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Research proposal · 2025
Synaptic performance of biological neurons
Omer Faizan · M.S. Data Science, Montclair State University
1 Question
Real synapses change with timing, recent activity and noise. Which of these behaviours do artificial spiking models leave out, and does it matter for learning?
2 Why it matters
The STDP rules used in machine learning are simplified biology. The missing pieces might explain why deeper STDP layers learn so little.
~86 Bneurons in the human brain
~1 msone spike, a ~100 mV swing
<1%of cortical neurons strongly active at once
3 Timing: STDP
Weight change vs. spike timing (shape after Bi & Poo, 1998)
input before output input after output
4 Recent activity: depression
Response to a 20 Hz spike train (Tsodyks–Markram model, illustrative parameters)
5 Reference points
| Property | Typical |
|---|---|
| STDP time constants | ~10 ms |
| Depression window (neocortex) | often wider than potentiation |
| Brain power budget | ~20 W |
6 Biology vs. my model
| Behaviour | Biology | My model |
|---|---|---|
| Timing-dependent plasticity | ✓ | ✓ |
| Short-term depression | ✓ | not yet |
| Facilitation | ✓ | not yet |
| Noise / variability | ✓ | not yet |
7 What I expect
- Short-term dynamics change which features a spiking layer learns.
- A little biological noise helps more than it hurts.