Quantitative properties of a feedback circuit predict frequency-dependent pattern separation
- Institute for Experimental Epileptology and Cognition Research, University of Bonn, Germany
- International Max Planck Research School for Brain and Behavior, University of Bonn, Germany
- Deutsches Zentrum für Neurodegenerative Erkrankungen e.V., Germany
Figures
Figure 1 with 1 supplement
Recruitment of feedback inhibition assessed using population Ca2+ imaging.
Combined population Ca2+ imaging and IPSC recordings of GCs during antidromic electrical stimulation. (A) Schematic illustration of the experimental setup. Dashed lines represent cuts to sever CA3 …
Figure 1—figure supplement 1
Detection of single action potential induced calcium transients.
A section of the dentate gyrus was loaded with OGB1-AM and imaged with multibeam two-photon microscopy while antidromically eliciting action potentials and recording from individual cells in …
Figure 2 with 3 supplements
Recruitment of feedback inhibition assessed optogenetically.
(A) EYFP fluorescence in dentate GCs of Prox1/ChR2(H134R)-EYFP transgenic mice. The field of view for rapid focal optogenetic stimulation is indicated by a blue square. A typical stimulation site …
Figure 2—figure supplement 1
Optogenetically activated cell fraction.
(A) Schematic illustration of the experimental setup. Cells were recorded in cell attached mode (two per slice), while systematically stimulating at varying distances. Traces from a representative …
Figure 2—figure supplement 2
Error in somatic IPSC measurements with increasing inhibitory conductance.
A simple ball and stick model was used to estimate the impact of voltage escape errors for dendritic IPSCs (soma diameter 20um; dendrite diameter and length 3 µm and 200 µm, respectively). To …
Figure 2—figure supplement 3
Absence of single GC induced feedback inhibition.
Pairs of juxtaposed GCs (<100 µm distance) were recorded to test for single GC induced feedback inhibition. (A) Schematic illustration of the experimental setup. (B) Example of a paired recording …
Figure 3
Spatial organization of feedback inhibition.
Feedback IPSCs recorded from an individual GC while GCs at varying distances were activated. (A) Schematic illustration of the stimulation paradigm and example IPSC traces of an individual trial …
Figure 4 with 1 supplement
Short-term dynamics in the feedback inhibitory microcircuit.
Trains of ten antidromic electrical stimulations at 1, 10, 30 or 50 Hz were applied to elicit disynaptic feedback inhibition or excitation of hilar cells (electrical stimulation artifacts were …
Figure 4—figure supplement 1
Frequency dependence of feedback inhibition over space.
Trains of 10 focal optic stimulations (20 ms duration) were applied either locally (1) or remotely (2) to elicit feedback inhibition. (A) Schematic of the experimental paradigm and example traces of …
Figure 5 with 1 supplement
Computational model of the DG feedback circuit.
A biophysically realistic model of DG was tuned to capture the key quantitative features of the feedback circuit. All analyses were performed as for the real data (including IPSC normalization to …
Figure 5—figure supplement 1
Model tuning and validation.
(A) Frequency responses to somatic current injections of model cell types (GC: granule cells, BC: basket cells, HC: hilar perforant path associated cells, MC: mossy cells). All model cells were …
Figure 6 with 7 supplements
Frequency dependent pattern separation of temporally structured inputs.
The quantitative DG model was challenged with theta (10 Hz) or slow gamma (30 Hz) modulated input patterns with defined overlap to probe its pattern separation ability. (A) Pair of theta modulated …
Figure 6—figure supplement 1
Activity levels and pattern separation.
(GCs: granule cells; MCs: mossy cells; BCs: basket cells; HCs: HIPP cells; n = 7 model runs). (A) Exemplary raster plots of 10 Hz and 30 Hz modulated inputs. (B) Mean number of action potentials per …
Figure 6—figure supplement 2
Robustness over different Similarity Metrics.
To test if the main finding of frequency dependent pattern separation, particularly for highly similar inputs, depended on the similarity metric used, the original data was reanalyzed with two …
Figure 6—figure supplement 3
Isolated pattern separation effects of spatial tuning and MF facilitation.
Figure 6—figure supplement 4
Robustness for shorter analysis time-window.
(A) 33 ms time-resolved pattern separation effects of the full model, isolated feedback (FB) or feedforward (FF) inhibition for theta modulated input (10 Hz). All analyses were performed as above …
Figure 6—figure supplement 5
Robustness over various IPSC decay time-constants and over the full gamma range.
(A–C) To test if the frequency dependence of feedback inhibitory pattern separation remained robust for different IPSC decay time constants we probed a range of altered time constants (our …
Figure 6—figure supplement 6
Robustness for increased feedforward inhibition.
To test if the frequency-dependent enhancement of feedback inhibitory pattern separation of highly similar inputs was sensitive to the changes in the relative strengths of feedforward and feedback …
Figure 6—figure supplement 7
Robustness for increased perforant path (PP) drive.
The weight of the PP input synapses was varied between 0.6x to 2x their original weight. (A) Illustration of the network alteration. (B) Active GC fractions for the full network (full), the no …
Tables
Key resources table
| Reagent type (species) or resource | Designation | Source or reference | Identifiers | Additional information |
|---|---|---|---|---|
| Strain, strain background (Mus musculus) | C57BL/6N | Charles River | Strain Code 027 | |
| Strain, strain background (Mus musculus) | Prox1-Cre | MMRRC-UCD | RRID: MMRRC_036632-UCD | obtained as cryopreserved sperm and rederived in the local facility |
| Strain, strain background (Mus musculus) | Ai32-ChR-eYFP | Jackson Laboratory | RRID: IMSR_JAX:012569 | |
| Other | UGA-40 | RAPP Optoelectronics | Galvanometric, focal laser stimulation device | |
| Software, algorithm | Igor Pro 6.3 | Wavemetrics | ||
| Software, algorithm | Python 3.5 scikit learn | Pedregosa et al. (2011) | https://scikit-learn.org/stable/ | |
| Software, algorithm | ouropy | Custom Python code. This Paper | https://github.com/danielmk/ouropy | |
| Software, algorithm | pyDentate | Custom Python code, This Paper | https://github.com/danielmk/pyDentateeLife2020 | |
| Software, algorithm | Neuron 7.4 | Carnevale and Hines, 2006 | ||
| Software, algorithm | Prism 6 | Graphpad |
Additional files
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Supplementary file 1
Literature review for DG circuit short-term dynamics.
Studies reporting short-term dynamics within the DG circuit were reviewed with a main focus on facilitation or depression of synaptic connections defined by pre and postsynaptic cell types. Note the abundance of depressing synapses (quantitative descriptions of depression blue). Also note the complexity of direct connections between Interneurons (lower third of the table).
- https://cdn.elifesciences.org/articles/53148/elife-53148-supp1-v2.xlsx
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Supplementary file 2
Model parameters.
Overview of synaptic and intrinsic parameters between model cell-types. First row includes modeled cell number per type. PP: perforant path, GC: granule cell, MC: mossy cell, BC: basket cell, HC: Hilar perforant path associated cell; Weight: maximal synaptic conductance, Facilitation Max.: maximal fold increase of synaptic conductance, Decay Tau: synaptic decay time constant, Facilit. Tau: facilitation time constant, Delay: latency to postsynaptic event after presynaptic action potential, Target pool: range of n closest cells potentially receiving an output, Divergence: number of output synapses per cell stochastically picked from target pool, Target segments: cellular compartment receiving the synapse. Values in brackets are values for robustness analyses in Figs. S10, S11.
- https://cdn.elifesciences.org/articles/53148/elife-53148-supp2-v2.xlsx
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Transparent reporting form
- https://cdn.elifesciences.org/articles/53148/elife-53148-transrepform-v2.pdf
