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How reduction of theta rhythm by medial septum inactivation may covary with disruption of entorhinal grid cell responses due to reduced cholinergic transmission.

Pilly PK, Grossberg S - Front Neural Circuits (2013)

Bottom Line: Two recent studies reduced the theta rhythm by inactivating the medial septum (MS) and demonstrated a correlated reduction in the characteristic hexagonal spatial firing patterns of grid cells.In particular, the adverse effects of MS inactivation on grid cells can be understood in terms of how the concomitant reduction in cholinergic inputs may increase the conductances of leak potassium (K(+)) and slow and medium after-hyperpolarization (sAHP and mAHP) channels.These results demonstrate how models of grid cell self-organization can provide new insights into the relationship between brain learning and oscillatory dynamics.

View Article: PubMed Central - PubMed

Affiliation: Center for Neural and Emergent Systems, Information and Systems Sciences Laboratory, HRL Laboratories Malibu, CA, USA.

ABSTRACT
Oscillations in the coordinated firing of brain neurons have been proposed to play important roles in perception, cognition, attention, learning, navigation, and sensory-motor control. The network theta rhythm has been associated with properties of spatial navigation, as has the firing of entorhinal grid cells and hippocampal place cells. Two recent studies reduced the theta rhythm by inactivating the medial septum (MS) and demonstrated a correlated reduction in the characteristic hexagonal spatial firing patterns of grid cells. These results, along with properties of intrinsic membrane potential oscillations (MPOs) in slice preparations of medial entorhinal cortex (MEC), have been interpreted to support oscillatory interference models of grid cell firing. The current article shows that an alternative self-organizing map (SOM) model of grid cells can explain these data about intrinsic and network oscillations without invoking oscillatory interference. In particular, the adverse effects of MS inactivation on grid cells can be understood in terms of how the concomitant reduction in cholinergic inputs may increase the conductances of leak potassium (K(+)) and slow and medium after-hyperpolarization (sAHP and mAHP) channels. This alternative model can also explain data that are problematic for oscillatory interference models, including how knockout of the HCN1 gene in mice, which flattens the dorsoventral gradient in MPO frequency and resonance frequency, does not affect the development of the grid cell dorsoventral gradient of spatial scales, and how hexagonal grid firing fields in bats can occur even in the absence of theta band modulation. These results demonstrate how models of grid cell self-organization can provide new insights into the relationship between brain learning and oscillatory dynamics.

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Spatial responses of a model grid cell with the larger scale before, during, and after MS inactivation (Case 2). The rows from top to bottom correspond to three consecutive trials (20th—22nd), with the middle row (21st) being the one in which MS is inactivated. The four columns from left to right show the spatial rate map, its autocorrelogram, weight strengths of connections from stripe cells of the smaller scale (s1 = 20 cm), and weight strengths of connections from stripe cells of the larger scale (s2 = 35 cm), respectively, at the end of the trial. Note the mean (m) and peak (p) firing rates, and the gridness score (g) on the top of each rate map and autocorrelogram, respectively. Color coding from blue (min.) to red (max.) is used for each rate map, and from blue (−1) to red (1) for each autocorrelogram.
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Figure 6: Spatial responses of a model grid cell with the larger scale before, during, and after MS inactivation (Case 2). The rows from top to bottom correspond to three consecutive trials (20th—22nd), with the middle row (21st) being the one in which MS is inactivated. The four columns from left to right show the spatial rate map, its autocorrelogram, weight strengths of connections from stripe cells of the smaller scale (s1 = 20 cm), and weight strengths of connections from stripe cells of the larger scale (s2 = 35 cm), respectively, at the end of the trial. Note the mean (m) and peak (p) firing rates, and the gridness score (g) on the top of each rate map and autocorrelogram, respectively. Color coding from blue (min.) to red (max.) is used for each rate map, and from blue (−1) to red (1) for each autocorrelogram.

Mentions: Figures 5, 6 provide illustrative spatial responses and input synaptic weights of two model grid cells, one each from the two simulated entorhinal SOMs, through the experimental paradigm. Note in either case the distribution of learned connections from input stripe cells, grouped by spatial scale and preferred direction, before MS is inactivated reveals the spatial scale of the hexagonal grid firing field structure that is being encoded. For instance, in the first row of Figure 6, the three stripe cells with the maximal learned weights to the pertinent grid cell share the same larger spacing (namely, s2 = 35 cm) and have preferred directions of −40°, 20°, and 80°, which are all 60° apart. The erosion of these weights during the period of reduced integration rates occurs with cell firing in spatial positions that do not conform to the encoded grid exemplar (cf. activity-dependent plasticity in Equation 1.6).


How reduction of theta rhythm by medial septum inactivation may covary with disruption of entorhinal grid cell responses due to reduced cholinergic transmission.

Pilly PK, Grossberg S - Front Neural Circuits (2013)

Spatial responses of a model grid cell with the larger scale before, during, and after MS inactivation (Case 2). The rows from top to bottom correspond to three consecutive trials (20th—22nd), with the middle row (21st) being the one in which MS is inactivated. The four columns from left to right show the spatial rate map, its autocorrelogram, weight strengths of connections from stripe cells of the smaller scale (s1 = 20 cm), and weight strengths of connections from stripe cells of the larger scale (s2 = 35 cm), respectively, at the end of the trial. Note the mean (m) and peak (p) firing rates, and the gridness score (g) on the top of each rate map and autocorrelogram, respectively. Color coding from blue (min.) to red (max.) is used for each rate map, and from blue (−1) to red (1) for each autocorrelogram.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3814006&req=5

Figure 6: Spatial responses of a model grid cell with the larger scale before, during, and after MS inactivation (Case 2). The rows from top to bottom correspond to three consecutive trials (20th—22nd), with the middle row (21st) being the one in which MS is inactivated. The four columns from left to right show the spatial rate map, its autocorrelogram, weight strengths of connections from stripe cells of the smaller scale (s1 = 20 cm), and weight strengths of connections from stripe cells of the larger scale (s2 = 35 cm), respectively, at the end of the trial. Note the mean (m) and peak (p) firing rates, and the gridness score (g) on the top of each rate map and autocorrelogram, respectively. Color coding from blue (min.) to red (max.) is used for each rate map, and from blue (−1) to red (1) for each autocorrelogram.
Mentions: Figures 5, 6 provide illustrative spatial responses and input synaptic weights of two model grid cells, one each from the two simulated entorhinal SOMs, through the experimental paradigm. Note in either case the distribution of learned connections from input stripe cells, grouped by spatial scale and preferred direction, before MS is inactivated reveals the spatial scale of the hexagonal grid firing field structure that is being encoded. For instance, in the first row of Figure 6, the three stripe cells with the maximal learned weights to the pertinent grid cell share the same larger spacing (namely, s2 = 35 cm) and have preferred directions of −40°, 20°, and 80°, which are all 60° apart. The erosion of these weights during the period of reduced integration rates occurs with cell firing in spatial positions that do not conform to the encoded grid exemplar (cf. activity-dependent plasticity in Equation 1.6).

Bottom Line: Two recent studies reduced the theta rhythm by inactivating the medial septum (MS) and demonstrated a correlated reduction in the characteristic hexagonal spatial firing patterns of grid cells.In particular, the adverse effects of MS inactivation on grid cells can be understood in terms of how the concomitant reduction in cholinergic inputs may increase the conductances of leak potassium (K(+)) and slow and medium after-hyperpolarization (sAHP and mAHP) channels.These results demonstrate how models of grid cell self-organization can provide new insights into the relationship between brain learning and oscillatory dynamics.

View Article: PubMed Central - PubMed

Affiliation: Center for Neural and Emergent Systems, Information and Systems Sciences Laboratory, HRL Laboratories Malibu, CA, USA.

ABSTRACT
Oscillations in the coordinated firing of brain neurons have been proposed to play important roles in perception, cognition, attention, learning, navigation, and sensory-motor control. The network theta rhythm has been associated with properties of spatial navigation, as has the firing of entorhinal grid cells and hippocampal place cells. Two recent studies reduced the theta rhythm by inactivating the medial septum (MS) and demonstrated a correlated reduction in the characteristic hexagonal spatial firing patterns of grid cells. These results, along with properties of intrinsic membrane potential oscillations (MPOs) in slice preparations of medial entorhinal cortex (MEC), have been interpreted to support oscillatory interference models of grid cell firing. The current article shows that an alternative self-organizing map (SOM) model of grid cells can explain these data about intrinsic and network oscillations without invoking oscillatory interference. In particular, the adverse effects of MS inactivation on grid cells can be understood in terms of how the concomitant reduction in cholinergic inputs may increase the conductances of leak potassium (K(+)) and slow and medium after-hyperpolarization (sAHP and mAHP) channels. This alternative model can also explain data that are problematic for oscillatory interference models, including how knockout of the HCN1 gene in mice, which flattens the dorsoventral gradient in MPO frequency and resonance frequency, does not affect the development of the grid cell dorsoventral gradient of spatial scales, and how hexagonal grid firing fields in bats can occur even in the absence of theta band modulation. These results demonstrate how models of grid cell self-organization can provide new insights into the relationship between brain learning and oscillatory dynamics.

Show MeSH
Related in: MedlinePlus