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Cortical representations supporting many cognitive abilities emerge from underlying circuits comprised of several different cell types. However, cell type-specific contributions to rate and timing-based cortical coding are not well-understood. Here, we investigated the role of parvalbumin neurons in cortical complex scene analysis. Many complex scenes contain sensory stimuli which are highly dynamic in time and compete with stimuli at other spatial locations. Parvalbumin neurons play a fundamental role in balancing excitation and inhibition in cortex and sculpting cortical temporal dynamics; yet their specific role in encoding complex scenes via timing-based coding, and the robustness of temporal representations to spatial competition, has not been investigated. Here, we address these questions in auditory cortex of mice using a cocktail party-like paradigm, integrating electrophysiology, optogenetic manipulations, and a family of spike-distance metrics, to dissect parvalbumin neurons’ contributions towards rate and timing-based coding. We find that suppressing parvalbumin neurons degrades cortical discrimination of dynamic sounds in a cocktail party-like setting via changes in rapid temporal modulations in rate and spike timing, and over a wide range of time-scales. Our findings suggest that parvalbumin neurons play a critical role in enhancing cortical temporal coding and reducing cortical noise, thereby improving representations of dynamic stimuli in complex scenes.

 

Rhythmic neural network activity has been broadly linked to behavior. However, it is unclear how membrane potentials of individual neurons track behavioral rhythms, even though many neurons exhibit pace-making properties in isolated brain circuits. To examine whether single-cell voltage rhythmicity is coupled to behavioral rhythms, we focused on delta-frequencies (1–4 Hz) that are known to occur at both the neural network and behavioral levels. We performed membrane voltage imaging of individual striatal neurons simultaneously with network-level local field potential recordings in mice during voluntary movement. We report sustained delta oscillations in the membrane potentials of many striatal neurons, particularly cholinergic interneurons, which organize spikes and network oscillations at beta-frequencies (20–40 Hz) associated with locomotion. Furthermore, the delta-frequency patterned cellular dynamics are coupled to animals’ stepping cycles. Thus, delta-rhythmic cellular dynamics in cholinergic interneurons, known for their autonomous pace-making capabilities, play an important role in regulating network rhythmicity and movement patterning.

 

The use of secondary ion mass spectrometry (SIMS) to perform micrometer‐scalein situcarbon isotope (δ¹³C) analyses of shells of marine microfossils called planktic foraminifers holds promise to explore calcification and ecological processes. The potential of this technique, however, cannot be realized without comparison to traditional whole‐shell δ¹³C values measured by gas source mass spectrometry (GSMS).

Paired SIMS and GSMS δ¹³C values measured from final chamber fragments of the same shell of the planktic foraminiferOrbulina universaare compared. The SIMS–GSMS δ¹³C differences (Δ¹³C_SIMS‐GSMS) were determined via paired analysis of hydrogen peroxide‐cleaned fragments of modern cultured specimens and of fossil specimens from deep‐sea sediments that were either untreated, sonicated, and cleaned with hydrogen peroxide or vacuum roasted. After treatment, fragments were analyzed by a CAMECA IMS 1280 SIMS instrument and either a ThermoScientific MAT‐253 or a Fisons Optima isotope ratio mass spectrometer (GSMS).

Paired analyses of cleaned fragments of cultured specimens (n = 7) yield no SIMS–GSMS δ¹³C difference. However, paired analyses of untreated (n = 18) and cleaned (n = 12) fragments of fossil shells yield average Δ¹³C_SIMS‐GSMSvalues of 0.8‰ and 0.6‰ (±0.2‰, 2 SE), respectively, while vacuum roasting of fossil shell fragments (n = 11) removes the SIMS–GSMS δ¹³C difference.

The noted Δ¹³C_SIMS‐GSMSvalues are most likely due to matrix effects causing sample–standard mismatch for SIMS analyses but may also be a combination of other factors such as SIMS measurement of chemically bound water. The volume of material analyzed via SIMS is ~10⁵times smaller than that analyzed by GSMS; hence, the extent to which these Δ¹³C_SIMS‐GSMSvalues represent differences in analyte or instrument factors remains unclear.

 

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.