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Activities of visual cortical and hippocampal neurons co-fluctuate in freely moving rats during spatial behavior.

Haggerty DC, Ji D - Elife (2015)

Bottom Line: The precise activities of individual V1 neurons fluctuate every time the animal travels through the track, in a correlated fashion with those of hippocampal place cells firing at overlapping locations.The results suggest the existence of visual cortical neurons that are functionally coupled with hippocampal place cells for spatial processing during natural behavior.These visual neurons may also participate in the formation and storage of hippocampal-dependent memories.

View Article: PubMed Central - PubMed

Affiliation: Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, United States.

ABSTRACT
Visual cues exert a powerful control over hippocampal place cell activities that encode external spaces. The functional interaction of visual cortical neurons and hippocampal place cells during spatial navigation behavior has yet to be elucidated. Here we show that, like hippocampal place cells, many neurons in the primary visual cortex (V1) of freely moving rats selectively fire at specific locations as animals run repeatedly on a track. The V1 location-specific activity leads hippocampal place cell activity both spatially and temporally. The precise activities of individual V1 neurons fluctuate every time the animal travels through the track, in a correlated fashion with those of hippocampal place cells firing at overlapping locations. The results suggest the existence of visual cortical neurons that are functionally coupled with hippocampal place cells for spatial processing during natural behavior. These visual neurons may also participate in the formation and storage of hippocampal-dependent memories.

No MeSH data available.


Related in: MedlinePlus

Illustration of computing spatial modulation indices (SMIs) for two example cells with different firing rates.(A) Lap by lap spike raster and spike rate curves of the two cells averaged across all laps. The plots are arranged the same way as in Figure 2A–D. Spatial information content (SIc) and spatial information rate (SIr) computed from the rate curves are shown. Note that the higher-rate Cell 2 appeared more spatially modulated than Cell 1, but had a lower SIc. (B) An example of shuffled firing rate curve for each of the two cells, generated via circularly shifting the spike trains in A by a random time interval for every lap. Note the comparable peaks between the actual and the shuffled rate curves in the low-rate Cell 1, but not in Cell 2. The SIc and SIr computed from the shuffled rate curves were shown. (C) Histograms of SIc and SIr values computed from 100 randomly shuffled firing rate curves for the two cells. The mean and standard deviation (std) of each histogram are shown. Red arrows mark the actual values. SMI is computed as (actual value−mean)/std. It can be seen that the shuffle-generated values differed between SIc and SIr for the same cell and between the two cells, even though the shuffled spike trains by definition contained no spatial information. The shuffled spikes of the low-rate Cell 1 yielded a higher SIc, but a lower SIr, than the shuffled spikes of the high-rate Cell 2. The examples illustrate the rate–dependence of SIc and SIr. Second, by normalizing the actual SIc/SIr values relative to their shuffle-generated values, SMIs computed from SIc and SIr become equivalent.DOI:http://dx.doi.org/10.7554/eLife.08902.005
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fig2s1: Illustration of computing spatial modulation indices (SMIs) for two example cells with different firing rates.(A) Lap by lap spike raster and spike rate curves of the two cells averaged across all laps. The plots are arranged the same way as in Figure 2A–D. Spatial information content (SIc) and spatial information rate (SIr) computed from the rate curves are shown. Note that the higher-rate Cell 2 appeared more spatially modulated than Cell 1, but had a lower SIc. (B) An example of shuffled firing rate curve for each of the two cells, generated via circularly shifting the spike trains in A by a random time interval for every lap. Note the comparable peaks between the actual and the shuffled rate curves in the low-rate Cell 1, but not in Cell 2. The SIc and SIr computed from the shuffled rate curves were shown. (C) Histograms of SIc and SIr values computed from 100 randomly shuffled firing rate curves for the two cells. The mean and standard deviation (std) of each histogram are shown. Red arrows mark the actual values. SMI is computed as (actual value−mean)/std. It can be seen that the shuffle-generated values differed between SIc and SIr for the same cell and between the two cells, even though the shuffled spike trains by definition contained no spatial information. The shuffled spikes of the low-rate Cell 1 yielded a higher SIc, but a lower SIr, than the shuffled spikes of the high-rate Cell 2. The examples illustrate the rate–dependence of SIc and SIr. Second, by normalizing the actual SIc/SIr values relative to their shuffle-generated values, SMIs computed from SIc and SIr become equivalent.DOI:http://dx.doi.org/10.7554/eLife.08902.005

Mentions: We quantified the location-specificity of each individual V1 and CA1 cell active on a trajectory. To compare with the location-specificity arising randomly from chance, we randomly shuffled the cell's spiking activity by circularly shifting the spikes within each lap of the trajectory with a random time interval (Henriksen et al., 2010; Igarashi et al., 2014). First, we computed spatial information content (SIc), a measure of how much information (in bits per spike) a cell's spiking activity contained about the animal's location (Skaggs et al., 1993). Although the median SIc value of V1 cells (0.17 [0.085 0.34] bits/spike, N = 1501 cell × trajectories) was relatively small, compared with that of CA1 place cells (1.6 [1.1 2.2] bits/spike, N = 2909; p < 0.0001, ranksum test), it was significantly greater than that of the shuffled V1 cells (0.061 [0.025 0.13] bits/spike; p < 0.0001; Figure 2E). Second, we computed spatial information rate (SIr), which measures spatial information in bits per second. Similarly to SIc, the median SIr of V1 cells (0.70 [0.42 1.2] bits/s) was smaller than that of CA1 cells (2.4 [1.2 4.2] bits/s; p < 0.0001), but significantly greater than that of the shuffled V1 cells (0.22 [0.14 0.37] bits/spike; p < 0.0001; Figure 2F). Third, using a method modified from previous studies (Henriksen et al., 2010; Igarashi et al., 2014), we derived a normalized spatial modulation index (SMI). The reason for this additional measure was that SIc and SIr are affected by firing rate (Figure 2—figure supplement 1). Since V1 and CA1 cells had different firing rates, the SIc and SIr values between V1 and CA1 cells were not directly comparable. SMI was defined as the SIc (or equivalently SIr) of a cell relative to its chance-level distribution produced by the random shuffling (Figure 2—figure supplement 1). SMI does not directly quantify the location-specificity of a cell's firing activity, but provides a measure of the degree of location modulation relative to random spike trains with identical firing rate and temporal spiking patterns. SMI is therefore insensitive to firing rate. The chance-level of SMI for any given cell is zero. The median SMIs of both V1 (8.1 [3.4 15.4]) and CA1 cells (12.0 [3.3 23.2]) were much higher than zero (p < 0.0001, ranksum test; Figure 2G). Finally, we defined a cell with SMI >2.325 (99th percentile of the chance-level) as a ‘location-responsive’ V1 cell. We found that 81% of trajectory-active V1 cells were location-responsive on a trajectory and for comparison, 90% of the trajectory-active CA1 cells were location-responsive.


Activities of visual cortical and hippocampal neurons co-fluctuate in freely moving rats during spatial behavior.

Haggerty DC, Ji D - Elife (2015)

Illustration of computing spatial modulation indices (SMIs) for two example cells with different firing rates.(A) Lap by lap spike raster and spike rate curves of the two cells averaged across all laps. The plots are arranged the same way as in Figure 2A–D. Spatial information content (SIc) and spatial information rate (SIr) computed from the rate curves are shown. Note that the higher-rate Cell 2 appeared more spatially modulated than Cell 1, but had a lower SIc. (B) An example of shuffled firing rate curve for each of the two cells, generated via circularly shifting the spike trains in A by a random time interval for every lap. Note the comparable peaks between the actual and the shuffled rate curves in the low-rate Cell 1, but not in Cell 2. The SIc and SIr computed from the shuffled rate curves were shown. (C) Histograms of SIc and SIr values computed from 100 randomly shuffled firing rate curves for the two cells. The mean and standard deviation (std) of each histogram are shown. Red arrows mark the actual values. SMI is computed as (actual value−mean)/std. It can be seen that the shuffle-generated values differed between SIc and SIr for the same cell and between the two cells, even though the shuffled spike trains by definition contained no spatial information. The shuffled spikes of the low-rate Cell 1 yielded a higher SIc, but a lower SIr, than the shuffled spikes of the high-rate Cell 2. The examples illustrate the rate–dependence of SIc and SIr. Second, by normalizing the actual SIc/SIr values relative to their shuffle-generated values, SMIs computed from SIc and SIr become equivalent.DOI:http://dx.doi.org/10.7554/eLife.08902.005
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fig2s1: Illustration of computing spatial modulation indices (SMIs) for two example cells with different firing rates.(A) Lap by lap spike raster and spike rate curves of the two cells averaged across all laps. The plots are arranged the same way as in Figure 2A–D. Spatial information content (SIc) and spatial information rate (SIr) computed from the rate curves are shown. Note that the higher-rate Cell 2 appeared more spatially modulated than Cell 1, but had a lower SIc. (B) An example of shuffled firing rate curve for each of the two cells, generated via circularly shifting the spike trains in A by a random time interval for every lap. Note the comparable peaks between the actual and the shuffled rate curves in the low-rate Cell 1, but not in Cell 2. The SIc and SIr computed from the shuffled rate curves were shown. (C) Histograms of SIc and SIr values computed from 100 randomly shuffled firing rate curves for the two cells. The mean and standard deviation (std) of each histogram are shown. Red arrows mark the actual values. SMI is computed as (actual value−mean)/std. It can be seen that the shuffle-generated values differed between SIc and SIr for the same cell and between the two cells, even though the shuffled spike trains by definition contained no spatial information. The shuffled spikes of the low-rate Cell 1 yielded a higher SIc, but a lower SIr, than the shuffled spikes of the high-rate Cell 2. The examples illustrate the rate–dependence of SIc and SIr. Second, by normalizing the actual SIc/SIr values relative to their shuffle-generated values, SMIs computed from SIc and SIr become equivalent.DOI:http://dx.doi.org/10.7554/eLife.08902.005
Mentions: We quantified the location-specificity of each individual V1 and CA1 cell active on a trajectory. To compare with the location-specificity arising randomly from chance, we randomly shuffled the cell's spiking activity by circularly shifting the spikes within each lap of the trajectory with a random time interval (Henriksen et al., 2010; Igarashi et al., 2014). First, we computed spatial information content (SIc), a measure of how much information (in bits per spike) a cell's spiking activity contained about the animal's location (Skaggs et al., 1993). Although the median SIc value of V1 cells (0.17 [0.085 0.34] bits/spike, N = 1501 cell × trajectories) was relatively small, compared with that of CA1 place cells (1.6 [1.1 2.2] bits/spike, N = 2909; p < 0.0001, ranksum test), it was significantly greater than that of the shuffled V1 cells (0.061 [0.025 0.13] bits/spike; p < 0.0001; Figure 2E). Second, we computed spatial information rate (SIr), which measures spatial information in bits per second. Similarly to SIc, the median SIr of V1 cells (0.70 [0.42 1.2] bits/s) was smaller than that of CA1 cells (2.4 [1.2 4.2] bits/s; p < 0.0001), but significantly greater than that of the shuffled V1 cells (0.22 [0.14 0.37] bits/spike; p < 0.0001; Figure 2F). Third, using a method modified from previous studies (Henriksen et al., 2010; Igarashi et al., 2014), we derived a normalized spatial modulation index (SMI). The reason for this additional measure was that SIc and SIr are affected by firing rate (Figure 2—figure supplement 1). Since V1 and CA1 cells had different firing rates, the SIc and SIr values between V1 and CA1 cells were not directly comparable. SMI was defined as the SIc (or equivalently SIr) of a cell relative to its chance-level distribution produced by the random shuffling (Figure 2—figure supplement 1). SMI does not directly quantify the location-specificity of a cell's firing activity, but provides a measure of the degree of location modulation relative to random spike trains with identical firing rate and temporal spiking patterns. SMI is therefore insensitive to firing rate. The chance-level of SMI for any given cell is zero. The median SMIs of both V1 (8.1 [3.4 15.4]) and CA1 cells (12.0 [3.3 23.2]) were much higher than zero (p < 0.0001, ranksum test; Figure 2G). Finally, we defined a cell with SMI >2.325 (99th percentile of the chance-level) as a ‘location-responsive’ V1 cell. We found that 81% of trajectory-active V1 cells were location-responsive on a trajectory and for comparison, 90% of the trajectory-active CA1 cells were location-responsive.

Bottom Line: The precise activities of individual V1 neurons fluctuate every time the animal travels through the track, in a correlated fashion with those of hippocampal place cells firing at overlapping locations.The results suggest the existence of visual cortical neurons that are functionally coupled with hippocampal place cells for spatial processing during natural behavior.These visual neurons may also participate in the formation and storage of hippocampal-dependent memories.

View Article: PubMed Central - PubMed

Affiliation: Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, United States.

ABSTRACT
Visual cues exert a powerful control over hippocampal place cell activities that encode external spaces. The functional interaction of visual cortical neurons and hippocampal place cells during spatial navigation behavior has yet to be elucidated. Here we show that, like hippocampal place cells, many neurons in the primary visual cortex (V1) of freely moving rats selectively fire at specific locations as animals run repeatedly on a track. The V1 location-specific activity leads hippocampal place cell activity both spatially and temporally. The precise activities of individual V1 neurons fluctuate every time the animal travels through the track, in a correlated fashion with those of hippocampal place cells firing at overlapping locations. The results suggest the existence of visual cortical neurons that are functionally coupled with hippocampal place cells for spatial processing during natural behavior. These visual neurons may also participate in the formation and storage of hippocampal-dependent memories.

No MeSH data available.


Related in: MedlinePlus