Synapses—the connections between neurons—are remarkably efficient at transmitting information. A new study led by James Stone reveals that this efficiency can be predicted by a simple model from information theory, with no adjustable parameters. The work, published on arXiv, builds on earlier findings that synaptic noise is minimized for a given energy budget, and it shows that synapses naturally operate at the signal-to-noise ratio that maximizes information per unit energy.
The Research
Building on observations by Harris et al. (2015) that synapses convey as many bits per Joule as possible when conductance is at its natural value, Stone asked why efficiency drops so quickly when conductance changes. He combined that work with a 2026 study by Malkin et al., which found a minimal energy boundary for synaptic transmission. By expressing that boundary in Shannon's information theory terms (Shannon, 1949), Stone demonstrated that synapses operate at signal-to-noise ratios that maximize information efficiency. The model accurately predicts the efficiency decline observed by Harris et al. across a wide range of conductances. Crucially, because the model is derived from biophysics, it contains no free parameters—meaning the fit is not forced but emerges from physical principles.
Why It Matters
This finding supports the overarching principle that the brain has evolved for maximum efficiency. For the average person, it underscores that even basic neuronal processes are optimized through evolution. Understanding that synaptic efficiency is limited by fundamental physics helps explain why certain mental tasks are energetically costly and why brain training may improve efficiency by strengthening synaptic connections.
What You Can Do
To support your brain's natural efficiency, engage in activities that promote synaptic plasticity: learn new skills, get regular aerobic exercise, and maintain a healthy diet with omega-3 fatty acids. Quality sleep also helps consolidate learning and maintain efficient neural firing.
Source: arXiv q-bio.NC
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