Constraints on persistent activity in a biologically detailed network model of the prefrontal cortex with heterogeneities

Persistent activity, the maintenance of neural activation over short periods of time in cortical networks, is widely thought to underlie the cognitive function of working memory. A large body of modeling studies has reproduced this kind of activity using cell assemblies with strengthened synaptic connections. However, almost all of these studies have considered persistent activity within networks with homogeneous neurons and synapses, making it difficult to judge the validity of such model results for cortical dynamics, which is based on highly heterogeneous neurons. Here, we consider persistent activity in a detailed, strongly data-driven network model of the prefrontal cortex with heterogeneous neuron and synapse parameters. Surprisingly, persistent activity could not be reproduced in this model without incorporating further constraints. We identified three factors that prevent successful persistent activity: heterogeneity in the cell parameters of interneurons, heterogeneity in the parameters of short-term synaptic plasticity and heterogeneity in the synaptic weights. Our model predicts that persistent activity is recovered if the heterogeneity in the activity of individual interneurons is diminished, which could be achieved by a homeostatic plasticity mechanism. Such a plasticity scheme could also compensate the heterogeneities in the synaptic weights and short-term plasticity when applied to the inhibitory synapses. Cell assemblies shaped in this way may be potentially targeted by distinct inputs or become more responsive to specific tuning or spectral properties. Furthermore, the model predicts that a network that exhibits persistent activity is not able to dynamically produce intrinsic in vivo-like irregular activity at the same time, because heterogeneous synaptic connections are required for these dynamics. Thus, the background noise in such a network must either be produced by external input or constitutes an entirely different state of the network, which is brought about, e.g., by neuromodulation. Author summary To operate effectively in a constantly changing world, it is crucial to keep relevant information in mind for short periods of time. This ability, called working memory, is commonly assumed to rest on reverberating activity among members of cell assemblies. While effective in reproducing key results of working memory, most cell assembly models rest on major simplifications such as using the same parameters for all neurons and synapses, i.e., assuming homogeneity in these parameters. Here, we show that this homogeneity assumption is necessary for persistent activity to arise, specifically for inhibitory interneurons and synapses. Using a strongly data-driven network model of the prefrontal cortex, we show that the heterogeneities in the above parameters that are implied by in vitro studies prevent persistent activity. When homogeneity is imposed on inhibitory neurons and synapses, persistent activity is recovered. We propose that the homogeneity constraints can be implemented in the brain by means of homeostatic plasticity, a form of learning that keeps the activity of a network in a constant, homeostatic state. The model makes a number of predictions for biological networks, including a structural separation of networks responsible for generating persistent activity and spontaneous, noise-like activity.