AbstractPatients who suffer from traumatic brain injury (TBI) often complain of learning and memory problems. Their symptoms are principally mediated by the hippocampus and the ability to adapt to stimulus, also known as neural plasticity. Therefore, one plausible injury mechanism is plasticity impairment, which currently lacks comprehensive investigation across TBI research. For these studies, we used a computational network model of the hippocampus that includes the dentate gyrus, CA3, and CA1 with neuron-scale resolution. We simulated mild injury through weakened spike-timing-dependent plasticity (STDP), which modulates synaptic weights according to causal spike timing. In preliminary work, we found functional deficits consisting of decreased firing rate and broadband power in areas CA3 and CA1 after STDP impairment. To address structural changes with these studies, we applied modularity analysis to evaluate how STDP impairment modifies community structure in the hippocampal network. We also studied the emergent function of network-based learning and found that impaired networks could acquire conditioned responses after training, but the magnitude of the response was significantly lower. Furthermore, we examined pattern separation, a prerequisite of learning, by entraining two overlapping patterns. Contrary to our initial hypothesis, impaired networks did not exhibit deficits in pattern separation with either population- or rate-based coding. Collectively, these results demonstrate how a mechanism of injury that operates at the synapse regulates circuit function.Author summaryTraumatic brain injury causes diverse symptoms, and memory problems are common among patients. These deficits are associated with the hippocampus, a brain region involved in learning and memory. Neural plasticity supports learning and memory by enabling the circuit to adapt to external stimulus. After brain injury, plasticity can be impaired, perhaps contributing to memory deficits. Yet, this mechanism of injury remains poorly understood. We implemented plasticity impairment and learning in a network model of the hippocampus that is unique because it has a high degree of biological detail in its structure and dynamics compared to other similar computational models. First, we examined the relationship between neurons in the network and characterized how the structure changed with injury. Then we trained the network with two input patterns to test the function of pattern separation, which is the ability to distinguish similar contexts and underpins general learning. We found that the strength of the encoded response decreased after impairment, but the circuit could still distinguish the two input patterns. This work provides insight into which specific aspects of memory become dysfunctional after injury.
d-Lysergic acid diethylamide has major potential as a cognitive enhancer
