{
  "id": 5653344,
  "title": "Neural competition and probabilistic representations",
  "url": "https://urgent.news/2026/09/04/neural-competition-and-probabilistic-representations",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-04T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.08.31.748371v1?rss=1"
  },
  "original_language": "en",
  "account": "Perception and action are closely tied to the statistical properties of physical stimuli and likely rewards. The precise mechanisms, however, remain elusive. A straightforward, biologically realistic network model reveals that probabilistic behavior can emerge naturally from the sampling of competing responses across various situations. Remarkably, this model also provides a computational framework to explain the common observation of balanced excitation and inhibition in the brain.\n\nIn this network, recurrent connections among excitatory neurons form multiple attractor states. These states are coupled to an inhibitory neuron pool, which enforces exclusivity among the attractors. When the same stimuli are presented simultaneously, the competing attractors engage in an alternating pattern of activity. This global transition between states is driven by local, uncorrelated spiking noise, yet over time it reflects the relative strengths of the different attractors, providing a neural basis for choice behavior in the face of uncertainty.\n\nSimple probabilistic competitive recurrent networks (PCRN) permit closed-form analysis, offering insights into the neural underpinnings of decision-making under uncertainty. More intricate systems consisting of laterally connected PCRN can collectively address the numerous local ambiguities inherent in sensory stimuli. Through rapid alternations between attractors, these systems settle into globally-consistent configurations that align with perceptual reports. Crucially, alternations in activity are essential, and they only occur if the inhibitory pool within the PCRN is robust enough to prevent any single attractor from reaching saturation. Thus, a delicate balance between excitation and inhibition emerges as both a necessary condition and a defining characteristic of probabilistic sampling within cortical networks.",
  "summary": "Perception and action show tight links to the statistical structure of physical stimuli and likely rewards, but the underlying mechanisms are unknown. A simple, biologically plausible network model shows that probabilistic behavior emerges naturally in diverse scenarios, and arises from sampling of competing responses. Notably, it also provides a principled computational rationale for the…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}