Basket cells are key GABAergic inhibitory interneurons that target the somata and proximal dendrites, enabling efficient control of the timing and rate of spiking of their postsynaptic targets. In all cortical circuits, there are two major types of basket cell that exhibit striking developmental, molecular, anatomical, and physiological differences. In this talk, I will discuss recent results that reveal the tightly coupled complementarity of these two key microcircuit regulatory modules, demonstrating a novel form of brain-state-specific segregation of inhibition during spontaneous behavior, with implications for the assessment of dysregulated inhibition in neurological disorders. In addition, we will discuss recent experimental and computational modeling insights from a collaborative project with the Losonczy lab at Columbia that predict key roles for interneurons in the selective suppression of cue-responsive cells during hippocampal ripples related to episodic memory replay.
Hearing perception relies on our ability to tell apart the spectral content of different sounds, and to learn to use this difference to distinguish behaviorally relevant (such as dangerous and safe) sounds. However, the neuronal circuits that underlie this modulation remain unknown. In the auditory cortex, the excitatory neurons serve the dominant function in transmitting information about the sensory world within and across brain areas, whereas inhibitory interneurons carry a range of modulatory functions, shaping the way information is represented and processed. I will discuss the results of our recent studies that elucidate the function of neuronal populations in sound encoding and perception. First, we found that the most common class of inhibitory neurons modulate frequency selectivity of excitatory neurons in the auditory cortex and regulate frequency discrimination acuity and specificity of discriminative auditory associative learning. Our results demonstrate that cortical inhibition can improve or impair acuity of innate and learned auditory behaviors. Second, we found that another class of inhibitory neurons regulate adaptation in the auditory cortex to frequent sounds, in a stimulus-specific fashion. By selectively reducing responses to frequently, but not rarely, occurring sounds, auditory cortical neurons enhance the brain's ability to detect unexpected events through stimulus-specific adaptation. The role of these inhibitory neurons extends to other forms of adaptation to temporal regularities. Third, I will discuss a computational study that integrates results from our and other laboratories into a unifying model of the auditory cortex. These results expand our understanding of how specific cortical circuits contribute to auditory perception in everyday acoustic environments.
Electrophysiological recordings in the hippocampus have revealed a tight control of inhibitory interneurons over the Granule Cells (GCs) of the Dentate Gyrus (DG). In experiments of Long-Term Potentiation (LTP) of the Perforant Pathway in vivo, we show that together with the expected potentiation of glutamatergic synapses, a reduction of the feed-forward inhibitory activity also occurs, facilitating activity propagation in the circuit. To further investigate this phenomenon, we built a population model where neurons were described by Izhikevich's equations. The results obtained from the numerical integration of the model equations, before and after LTP induction, support the counterintuitive experimental observation of synaptic depression in the feed-forward inhibitory connection after LTP induction. We find that LTP increases the efficiency of the glutamatergic input to recruit the inhibitory network of the hilar region, resulting in an average reduction of the basket cell population activity. The predictions of the model were supported by electrophysiological recordings in mice, in an in vitro preparation of intracellular patch-clamp recordings after in vivo LTP induction. Our models predict that the functional reorganization induced by LTP also improves the temporal coding of GCs and the information transmission from the entorhinal cortex to the CA3.
Systems consolidation refers to the reorganization of memory over time across brain regions. Despite recent advancements in unravelling engrams and circuits essential for this process, the exact mechanisms behind engram cell dynamics and the role of associated pathways remain poorly understood. Here, we propose a computational model to address this knowledge gap that consists of a multi-region spiking recurrent neural network subject to biologically-plausible synaptic plasticity mechanisms. By coordinating the timescales of synaptic plasticity throughout the network and incorporating a hippocampus-thalamus-cortex circuit, our model is able to couple engram reactivations across these brain regions and thereby reproduce key dynamics of cortical and hippocampal engram cells along with their interdependencies. Decoupling hippocampal-thalamic-cortical activity disrupts engram dynamics and systems consolidation. Our modeling work also yields several testable predictions: engram cells in mediodorsal thalamus are activated in response to partial cues in recent and remote recall and are crucial for systems consolidation; hippocampal and thalamic engram cells are essential for coupling engram reactivations between subcortical and cortical regions; inhibitory engram cells have region-specific dynamics with coupled reactivations; inhibitory input to mediodorsal thalamus is critical for systems consolidation; and thalamocortical synaptic coupling is predictive of cortical engram dynamics and the retrograde amnesia pattern induced by hippocampal damage. Overall, our results suggest that systems consolidation emerges from concerted interactions among engram cells in distributed brain regions enabled by coordinated synaptic plasticity timescales in multisynaptic subcortical-cortical circuits.
High Frequency Oscillations (HFOs) have been researched for over 20 years. Originally discovered in normal brain tissue, later work found they were more prevalent in epileptic tissue, leading to many clinical studies attempting to utilize them as epilepsy biomarkers. One of the primary challenges in HFO research is determining whether a recorded event represents normal or pathological neural function. One prevailing theory is that epileptic HFOs are predominantly caused by pyramidal cell firing, while “normal” HFOs are produced by inhibitory post synaptic potentials. Using a realistic computational model containing pyramidal neurons and basket cells, we determined the signal characteristics over a range of parameters. We found that fast ripples (HFOs > 250 Hz) could only reliably be generated by pyramidal cell firing. Surprisingly, these oscillations occurred even in a completely uncoupled network. We then proved mathematically that, in networks of strongly activated, structurally similar neurons, emergent oscillations are not only possible but expected. This result suggests that pathological HFOs may not require any specific network structure, but instead are simply a property of highly active neurons that have been disconnected from normal inhibitory feedback connections such as is commonly seen in epileptic tissue.