Adult-born granule cells improve stimulus encoding and discrimination in the dentate gyrus
- Instituto de Investigacion en Biomedicina de Buenos Aires (IBioBA) – CONICET/Partner Institute of the Max Planck Society, Polo Cientifico Tecnologico, Argentina
- Departamento de Fisica, FCEyN UBA, Ciudad Universitaria, Argentina
- Max Planck Institute for Molecular Physiology, Department of Systemic Cell Biology, Germany
Figures
Figure 1 with 2 supplements
Granule cells (GCs) recordings and analysis of the temporal structure of the responses to the same stimulus.
(A) Schematics of the experimental setup showing a hippocampal slice with the dentate gyrus highlighted in gray, and a blow-up of the dentate gyrus GCs. Colors indicate GCs’ age: 4-week-old GCs …
Figure 1—figure supplement 1
Intrinsic properties of granule cells (GCs) measured with current steps.
(A) Negative step in a 4-week-old GC (4wGC) (orange) and a mature GC (mGC) (black). (B, C) Passive properties obtained with negative steps. Spearman’s correlation between age and input resistance: …
Figure 1—figure supplement 2
Adjusting the baseline and amplitude of the stimulus to granule cells (GCs) of different ages while keeping the same time structure.
(A) Immature GCs have larger input resistances, hence they need smaller currents to produce similar firing rates. (B) Resulting firing rates for the recorded GCs. There was no significant difference …
Figure 2 with 2 supplements
Spike response model (SRM) fits to recorded granule cells (GCs).
(A) Schematics of the SRM. Free model parameters are highlighted within boxes. (B–G) SRM parameters obtained for all GCs of different ages: (B) voltage bias vb, (C) membrane filter , (D) postspike …
Figure 2—figure supplement 1
SRM fitting validations.
(A) Root mean squared error of the subthreshold membrane potential prediction using the validation data. (B) Log-likelihood per spike relative to the likelihood of a Poisson process of the same rate …
Figure 2—figure supplement 2
Linear discriminant analysis components as determined by the scalings of each parameter used.
Figure 3 with 3 supplements
Model-based Bayesian decoding of the stimulus using recordings of single granule cells (GCs) and simulated GCs (SGCs).
(A) Model-based Bayesian decoding scheme, illustrating how CGs or SGCs encode a stimulus in spike trains that can be used to estimate the stimulus that produced them. (B) Spike trains from recorded …
Figure 3—figure supplement 1
Decoding the experimentally used stimulus from the recorded spike trains.
(A) and (B) conditional entropy from Figure 3D and E vs. the and reduction in uncertainty obtained by decoding the experimentally used stimulus from the recorded spike trains.
Figure 3—figure supplement 2
Decoding performance with increasing number of trials.
Fold increase in (A) and (B) information with number of trials from single simulated granule cells (SGCs). Spearman’s correlation between age and at 26 trials: , ; Spearman’s correlation …
Figure 3—figure supplement 3
Decoding with pairs of SGCs of different age groups.
(A) Decoding example using approximately 140 spikes from a g5-week-old granule cell (5wGC) and a mature GC (mGC) to get a single stimulus reconstruction. (B) obtained by using pairs of simulated …
Figure 4 with 3 supplements
Greedy procedure used to build populations of simulated granule cells (SGCs) optimized for stimulus reconstruction.
(A) Greedy procedure diagram: at each step, the SGC that optimizes stimulus reconstruction measured by average is chosen. (B) Decoding example using a population of five SGCs of different ages. …
Figure 4—figure supplement 1
Statistics of SGC selection.
(A) Total number of times each simulated granule cell (SGC) was selected by the greedy procedure after 12 steps. SGC index was sorted according to counts. (B) Average value achieved by each SGC …
Figure 4—figure supplement 2
Greedy procedure with restricted mSGC selection in the first steps.
(A) Greedy procedure diagram restricting the selection to mature simulated granule cells (mSGCs) in the first steps: at each step, the SGC that optimizes stimulus reconstruction measured by average is chosen. Over the first steps we only allow mSGCs to be selected, and after steps we select SGCs from the whole pool of mSGCs and immature SGCs. (B) and (C) information vs. number of immature SGCs in each population. The populations were built restricting the first 11, 10, 9, 8, 7, 6, 5, and 4 steps of the greedy procedure, to select mSGCs. Solid lines indicate averages and error bars indicate ±1 s.e.m. Dashed green lines indicate the performance obtained with 12 SGCs’ populations built without restrictions. Dashed black lines indicate the performance obtained with 12 SGCs’ populations built with exclusively mSGCs.
Figure 4—figure supplement 3
Decoding stimuli with underlying theta oscillations.
(A) Validation data (blue) generated using a stimulus exhibiting a theta rhythm together with its spike response model (SRM) prediction (red). Top: Stimulus with theta rhythm. Middle: Spike raster …
Figure 5
Pattern discrimination between pairs of fluctuating stimuli.
(A) Diagram of the pattern discrimination procedure. (B) Discrimination accuracy achieved by mixed and exclusively mature populations for different degrees of separation between the two stimuli. …
