Figures and data in Paradoxical response reversal of top-down modulation in cortical circuits with three interneuron types | eLife

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Version of Record: March 13, 2020
Version of Record: August 2, 2018
Version of Record: January 22, 2018
Accepted Manuscript: December 19, 2017

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Luis Carlos Garcia del Molino

Guangyu Robert Yang

Jorge F Mejias

Xiao-Jing Wang

(2017)
Paradoxical response reversal of top-down modulation in cortical circuits with three interneuron types

eLife 6:e29742.

https://doi.org/10.7554/eLife.29742
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Figures
Tables
Additional files

4 figures, 5 tables and 1 additional file

Figures

Figure 1

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Response to top-down modulation depends on baseline activity.

(a) Microcircuit connectivity and top-down modulatory input. (b) f-I curve. When input is low changes in input have almost no effect on the output rate, instead, when input is high changes in input …
https://doi.org/10.7554/eLife.29742.002

Figure 2

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Response matrix and disinhibition vs.

response reversal regime. (a–b) Tuning curves for the different populations and baseline activity in both scenarios (low and high). In the low baseline activity scenario (a) all populations are …
https://doi.org/10.7554/eLife.29742.003

Figure 3

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Random network model.

(a) Schematic of the model. Each population is composed of several rate units and the connectivity between units is random with probabilities extracted from experimental data in the literature. (b) …
https://doi.org/10.7554/eLife.29742.004

Figure 4 with 3 supplements

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Model of mouse V1 behavior.

(a) Schematic of the microcircuit. Visual input targets E and SST cells. Behavior related top-down modulation targets VIP cells. (b) Response of E and SST populations when a weak visual stimulus (6 …
https://doi.org/10.7554/eLife.29742.005

Figure 4—figure supplement 1

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Model of mouse V1 behavior with different grating sizes.

(a) Relative change in calcium fluorescence for gratings of diameters ranging from 10 deg to 60 deg for the two behavioral states: immobility (empty dots) and locomotion (filled dots) extracted from …
https://doi.org/10.7554/eLife.29742.006

Figure 4—figure supplement 2

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Robustness of the behavior.

Top: Example of three connectivity matrices that have the same qualitative behavior (in pAs). Bottom: rate modulation (rate during locomotion minus rate for immobility). Each bar corresponds to the …
https://doi.org/10.7554/eLife.29742.007

Figure 4—figure supplement 3

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Alternative architectures.

Two alternative microcircuits with visual input targeting E, SST and PV populations and PV to VIP (a) and PV to SST (b) connections exhibit the same qualitative behavior as the circuit in Figure 1 …
https://doi.org/10.7554/eLife.29742.008

Tables

Table 1

Connectivity matrix (in pAs).

https://doi.org/10.7554/eLife.29742.009

		From
		E	PV	SST	VIP
to	E	2.42	−0.33	−0.80	0
	PV	2.97	−3.45	−2.13	0
	SST	4.64	0	0	−2.79
	VIP	0.71	0	−0.16	0

Table 2

Population-dependent parameters.

https://doi.org/10.7554/eLife.29742.010

	E	PV	SST	VIP
$g_{l}$	6.25 nS	10 nS	five nS	five nS
$τ$	28 ms	8 ms	16 ms	16 ms

Table 3

Entries of the respone matrix.

https://doi.org/10.7554/eLife.29742.011

$M_{E E} = C (w_{P P} + d_{P}) (d_{S} d_{V} - w_{S V} w_{V S})$

$M_{P E} = C (w_{P E} (d_{S} d_{V} - w_{S V} w_{V S}) - w_{P S} (w_{S E} d_{V} - w_{S V} w_{V E}))$

$M_{S E} = C (w_{P P} + d_{P}) (w_{S E} d_{V} - w_{S V} w_{V E})$

$M_{V E} = C (w_{P P} + d_{P}) (w_{V E} d_{S} - w_{S E} w_{V S})$

$M_{E P} = - C w_{E P} (d_{S} d_{V} - w_{S V} w_{V S})$

$M_{P P} = - C ((w_{E E} - d_{E}) (d_{S} d_{V} - w_{S V} w_{V S}) + w_{E S} (w_{S E} d_{V} - w_{S V} w_{V E}))$

$M_{S P} = - C w_{E P} (w_{S E} d_{V} - w_{S V} w_{V E})$

$M_{V P} = - C w_{E P} (w_{V E} d_{S} - w_{S E} w_{V S})$

$M_{E S} = - C d_{V} (w_{E S} (w_{P P} + d_{P}) - w_{E P} w_{P S})$

$M_{P S} = - C d_{V} (w_{E S} w_{P E} - (w_{E E} - d_{E}) w_{P S})$

$M_{S S} = - C d_{V} ((w_{E E} - d_{E}) (w_{P P} + d_{P}) - w_{E P} w_{P E})$

$M_{V S} = - C (w_{V E} (w_{E S} (w_{P P} + d_{P}) - w_{E P} w_{P S}) + w_{V S} ((w_{E E} - d_{E}) (w_{P P} + d_{P}) - w_{E P} w_{P E}))$

$M_{E V} = C w_{S V} (w_{E S} (w_{P P} + d_{P}) - w_{E P} w_{P S})$

$M_{P V} = C w_{S V} (w_{E S} w_{P E} - (w_{E E} - d_{E}) w_{P S})$

$M_{S V} = C w_{S V} ((w_{E E} - d_{E}) (w_{P P} + d_{P}) - w_{E P} w_{P E})$

$M_{V V} = C (w_{E S} (w_{E S} (w_{P P} + d_{P}) - w_{E P} w_{P S}) - d_{S} ((w_{E E} - d_{E}) (w_{P P} + d_{P}) - w_{E P} w_{P E}))$

Table 4

Connection probabilities for the random network model.

https://doi.org/10.7554/eLife.29742.012

		From
		E	PV	SST	VIP
to	E	0.02	1	1	0
	PV	0.01	1	0.85	0
	SST	0.01	0	0	−0.55
	VIP	0.01	0	0.5	0

Table 5

Connectivity matrix for the mouse V1 model (in pAs).

https://doi.org/10.7554/eLife.29742.013

		From
		E	PV	SST	VIP
to	E	3.30	−3.48	−2.98	0
	PV	1.73	−4.25	−1.07	0
	SST	3.50	0	0	−4.51
	VIP	0.53	0	−0.13	0

Additional files

Transparent reporting form: https://doi.org/10.7554/eLife.29742.014
Download elife-29742-transrepform-v3.pdf

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