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Predictable sensory stimuli do not evoke significant responses in a subset of cortical excitatory neurons. Some of those neurons, however, change their activity upon mismatches between actual and predicted stimuli. Different variants of these prediction-error neurons exist, and they differ in their responses to unexpected sensory stimuli. However, it is unclear how these variants can develop and coexist in the same recurrent network and how they are simultaneously shaped by the astonishing diversity of inhibitory interneurons. Here, we study these questions in a computational network model with three types of inhibitory interneurons. We find that balancing excitation and inhibition in multiple pathways gives rise to heterogeneous prediction-error circuits. Dependent on the network’s initial connectivity and distribution of actual and predicted sensory inputs, these circuits can form different variants of prediction-error neurons that are robust to network perturbations and generalize to stimuli not seen during learning. These variants can be learned simultaneously via homeostatic inhibitory plasticity with low baseline firing rates. Finally, we demonstrate a possible role of prediction-error neurons in weighting sensory inputs and predictions thereof.

Bio: After studying physics at the University of Leipzig, Loreen worked in the Durstewitz lab at the Central Institute for Mental Health in Mannheim and completed her PhD in computational neuroscience at the BCCN Heidelberg-Mannheim. In the last years, she has worked in the Sprekeler lab at the TU Berlin and in the Clopath lab at the Imperial College London as a postdoctoral researcher. Since 2022, she is an independent postdoctoral researcher at the TU Berlin funded by the DFG.

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