Research map / Learning theory and algorithms / Prediction and decision losses
Efficient Neural Codes under Metabolic Constraints
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use ON-OFF instead of ON-ON coding for a neuron pair | Two-neuron monotonic code · Gaussian noise, linear cost | energy needed to saturate mutual information: 0.5rmax -> 0.25rmax | Helped |
| Use ON-OFF instead of ON-ON coding under extremely limited metabolic budget | Two-neuron monotonic code · Gaussian noise, K_total << r_max | log2 mutual information: −1 | Helped |
| Use ON-OFF instead of ON-ON coding with unlimited metabolic budget | Two-neuron monotonic code · Unconstrained energy | mutual information | No clear effect |
| Relax the metabolic budget until it exceeds the threshold K_thre | Single-neuron optimal code · Gaussian noise | optimal tuning curve shape | Mixed |
| Switch from Gaussian to Poisson noise | Single-neuron optimal code · Metabolic-constrained coding | penalty on large responses | Hurt |
| Use ON-ON pair instead of ON-OFF pair | Two-neuron monotonic code · Gaussian noise, linear cost | energy cost for same information | Hurt |
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