Acetylcholine regulates memory encoding and retrieval by inducing the hippocampus to switch between pattern separation and pattern completion modes. However, both processes can introduce significant variations in the level of network activity and potentially cause a seizure-like spread of excitation. Thus, mechanisms that keep network excitation within certain bounds are necessary to prevent such instability. We developed a biologically realistic computational model of the hippocampus to investigate potential intrinsic mechanisms that might stabilize the network dynamics during encoding and retrieval. The model was developed by matching experimental data, including neuronal behavior, synaptic current dynamics, network spatial connectivity patterns, and short-term synaptic plasticity. Furthermore, it was constrained to perform pattern completion and separation under the effects of acetylcholine. The model was then used to investigate the role of short-term synaptic depression at the recurrent synapses in CA3, and inhibition by basket cell (BC) interneurons and oriens lacunosum-moleculare (OLM) interneurons in stabilizing these processes. Results showed that when CA3 was considered in isolation, inhibition solely by BCs was not sufficient to control instability. However, both inhibition by OLM cells and short-term depression at the recurrent CA3 connections stabilized the network activity. In the larger network including the dentate gyrus, the model suggested that OLM inhibition could control the network during high cholinergic levels while depressing synapses at the recurrent CA3 connections were important during low cholinergic states. Our results demonstrate that short-term plasticity is a critical property of the network that enhances its robustness. Furthermore, simulations suggested that the low and high cholinergic states can each produce runaway excitation through unique mechanisms and different pathologies. Future studies aimed at elucidating the circuit mechanisms of epilepsy could benefit from considering the two modulatory states separately.

Interactions across frontal cortex are critical for cognition. Animal studies suggest a role for mediodorsal thalamus (MD) in these interactions, but the computations performed and direct relevance to human decision making are unclear. Here, inspired by animal work, we extended a neural model of an executive frontal-MD network and trained it on a human decision-making task for which neuroimaging data were collected. Using a biologically-plausible learning rule, we found that the model MD thalamus compressed its cortical inputs (dorsolateral prefrontal cortex, dlPFC) underlying stimulus-response representations. Through direct feedback to dlPFC, this thalamic operation efficiently partitioned cortical activity patterns and enhanced task switching across different contingencies. To account for interactions with other frontal regions, we expanded the model to compute higher-order strategy signals outside dlPFC, and found that the MD offered a more efficient route for such signals to switch dlPFC activity patterns. Human fMRI data provided evidence that the MD engaged in feedback to dlPFC, and had a role in routing orbitofrontal cortex inputs when subjects switched behavioral strategy. Collectively, our findings contribute to the emerging evidence for thalamic regulation of frontal interactions in the human brain.

We propose a novel reduced order neuronal network modeling framework that includes an enhanced firing rate model and a corresponding synaptic calcium-based synaptic learning rule. Specifically, we propose enhancements to the Wilson-Cowan firing rate neuron model that permits full spike frequency adaptation seen in biological LA neurons, while being sufficiently general to accommodate other spike frequency patterns. We also report a technique to incorporate calcium-dependent plasticity in the synapses of the network using a regression scheme to link firing rate to postsynaptic calcium. Together, the single cell model and the synaptic learning scheme constitute a general framework to develop computationally efficient neuronal networks that employ biologically-realistic synaptic learning. The reduced order modeling framework was validated using a previously reported biophysical conductance-based neuronal network model of a rodent lateral amygdala (LA) that modeled features of Pavlovian conditioning and extinction of auditory fear (Li et al., 2009). The framework was then used to develop a larger LA network model to investigate the roles of tone and shock distributions and of intrinsic connectivity in auditory fear learning. The model suggested combinations of tone and shock densities that would provide experimental estimates of tone responsive and conditioned cell proportions. Furthermore, it provided several insights including how intrinsic connectivity might help distribute sensory inputs to produce conditioned responses in cells that do not directly receive both tone and shock inputs, and how a balance between potentiation of excitation and inhibition prevents stimulus generalization during fear learning.