Encoding Time in Feedforward Trajectories of a Recurrent Neural Network Model.

Brain activity evolves through time, creating trajectories of activity that underlie sensorimotor processing, behavior, and learning and memory. Therefore, understanding the temporal nature of neural dynamics is essential to understanding brain function and behavior. In vivo studies have demonstrated that sequential transient activation of neurons can encode time. However, it remains unclear whether these patterns emerge from feedforward network architectures or from recurrent networks and, furthermore, what role network structure plays in timing. We address these issues using a recurrent neural network (RNN) model with distinct populations of excitatory and inhibitory units. Consistent with experimental data, a single RNN could autonomously produce multiple functionally feedforward trajectories, thus potentially encoding multiple timed motor patterns lasting up to several seconds. Importantly, the model accounted for Weber's law, a hallmark of timing behavior. Analysis of network connectivity revealed that efficiency-a measure of network interconnectedness-decreased as the number of stored trajectories increased. Additionally, the balance of excitation (E) and inhibition (I) shifted toward excitation during each unit's activation time, generating the prediction that observed sequential activity relies on dynamic control of the E/I balance. Our results establish for the first time that the same RNN can generate multiple functionally feedforward patterns of activity as a result of dynamic shifts in the E/I balance imposed by the connectome of the RNN. We conclude that recurrent network architectures account for sequential neural activity, as well as for a fundamental signature of timing behavior: Weber's law.

Loss of promoter IV-driven BDNF expression impacts oscillatory activity during sleep, sensory information processing and fear regulation.

Posttraumatic stress disorder is characterized by hyperarousal, sensory processing impairments, sleep disturbances and altered fear regulation; phenotypes associated with changes in brain oscillatory activity. Molecules associated with activity-dependent plasticity, including brain-derived neurotrophic factor (BDNF), may regulate neural oscillations by controlling synaptic activity. BDNF synthesis includes production of multiple Bdnf transcripts, which contain distinct 5' noncoding exons. We assessed arousal, sensory processing, fear regulation and sleep in animals where BDNF expression from activity-dependent promoter IV is disrupted (Bdnf-e4 mice). Bdnf-e4 mice display sensory hyper-reactivity and impaired electrophysiological correlates of sensory information processing as measured by event-related potentials (ERP). Utilizing electroencephalogram, we identified a decrease in slow-wave activity during non-rapid eye movement sleep, suggesting impaired sleep homeostasis. Fear extinction is controlled by hippocampal-prefrontal cortical BDNF signaling, and neurophysiological communication patterns between the hippocampus (HPC) and medial prefrontal cortex (mPFC) correlate with behavioral performance during extinction. Impaired fear extinction in Bdnf-e4 mice is accompanied by increased HPC activation and decreased HPC-mPFC theta phase synchrony during early extinction, as well as increased mPFC activation during extinction recall. These results suggest that activity-dependent BDNF signaling is critical for regulating oscillatory activity, which may contribute to altered behavior.