| name | fast-efficient-coding-criticality |
| version | 1.0 |
| description | Fast efficient coding and sensory adaptation in gain-adaptive recurrent networks. Theoretical framework showing gain modulation in recurrent circuits reconciles adapter-repulsion and prior-attraction under a unified efficient-coding objective. |
| tags | ["computational-neuroscience","efficient-coding","sensory-adaptation","recurrent-networks","neural-coding","gain-modulation","theoretical-neuroscience"] |
| trigger_conditions | ["efficient coding sensory adaptation","adapter repulsion prior attraction","gain modulation recurrent network","neural tuning curve adaptation","sensory prior neural coding","recurrent circuit efficient coding"] |
| source | PubMed PMID:42140911 / Nature Communications 2026 |
| authors | ["Arthur Prat-Carrabin","Maximilian V. Harl","Samuel J. Gershman"] |
| doi | 10.1038/s41467-026-73032-0 |
Fast Efficient Coding and Sensory Adaptation in Gain-Adaptive Recurrent Networks
Overview
This paper presents a gain-adaptive recurrent network model that unifies two seemingly contradictory empirical phenomena in sensory neuroscience under a single efficient-coding framework:
- Adapter repulsion: Tuning curves shift away from an adapting stimulus
- Prior attraction: Tuning curves shift toward frequently-seen stimuli
The resolution: gain modulation propagated through recurrent connections mediates rapid, environment-adaptive efficient coding.
Core Problem
Efficient coding theory predicts that neural representations should adapt to match the statistical structure of the environment. However:
- Prior attraction is expected: encode more precisely where stimuli are frequent
- Adapter repulsion is observed: tuning curves repel from repeated stimuli
- These appear contradictory — the same physical mechanism cannot produce both
Key Insight
The reconciliation lies in the shape of the prior distribution:
- Peaked (narrow) priors → adapter repulsion (local gain suppression + recurrent propagation)
- Broad (diffuse) priors → prior attraction (global gain enhancement at prior peak)
Both emerge from the same gain-modulation mechanism optimizing an efficient-coding objective.
Mathematical Framework
Efficient Coding Objective
L = Accuracy - λ · Spiking_Cost
= E[log p(s | r)] - λ · Σ_i E[r_i]
where:
s = stimulus, r = neural responseλ = metabolic cost weight- Optimal tuning curves maximize accuracy per spike
Gain Modulation Dynamics
τ · dg_i/dt = -g_i + f(I_i + Σ_j W_ij · g_j)
where:
g_i = gain of neuron iW_ij = recurrent weight matrixf(·) = nonlinear activationτ = adaptation time constant (fast, ~100ms)
Tuning Curve Update
TC_i(θ) = g_i · TC_i^0(θ - Δθ_i)
- Amplitude modulated by gain
