Postsynaptic frequency filters shaped by the interplay of synaptic short-term plasticity and cellular time scales
Bibliographic record
Abstract
Abstract Neuronal frequency filters can be thought of as constituent building blocks underlying the ability of neuronal systems to process information, generate rhythms and perform computations. How neuronal filters are generated by the concerted activity of a multiplicity of processes (e.g., electric circuit, history-dependent) and interacting time scales within and across levels of neuronal network organization is poorly understood. In this paper, we use mathematical modeling, numerical simulations and analytical calculations of the postsynaptic response to presynaptic spike trains to address this issue in a basic feedforward network motif in the presence of synaptic short-term plasticity (STP, depression and facilitation). The network motif consists of a presynaptic spike-train, a postsynaptic passive cell, and an excitatory (AMPA) chemical synapse. The dynamics of each network component are controlled by one or more time scales. We explain the mechanisms by which the participating time scales shape the neuronal filters at the (i) synaptic update level (the target of the synaptic variable in response to presynaptic spikes), which is shaped by STP, (ii) the synaptic level, and (iii) the postsynaptic membrane potential (PSP) level. We focus on three metrics that gives rise to three types of profiles (curves of the corresponding metrics as a function of the spike-train input frequency or firing rate): (i) peak profiles, (ii) peak-to-trough amplitude profiles, and (iii) phase profiles. The effects of STP are present at the synaptic update level and are communicated to the synaptic level where they interact with the synaptic time scales. The PSP filters result from the interaction between these variables and time scales and the biophysical properties and time scales of the postsynaptic cell. Band-pass filters (BPFs) result from a combination of low-pass filters (LPFs) and high-pass filters (HPFs) operating at the same or different levels of organization. PSP BPFs can be inherited from the synaptic level (STP-mediated BPFs) or they can be generated across levels of organization due to the interaction between (i) a synaptic LPF and the PSP summation-mediated HPF (PSP peaks), and (ii) a synaptic HPF and the PSP summation-mediated LPF (PSP amplitude). These types of BPFs persist in response to more realistic presynaptic spike trains: jittered (randomly perturbed) periodic spike trains and Poisson-distributed spike trains. The response variability is frequency-dependent and is controlled by STP in a non-monotonic frequency manner. The results and and lessons learned from the investigation of this basic network motif are a necessary step for the construction of a framework to analyze the mechanisms of generation of neuronal filters in networks with more complex architectures and a variety of interacting cellular, synaptic and plasticity time scales.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".