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Intrinsic Plasticity Underlies the Malleability of Neural Network Heterogeneity

Daniel Trotter*

Taufik Valiante†,‡

Jeremie Lefebvre†,§

  • *Contact author: dtrotte2@uottawa.ca
  • †These authors contributed equally to this work.
  • ‡Also at Krembil Research Institution, University Health Network, Toronto, Ontario, Canada.
  • §Also at Department of Physics, University of Ottawa, Ottawa, Ontario, Canada and Krembil Research Institution, University Health Network, Toronto, Ontario, Canada.
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PRX Life 4, 013023 – Published 20 February, 2026

DOI: https://doi.org/10.1103/hrp5-sgvr

Abstract

Diversity exists throughout biology, playing an important role in maintaining robustness and stability. The same is true of the brain, as has become increasingly apparent in recent years with the accumulation of datasets of unparalleled resolution. These datasets show widespread neural heterogeneity, spanning cells, circuits, and system dynamics, marking it as an unavoidable component of the brain's composition. Recent experiments found that declines in heterogeneity among neurons may accompany pathological states. While heterogeneity has been linked to stability, robustness, and increased computational potential, the loss of biophysical diversity was found conducive to the onset of seizurelike activity, suggesting an important functional role. Despite this, how changes in heterogeneity arise remains unknown. Oftentimes considered a static metaparameter resulting from solely genetic disposition, heterogeneity is, in fact, a highly dynamic property of biological networks arising from various sources. Here, we consider this through the lens of intrinsic plasticity, the activity-dependent modulation of neuron biophysical properties, which we propose allows the degree of biophysical diversity to fluctuate in time. Using a network of Poisson neurons endowed with intrinsic plasticity, we combine analytical and numerical approaches to measure the effect of input statistics on the excitability of individual cells, and how this translates into changes in network heterogeneity at the population scale. Our results indicate that, through intrinsic plasticity, diversity in synaptic inputs promotes heterogeneity in cell-to-cell excitability due to changes in the statistics of presynaptic firing rates and network topology. In contrast, whenever the statistics of synaptic input between cells were too similar, intrinsic plasticity promoted the decline in heterogeneity. Further, we show that changes in heterogeneity can coexist with degeneracy in the firing rate between neurons. Taken together, understanding how input statistics affect neuronal network heterogeneity may provide key insights into brain function, resilience, and the manipulability of neural diversity through intrinsic plasticity.

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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.

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PRX Life 4, 013023– Published 20 February, 2026
  • Received 14 July 2025
  • Accepted 20 January 2026
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