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Abstract Animals must flexibly adjust their behavior to respond appropriately in changing behavioral situations, in order to survive and thrive. The posterior parietal cortex (PPC) has been implicated in flexible decision-making, as it encodes sensory stimuli depending on their behavioral relevance [1] and is required to adapt to changes in stimulus-response contingencies in behavioral tasks [2]. Here, we show that while mice performed an auditory decision-making task, the temporal organization of neural activity differed across excitatory and inhibitory interneuron subtypes, with pyramidal neurons sparsely encoding task-related information. Sensory responses of all cell types were modulated according to behavioral relevance in the task, and signatures of this modulation were evident even before the presentation of sensory stimuli. Population activity patterns preceding stimulus presentation predicted the strength of sensory responses and even the mouse's behavioral accuracy in the task. A network model revealed that randomly organized inhibitory connectivity could replicate the selective filtering of sensory responses, but that context-dependent inputs to the network must be targeted to neurons responding to behaviorally relevant cues, and avoid those responding to irrelevant cues. Our results reveal a selective filtering mechanism in cortical circuits underlying flexible decision-making. 

Neurons in higher-order visual areas integrate information through a canonical computation called normalization. The strength of normalization is highly heterogeneous across neurons, and this heterogeneity correlates with attention-mediated modulations in neural responses. However, the circuit mechanism underlying the heterogeneous normalization strength is unclear. In this work, we study normalization in a spiking neuron network model of visual cortex. Our model reveals that the heterogeneity of normalization strength is highly correlated with the inhibitory current each neuron receives. The correlation between inhibition and other synaptic inputs explains the experimentally observed dependence of spike count correlations on normalization strength. Further, we find that neurons with stronger normalization encode information more efficiently, and that networks with more heterogeneity in normalization encode visual stimuli with higher information and capacity. Together, our model provides a mechanistic explanation of heterogeneous normalization strengths in the visual cortex and sheds light on the computational benefits of neuronal heterogeneity. 

Several inhibitory interneuron subtypes have been identified as critical in regulating sensory responses. However, the specific contribution of each interneuron subtype remains uncertain. In this work, we explore the contributions of cell type–specific activity and synaptic connections to the dynamics of a spatially organized spiking neuron network. We find that the firing rates of the somatostatin (SOM) interneurons align closely with the level of network synchrony irrespective of the target of modulatory input. Further analysis reveals that inhibition from SOM to parvalbumin interneurons must be limited to allow gradual transitions from asynchrony to synchrony and that the strength of recurrent excitation onto SOM neurons determines the level of synchrony achievable in the network. Our results are consistent with recent experimental findings on cell type–specific manipulations. Overall, our results highlight common dynamic regimes achieved across modulations of different cell populations and identify SOM cells as the main driver of network synchrony. 

Codes for the manuscript:  Parker, M. M., Rubin, J. E., & Huang, C. (2025). State modulation in spatial networks with three interneuron subtypes. Science Advances, 11(26), eads9134. 

Abstract: Neurons in higher-order visual areas integrate information through a canonical computation called normalization. The strength of normalization is highly heterogeneous across neurons, and this heterogeneity correlates with attention-mediated modulations in neural responses. However, the circuit mechanism underlying the heterogeneous normalization strength is unclear. In this work, we study normalization in a spiking neuron network model of visual cortex. Our model reveals that the heterogeneity of normalization strength is highly correlated with the inhibitory current each neuron receives. The correlation between inhibition and other synaptic inputs explains the experimentally observed dependence of spike count correlations on normalization strength. Further, we find that neurons with stronger normalization encode information more efficiently, and that networks with more heterogeneity in normalization have more information and higher capacity in encoding visual stimuli. Together, our model provides a mechanistic explanation of heterogeneous normalization strengths in the visual cortex, and sheds light on the computational benefits of neuronal heterogeneity.