Impact of Noise and Spike Initiation Properties on the Encoding and Transmission of Neural Information
Bibliographic record
Abstract
Neurons use action potentials, or spikes, to process information. Different aspects of spiking, such as its rate or timing, are used to encode information about different features of an input. But noise can influence how robustly information is represented in each coding scheme. Additionally, information can be lost if neural representations are not transmitted with high fidelity to downstream neurons. In both cases, properties of the input (its amplitude and kinetics) and properties of the neuron (its spike initiation mechanism and excitability) impact neural information processing. In my thesis, I first investigated how axons are optimized to transmit spike-based representations. Using patch clamp electrophysiology combined with optogenetics, I showed that the axon of CA1 pyramidal neurons spikes transiently in response to sustained depolarization, in contrast to the soma and axon initial segment, which spike repetitively. These distinct spiking patterns are due to the differential expression of ion channels, supporting functional specialization of neuronal compartments. Specifically, low-threshold potassium channels (Kv1) cause the axon to behave as a high-pass filter, enabling high fidelity transmission of spike-based information so that the axon selectively responds to inputs with fast kinetics. Together with biophysical modeling, my findings demonstrate that spike initiation properties in each part of the neuron are well matched to the signals normally processed in that neuronal compartment. I then investigated how background synaptic activity (noise) affects rate and temporal coding of vibrotactile stimuli. Using patch clamp electrophysiology and dynamic clamp in vitro, I found that layer 2/3 pyramidal neurons in primary somatosensory cortex spike intermittently to inputs repeated at frequencies perceived as vibration. The fraction of inputs evoking a spike varies with input amplitude, enabling firing rate to encode stimulus intensity. Despite being small in amplitude, inputs are abrupt in onset, which allows them to evoke precisely timed spikes, even under noisy conditions. Unreliable spiking allows noise to produce irregular skipping, enabling spike times (patterns) to encode stimulus frequency. The reliability and precision of spikes depend on input amplitude and kinetics, respectively. With the help of simulations, my results show that noise helps multiplexed rate and temporal coding.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".