons with high values of these indices were selected as good detectors of the stimuli. Then, we compared the performance of monkey and neuron at near-threshold stimulus values in Easy and Difficult conditions (see Fig. 3). We also compared the neuronal responses to stimulus-absent (FA) and stimulus-present (CR) trials.

On average, modulations in the firing rate of VPL neurons within a 50-ms window accounted for detection performance. We chose this window size to capture modulations elicited by sinusoidal pulses without introducing periodicity effects. Thus, our results suggest that a simple code of stimulus amplitude based on the amplitude of a single sinusoidal pulse explains detection performance, and does not require the presence of periodic signals in spike trains.

We then compared psychometric and neurometric responses using this firing rate code, as shown in Fig. 4. There was no significant main effect of condition in the curves of original magnitudes or in the slopes of the curves normalized to the maximum responses (sp = 0.060 vs. sn = 0.030; P 0.001). Thus, this simple firing rate code was able to provide a reasonable estimate of the stimulus amplitude required for threshold detection in both Easy and Difficult conditions. The sharper the selectivity index, the better the neuron was able to distinguish between the stimuli presented in the Easy and Difficult conditions. This result is consistent with the notion that selectivity is largely determined by the amount of time a stimulus remains present during the integration window. This is an important finding because it indicates that the neuron can encode the duration of a stimulus, and it may therefore be possible to use this information to determine whether a stimulus is hidden.

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