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Plasticity of cerebellar Purkinje cells in behavioral training of body balance control.

Lee RX, Huang JJ, Huang C, Tsai ML, Yen CT - Front Syst Neurosci (2015)

Bottom Line: The ability to differentiate such sensory information can lead to movement execution with better accuracy.Both PC simple (SSs; 17 of 26) and complex spikes (CSs; 7 of 12) were found to code initially on the angle of the heads with respect to a fixed reference.Using periods with comparable degrees of movement, we found that such SS coding of information in most PCs (10 of 17) decreased rapidly during balance learning.

View Article: PubMed Central - PubMed

Affiliation: Department of Life Science, National Taiwan University Taipei, Taiwan.

ABSTRACT
Neural responses to sensory inputs caused by self-generated movements (reafference) and external passive stimulation (exafference) differ in various brain regions. The ability to differentiate such sensory information can lead to movement execution with better accuracy. However, how sensory responses are adjusted in regard to this distinguishability during motor learning is still poorly understood. The cerebellum has been hypothesized to analyze the functional significance of sensory information during motor learning, and is thought to be a key region of reafference computation in the vestibular system. In this study, we investigated Purkinje cell (PC) spike trains as cerebellar cortical output when rats learned to balance on a suspended dowel. Rats progressively reduced the amplitude of body swing and made fewer foot slips during a 5-min balancing task. Both PC simple (SSs; 17 of 26) and complex spikes (CSs; 7 of 12) were found to code initially on the angle of the heads with respect to a fixed reference. Using periods with comparable degrees of movement, we found that such SS coding of information in most PCs (10 of 17) decreased rapidly during balance learning. In response to unexpected perturbations and under anesthesia, SS coding capability of these PCs recovered. By plotting SS and CS firing frequencies over 15-s time windows in double-logarithmic plots, a negative correlation between SS and CS was found in awake, but not anesthetized, rats. PCs with prominent SS coding attenuation during motor learning showed weaker SS-CS correlation. Hence, we demonstrate that neural plasticity for filtering out sensory reafference from active motion occurs in the cerebellar cortex in rats during balance learning. SS-CS interaction may contribute to this rapid plasticity as a form of receptive field plasticity in the cerebellar cortex between two receptive maps of sensory inputs from the external world and of efference copies from the will center for volitional movements.

No MeSH data available.


Related in: MedlinePlus

Information coding in complex spikes. (A) A histogram of Nframe (number of recorded video frames) as a function of ω (head angular velocity) for a rat. The histogram has a largely symmetrical appearance about zero ω, indicating that the video caught the rat spending most of the time on the dowel with head angular velocity close to zero. (B) Histogram of NCS (number of CSs) as a function of ω for a sample PC of the rat. The histogram also has a largely symmetrical appearance about zero ω, showing that most of the CSs were observed while the head velocity close to zero. (C) To determine whether CS activity of this sample PC may indeed be coding ω, we examined the possibility that the apparent symmetrical patterns about zero ω in (A,B) may contain some subtle differences to indicate that the PC CS activities in fact show systematic differences with the head motion as a function of ω. This was accomplished by the derivation of (C). Here we calculated for the selected PC a value LCS, a representation of the CS discharge level expressed as the ratio of NCS [from (B)] to Nframe [from (A)] in each interval of ω. For this example PC, CS activities show a clear trend for coding ω. (D) Such form of CS coding ω was observed in 7 out of 12 PCs in the present study, with either downward-preferred [upper part in (D)] or upward-preferred [lower part in (D)] pitch movement of the head-body. The sample PC in (A–C) is included in the lower part of (D) as the red line. (E) A fSS-ω plot for the 7 PCs in (D). Each PC was indicated by the same color code as in the two parts of (D). Paired numbers show (r,p) of each PCs. These results suggest that PCs with ω-coding SS may also encoded ω using CS in either a complementary [n = 2 of 7, i.e., the lines colored with wood brown and bright green in (D,E)] or a reciprocal manner (n = 5 of 7, i.e., all other lines).
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Figure 5: Information coding in complex spikes. (A) A histogram of Nframe (number of recorded video frames) as a function of ω (head angular velocity) for a rat. The histogram has a largely symmetrical appearance about zero ω, indicating that the video caught the rat spending most of the time on the dowel with head angular velocity close to zero. (B) Histogram of NCS (number of CSs) as a function of ω for a sample PC of the rat. The histogram also has a largely symmetrical appearance about zero ω, showing that most of the CSs were observed while the head velocity close to zero. (C) To determine whether CS activity of this sample PC may indeed be coding ω, we examined the possibility that the apparent symmetrical patterns about zero ω in (A,B) may contain some subtle differences to indicate that the PC CS activities in fact show systematic differences with the head motion as a function of ω. This was accomplished by the derivation of (C). Here we calculated for the selected PC a value LCS, a representation of the CS discharge level expressed as the ratio of NCS [from (B)] to Nframe [from (A)] in each interval of ω. For this example PC, CS activities show a clear trend for coding ω. (D) Such form of CS coding ω was observed in 7 out of 12 PCs in the present study, with either downward-preferred [upper part in (D)] or upward-preferred [lower part in (D)] pitch movement of the head-body. The sample PC in (A–C) is included in the lower part of (D) as the red line. (E) A fSS-ω plot for the 7 PCs in (D). Each PC was indicated by the same color code as in the two parts of (D). Paired numbers show (r,p) of each PCs. These results suggest that PCs with ω-coding SS may also encoded ω using CS in either a complementary [n = 2 of 7, i.e., the lines colored with wood brown and bright green in (D,E)] or a reciprocal manner (n = 5 of 7, i.e., all other lines).

Mentions: To overcome the problem of low and variable firing frequency of CSs, we took the ratio (LCS) of the CS number (NCS) to recorded frame number (Nframe) in a range of θ, ω, or α as a representation of the level of CS discharge in this interval of θ, ω, or α (Figures 5A–C). To account for the effect of head motion distribution (an example is shown in Figure 5A) on CS distribution (a sample PC recorded from the same rat as Figure 5A is shown in Figure 5B), the number of CSs in each ω interval was divided by the recorded frame numbers of head motion in the same ω interval, i.e., the LCS (Figure 5C). To be specific, in Figure 5A, we made a histogram plotting the number of recorded video frames (y-axis) with head angular velocity ω (x-axis) for the exemplar rat. The bell-shaped distribution peaked around zero ω suggested that the video caught the rat spending most of its time on the dowel with a head angular velocity close to zero. Similarly, Figure 5B shows the same type of histogram plotting the number of complex spike (y-axis) with head angular velocity ω (x-axis) for the sample PC. A similarly bell-shaped distribution to that in Figure 5A indicates, perhaps not surprisingly, a tendency for complex spikes to occur in periods when head angular velocities were close to zero. If CS activities indeed contains information on head angular velocity ω, then there should be subtle but systematic differences between the two bell-shaped histograms in Figures 5A,B. To represent the conditional probability of observing a CS event in each ω interval, we calculated the ratio of frame number to CS number (y-axis, Figure 5C) with respect to each interval of angular velocity for the selected sample PC (x-axis, Figure 5C). This is not only a normalization process accounting for the fact that most of the data will be from cases with ω close to zero, but also has the added advantage to allow us to overcome a common technical problem in the numerical analysis of low and variable firing frequency of CS. Accordingly, we took the ratio (LCS) of the CS number (NCS) to recorded frame number (Nframe) in a range of ω as a representation of the level of CS discharge in this interval of ω (Figure 5C).


Plasticity of cerebellar Purkinje cells in behavioral training of body balance control.

Lee RX, Huang JJ, Huang C, Tsai ML, Yen CT - Front Syst Neurosci (2015)

Information coding in complex spikes. (A) A histogram of Nframe (number of recorded video frames) as a function of ω (head angular velocity) for a rat. The histogram has a largely symmetrical appearance about zero ω, indicating that the video caught the rat spending most of the time on the dowel with head angular velocity close to zero. (B) Histogram of NCS (number of CSs) as a function of ω for a sample PC of the rat. The histogram also has a largely symmetrical appearance about zero ω, showing that most of the CSs were observed while the head velocity close to zero. (C) To determine whether CS activity of this sample PC may indeed be coding ω, we examined the possibility that the apparent symmetrical patterns about zero ω in (A,B) may contain some subtle differences to indicate that the PC CS activities in fact show systematic differences with the head motion as a function of ω. This was accomplished by the derivation of (C). Here we calculated for the selected PC a value LCS, a representation of the CS discharge level expressed as the ratio of NCS [from (B)] to Nframe [from (A)] in each interval of ω. For this example PC, CS activities show a clear trend for coding ω. (D) Such form of CS coding ω was observed in 7 out of 12 PCs in the present study, with either downward-preferred [upper part in (D)] or upward-preferred [lower part in (D)] pitch movement of the head-body. The sample PC in (A–C) is included in the lower part of (D) as the red line. (E) A fSS-ω plot for the 7 PCs in (D). Each PC was indicated by the same color code as in the two parts of (D). Paired numbers show (r,p) of each PCs. These results suggest that PCs with ω-coding SS may also encoded ω using CS in either a complementary [n = 2 of 7, i.e., the lines colored with wood brown and bright green in (D,E)] or a reciprocal manner (n = 5 of 7, i.e., all other lines).
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Figure 5: Information coding in complex spikes. (A) A histogram of Nframe (number of recorded video frames) as a function of ω (head angular velocity) for a rat. The histogram has a largely symmetrical appearance about zero ω, indicating that the video caught the rat spending most of the time on the dowel with head angular velocity close to zero. (B) Histogram of NCS (number of CSs) as a function of ω for a sample PC of the rat. The histogram also has a largely symmetrical appearance about zero ω, showing that most of the CSs were observed while the head velocity close to zero. (C) To determine whether CS activity of this sample PC may indeed be coding ω, we examined the possibility that the apparent symmetrical patterns about zero ω in (A,B) may contain some subtle differences to indicate that the PC CS activities in fact show systematic differences with the head motion as a function of ω. This was accomplished by the derivation of (C). Here we calculated for the selected PC a value LCS, a representation of the CS discharge level expressed as the ratio of NCS [from (B)] to Nframe [from (A)] in each interval of ω. For this example PC, CS activities show a clear trend for coding ω. (D) Such form of CS coding ω was observed in 7 out of 12 PCs in the present study, with either downward-preferred [upper part in (D)] or upward-preferred [lower part in (D)] pitch movement of the head-body. The sample PC in (A–C) is included in the lower part of (D) as the red line. (E) A fSS-ω plot for the 7 PCs in (D). Each PC was indicated by the same color code as in the two parts of (D). Paired numbers show (r,p) of each PCs. These results suggest that PCs with ω-coding SS may also encoded ω using CS in either a complementary [n = 2 of 7, i.e., the lines colored with wood brown and bright green in (D,E)] or a reciprocal manner (n = 5 of 7, i.e., all other lines).
Mentions: To overcome the problem of low and variable firing frequency of CSs, we took the ratio (LCS) of the CS number (NCS) to recorded frame number (Nframe) in a range of θ, ω, or α as a representation of the level of CS discharge in this interval of θ, ω, or α (Figures 5A–C). To account for the effect of head motion distribution (an example is shown in Figure 5A) on CS distribution (a sample PC recorded from the same rat as Figure 5A is shown in Figure 5B), the number of CSs in each ω interval was divided by the recorded frame numbers of head motion in the same ω interval, i.e., the LCS (Figure 5C). To be specific, in Figure 5A, we made a histogram plotting the number of recorded video frames (y-axis) with head angular velocity ω (x-axis) for the exemplar rat. The bell-shaped distribution peaked around zero ω suggested that the video caught the rat spending most of its time on the dowel with a head angular velocity close to zero. Similarly, Figure 5B shows the same type of histogram plotting the number of complex spike (y-axis) with head angular velocity ω (x-axis) for the sample PC. A similarly bell-shaped distribution to that in Figure 5A indicates, perhaps not surprisingly, a tendency for complex spikes to occur in periods when head angular velocities were close to zero. If CS activities indeed contains information on head angular velocity ω, then there should be subtle but systematic differences between the two bell-shaped histograms in Figures 5A,B. To represent the conditional probability of observing a CS event in each ω interval, we calculated the ratio of frame number to CS number (y-axis, Figure 5C) with respect to each interval of angular velocity for the selected sample PC (x-axis, Figure 5C). This is not only a normalization process accounting for the fact that most of the data will be from cases with ω close to zero, but also has the added advantage to allow us to overcome a common technical problem in the numerical analysis of low and variable firing frequency of CS. Accordingly, we took the ratio (LCS) of the CS number (NCS) to recorded frame number (Nframe) in a range of ω as a representation of the level of CS discharge in this interval of ω (Figure 5C).

Bottom Line: The ability to differentiate such sensory information can lead to movement execution with better accuracy.Both PC simple (SSs; 17 of 26) and complex spikes (CSs; 7 of 12) were found to code initially on the angle of the heads with respect to a fixed reference.Using periods with comparable degrees of movement, we found that such SS coding of information in most PCs (10 of 17) decreased rapidly during balance learning.

View Article: PubMed Central - PubMed

Affiliation: Department of Life Science, National Taiwan University Taipei, Taiwan.

ABSTRACT
Neural responses to sensory inputs caused by self-generated movements (reafference) and external passive stimulation (exafference) differ in various brain regions. The ability to differentiate such sensory information can lead to movement execution with better accuracy. However, how sensory responses are adjusted in regard to this distinguishability during motor learning is still poorly understood. The cerebellum has been hypothesized to analyze the functional significance of sensory information during motor learning, and is thought to be a key region of reafference computation in the vestibular system. In this study, we investigated Purkinje cell (PC) spike trains as cerebellar cortical output when rats learned to balance on a suspended dowel. Rats progressively reduced the amplitude of body swing and made fewer foot slips during a 5-min balancing task. Both PC simple (SSs; 17 of 26) and complex spikes (CSs; 7 of 12) were found to code initially on the angle of the heads with respect to a fixed reference. Using periods with comparable degrees of movement, we found that such SS coding of information in most PCs (10 of 17) decreased rapidly during balance learning. In response to unexpected perturbations and under anesthesia, SS coding capability of these PCs recovered. By plotting SS and CS firing frequencies over 15-s time windows in double-logarithmic plots, a negative correlation between SS and CS was found in awake, but not anesthetized, rats. PCs with prominent SS coding attenuation during motor learning showed weaker SS-CS correlation. Hence, we demonstrate that neural plasticity for filtering out sensory reafference from active motion occurs in the cerebellar cortex in rats during balance learning. SS-CS interaction may contribute to this rapid plasticity as a form of receptive field plasticity in the cerebellar cortex between two receptive maps of sensory inputs from the external world and of efference copies from the will center for volitional movements.

No MeSH data available.


Related in: MedlinePlus