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Here, we propose a model for the mechanisms that underlie neuron responses in the auditory cortex. This study focuses on a cortical circuit involving excitatory and inhibitory (parvalbumin) neurons. Using physiologically relevant parameters in the proposed model network, we show that we can recreate observed results in live studies. 

Little is known about how populations of neurons within cortical circuits encode sensory stimuli in the presence of competing stimuli at other spatial locations. Here, we investigate this problem in auditory cortex using a recently proposed information-theoretic approach. We find a small subset of neurons nearly maximizes information about target sounds in the presence of competing maskers, approaching information levels for isolated stimuli, and provides a noise-robust code for sounds in a complex auditory scene. 

Abstract Hippocampal network activity at theta frequencies (5-10Hz) is important for behavior. However, it remains unclear how behaviorally-relevant network theta rhythms arise and interact with cellular dynamics to dictate spike timing. We performed membrane voltage (Vm) imaging of individual CA1 pyramidal cells and parvalbumin interneurons with simultaneous local field potential (LFP) recordings in mice during locomotion. We found that Vm theta rhythms organize spike timing in both cell types regardless of behavioral conditions, but the Vm of parvalbumin interneurons is better synchronized with LFP. The temporal relationships between spikes and LFP theta reliably reflect the Vm-LFP relationships in parvalbumin cells, but not in pyramidal cells. Thus, cellular theta rhythms broadly organize spike timing in CA1 neurons, and parvalbumin interneurons are critical in coordinating network theta rhythms. One-Sentence SummaryCellular membrane voltage of parvalbumin interneurons organizes spiking and network dynamics in the hippocampus. 

Theta and gamma rhythms and their cross-frequency coupling play critical roles in perception, attention, learning, and memory. Available data suggest that forebrain acetylcholine (ACh) signaling promotes theta-gamma coupling, although the mechanism has not been identified. Recent evidence suggests that cholinergic signaling is both temporally and spatially constrained, in contrast to the traditional notion of slow, spatially homogeneous, and diffuse neuromodulation. Here, we find that spatially constrained cholinergic stimulation can generate theta-modulated gamma rhythms. Using biophysically-based excitatory-inhibitory (E-I) neural network models, we simulate the effects of ACh on neural excitability by varying the conductance of a muscarinic receptor-regulated K + current. In E-I networks with local excitatory connectivity and global inhibitory connectivity, we demonstrate that theta-gamma-coupled firing patterns emerge in ACh modulated network regions. Stable gamma-modulated firing arises within regions with high ACh signaling, while theta or mixed theta-gamma activity occurs at the peripheries of these regions. High gamma activity also alternates between different high-ACh regions, at theta frequency. Our results are the first to indicate a causal role for spatially heterogenous ACh signaling in the emergence of localized theta-gamma rhythmicity. Our findings also provide novel insights into mechanisms by which ACh signaling supports the brain region-specific attentional processing of sensory information. 

Trace conditioning and extinction learning depend on the hippocampus, but it remains unclear how neural activity in the hippocampus is modulated during these two different behavioral processes. To explore this question, we performed calcium imaging from a large number of individual CA1 neurons during both trace eye-blink conditioning and subsequent extinction learning in mice. Our findings reveal that distinct populations of CA1 cells contribute to trace conditioned learning versus extinction learning, as learning emerges. Furthermore, we examined network connectivity by calculating co-activity between CA1 neuron pairs and found that CA1 network connectivity patterns also differ between conditioning and extinction, even though the overall connectivity density remains constant. Together, our results demonstrate that distinct populations of hippocampal CA1 neurons, forming different sub-networks with unique connectivity patterns, encode different aspects of learning.