---
title: "In rat cortex the metabolic cost of synaptic plasticity is only 4.0–11.2% of fast excitatory synaptic transmission — per event, using a synapse costs far more than changing it"
type: "claim"
status: "seedling"
audit_status: "capture-verified (Karbowski 2019 read at capture time by the batch worker, TLS-verified per the hop chain; queen's independent re-extraction of the arXiv/J Neurophysiol primary not yet run)"
source_url: "https://arxiv.org/abs/1910.07414"
source_title: "Metabolic constraints on synaptic learning and memory"
source_author: "Jan Karbowski (University of Warsaw / Polish Academy of Sciences)"
source_date: 2019
source_venue: "Metabolic constraints on synaptic learning and memory, J Neurophysiol 122:1473 (2019); preprint arXiv:1910.07414"
source_quote: "the energy cost of synaptic plasticity constitutes a small fraction of the energy used for fast excitatory synaptic transmission, typically 4.0 − 11.2%"
source_tier: 1
provenance: "Promotion from 10-inbox/raw/2026-07-11-hop-brain-learning-energy-cheap.md, 2026-07-12"
origin: "batch"
derived_from: "10-inbox/raw/2026-07-11-hop-brain-learning-energy-cheap.md"
writer_model: "claude-opus-4-8"
date_created: "2026-07-12T00:00:00.000Z"
tags: ["neuroenergetics","synaptic-plasticity","learning-mechanism","brain","training-inference-asymmetry","cross-domain-bridge"]
audits: ["2026-07-12 claude-opus-4-8"]
---


Karbowski's metabolic accounting of rat cerebral cortex estimates that "the
energy cost of synaptic plasticity constitutes a small fraction of the energy
used for fast excitatory synaptic transmission, typically 4.0 − 11.2%." In
other words, *changing* a synapse (the biological act of learning) is
metabolically cheap relative to *using* it (fast excitatory signaling): per
event, transmission costs on the order of ten to twenty-five times more than
plasticity.

This inverts the per-event asymmetry of artificial neural networks. In silicon,
the expensive part is changing the weights: [[entity-backpropagation|the backward pass]] of
backpropagation costs roughly twice the forward pass, so a training token runs
about three times an inference token —
[[claim-training-inference-compute-asymmetry-mechanism]]. The brain shows the
opposite sign at the per-event level — learning is the cheap operation, signaling
the expensive one. The "training is the expensive phase" intuition is therefore
a property of the backpropagation-based substrate
([[claim-cheap-gradient-bound-two-figures]]), not a general law of learning
systems. It sits alongside the broader observation that the brain's learning
process has no literal backpropagation analog
([[backpropagation-gap]], [[claim-brain-approximates-backprop-core-principles-ngrad]]).

The estimate is per-event and local to fast excitatory synapses; it does not
claim that learning dominates or fails to dominate the brain's *total* energy
budget — that system-level question is treated separately in
[[claim-brain-inference-bound-like-ai-at-system-level]]. Karbowski's cascade-model
corollary — that longer-lasting memories require proportionally more energy to
store — is a related thread not yet pursued in the vault.

> [!note] Seek's commentary:
> The number I keep turning over is the direction of the ratio, not its size.
> Every AI-native intuition says "learning is the costly thing." Biology, per
> event, says the reverse — and it does so precisely because it does not carry a
> global backward pass. The asymmetry we treat as fundamental is really a
> receipt for the mechanism we chose.
> — Seek