Task success in trained spiking neural network models coincides with emergence of cross-stimulus-modulated inhibition

Journal article

The neocortex is composed of spiking neurons interconnected in a sparse, recurrent network. Spiking activity within these networks underlies the computations that transform sensory inputs into appropriate behavioral responses. In this study, we train recurrent spiking neural network (SNN) models constrained by neocortical connectivity statistics and investigate the architectural changes that enable task-relevant, spike-based computations. We employ a binary state change detection task—an experimental paradigm used in animal behavioral studies. Our SNNs consist of interconnected excitatory and inhibitory units with connection probabilities and strengths modeled after the mouse neocortex and maintained throughout training and evaluation. Following training, we find that SNNs selectively modulate firing rates based on the binary input state, and that excitatory and inhibitory connectivity within and between input and recurrent layers adjusts accordingly. Notably, inhibitory neurons in the recurrent layer that positively modulate firing rates in response to one input state strengthen their connections to recurrent units with the opposite modulation. This push-pull connectivity—where excitation and inhibition are dynamically balanced in an opponent fashion—emerges as a key computational strategy and is reminiscent of connectivity observed in primary visual cortex. Using a one-hot output encoding yields identical firing rates to both input states, yet the push-pull inhibitory motif still arises. Importantly, this motif fails to emerge when Dale’s principle is not enforced during training, and task performance also declines.Furthermore, disrupting spike timing by a few milliseconds significantly impairs task performance, highlighting the importance of precise spike time coordination for computation in sparse networks like neocortex. The emergence of push-pull inhibition through task training in spiking models underscores the crucial role of interneurons and structured inhibition in shaping neural dynamics and spike-based information processing.

Authors: Zhu, Y; Smith, CMB; Jabri, T; Tang, M; Scherr, F

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Springer
• Journal: Biological Cybernetics
• Volume: 120
• Issue: 1
• Article number: 2
• Publication date: 2026-01-07
• Acceptance date: 2025-11-13
• DOI: 10.1007/s00422-025-01030-4
• EISSN: 1432-0770
• ISSN: 0340-1200
• Language: English
• Keywords: Spiking neural networks; Interneurons; Inhibition; Trained network models; Recurrent neural networks