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Autonomous Learning from Intrinsic Sensory Dependencies Yields Generalizable Representations in Cortical-Like Networks

Udaya B. Rongala* and Henrik Jörntell†

  • Neural Basis of Sensorimotor Control, Department of Experimental Medical Science, Lund University, Lund, Sweden

  • *Contact author: udaya_bhaskar.rongala@med.lu.se
  • †Contact author: henrik.jorntell@med.lu.se
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PRX Life 4, 023030 – Published 22 June, 2026

DOI: https://doi.org/10.1103/wkwq-r8hk

Abstract

How biological brains become operational from development and onwards is an unresolved issue. Here we explored the emerging effects in a brain circuit model that received simulated touches through a biological skin model, which featured the potentially critical aspect of sensory dependencies resulting from mechanical couplings across a tissue patch. Our skin model system was connected to a generic, primitive cortical-like subnetwork that was composed of fully connected excitatory and inhibitory neurons, where each individual synapse was subject to continuous, independent, Hebbian-like learning. We used continuous random mechanical activations of the skin model, in this regard mimicking behavioral patterns of early brain development observed in infants, and let the neuron-independent learning define the function that emerged in the network. Remarkably, we found that the network could rapidly learn to separate various naive dynamic skin inputs and solve a kinematics task it had never encountered, even when substantial parts of the sensor population or even network connections were removed post-training. We propose that autonomous learning from a sensor population with intrinsic, time-evolving dependencies could cause the extensively recursive cortical network to gradually adapt its intrinsic dynamics to better mirror the various dynamics of whatever body it is connected to, which results in many biologically useful features for the early acquisition of brain circuitry function.

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This article appears in the following collection:

PRX Life Collection on adaptation and learning

PRX Life presents a special collection on ‘Biological systems that learn’, curated by the organizers of the National Institute for Theory and Mathematics in Biology (NITMB) workshop together with early-career researchers. The papers in this collection illustrate how living systems acquire functions, showing it is useful to describe them as tunable matter with many individually adjustable degrees of freedom characterizing microscopic properties. Drawing inspiration from AI and non-biological systems that can acquire desired functions, the featured research highlights the timeliness of the topic and its potential to drive unifying frameworks for emergent collective function in living tunable matter.

Every article in this collection is subject to a rigorous peer review process, upholding the high standards applied to all papers in the journal. The PRX Life editorial team manages peer review and makes all editorial decisions.

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PRX Life 4, 023030– Published 22 June, 2026
  • Received 31 August 2025
  • Accepted 12 May 2026
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