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Schnitzer, M.

Publications and source records attributed to Schnitzer, M..

3 recordsLinked to original sources

The Relationship between Birth Timing, Circuit Wiring, and Physiological ResponseProperties of Cerebellar Granule Cells

Cerebellar granule cells (GrCs) are usually regarded as a uniform cell type that collectively expands the coding space of the cerebellum by integrating diverse combinations of mossy fiber inputs. Accordingly, stable molecularly or physiologically defined GrC subtypes within a single cerebellar region have not been reported. The only known cellular properties that distinguishes otherwise homogeneous GrCs is the correspondence between GrC birthtime and the depth of the molecular layer to which their axons (parallel fibers) project. To determine the role birth timing plays in GrC wiring and function, we developed genetic strategies to access early- and late-born GrCs. We initiated retrograde monosynaptic rabies virus tracing from control, early-born, and late-born GrCs, revealing the different patterns of mossy fiber input to GrCs in vermis lobule 6 and simplex, as well as to early- and late-born GrCs of vermis lobule 6: sensory and motor nuclei provide more input to early-born GrCs, while basal pontine and cerebellar nuclei provide more input to late-born GrCs. In vivo multi-depth 2-photon Ca2+ imaging of parallel fibers of early- and late-born GrCs revealed representations of diverse task variables and stimuli by both populations, with differences in the proportions of parallel fibers encoding movement, reward anticipation, and reward consumption. Our results suggest neither organized parallel processing nor completely random organization of mossy fiber[->]GrC circuitry, but instead a moderate influence of birth timing on GrC wiring and encoding. Our imaging data also suggest that GrCs can represent general aversiveness, in addition to recently described reward representations. Significance StatementCerebellar granule cells (GrCs) comprise the majority of all neurons in the mammalian brain and are usually regarded as a uniform cell type. However, the birth timing of an individual GrC dictates where its axon projects. Using viral-genetic techniques, we find that early- and late-born GrCs receive different proportions of inputs from the same set of input regions. Using in vivo multi-depth 2-photon Ca2+ imaging of axons of early- and late-born GrCs, we found that both populations represent diverse task variables and stimuli, with differences in the proportions of axons in encoding of a subset of movement and reward parameters. These results indicate that birth timing contributes to the input selection and physiological response properties of GrCs.

neuroscience↗

Olfactory Landmarks and Path Integration Converge to Form a Cognitive Spatial Map

The convergence of internal path integration with sensory information from external landmarks generates a cognitive spatial map in the hippocampus. We have recorded the activity of cells in CA1 during a virtual navigation task to examine how mice represent, recognize and employ sparse olfactory landmarks to estimate their location. We observe that the presence of odor landmarks at multiple locations in a virtual environment greatly enriches the place cell representation and dramatically improves navigation. Presentation of the same odor at different locations generates distinct place cell representations, indicating that path integration can disambiguate two identical cues on the basis of location. The enhanced place cell representation at one cue location led to the formation of place cells at locations beyond that cue and, ultimately recognition of a second odor cue as a distinct landmark. This suggests an iterative mechanism for extending place cell representations into unknown territory. These results reveal how odor cues can serve as landmarks to guide navigation and suggest a model to explain how the convergence of landmarks and path integration participates in an iterative process that generates a cognitive spatial map.

neuroscience↗

Unsupervised discovery of demixed, low-dimensional neural dynamics across multiple timescales through tensor components analysis

Perceptions, thoughts and actions unfold over millisecond timescales, while learned behaviors can require many days to mature. While recent experimental advances enable large-scale and long-term neural recordings with high temporal fidelity, it remains a formidable challenge to extract unbiased and interpretable descriptions of how rapid single-trial circuit dynamics change slowly over many trials to mediate learning. We demonstrate a simple tensor components analysis (TCA) can meet this challenge by extracting three interconnected low dimensional descriptions of neural data: neuron factors, reflecting cell assemblies; temporal factors, reflecting rapid circuit dynamics mediating perceptions, thoughts, and actions within each trial; and trial factors, describing both long-term learning and trial-to-trial changes in cognitive state. We demonstrate the broad applicability of TCA by revealing insights into diverse datasets derived from artificial neural networks, large-scale calcium imaging of rodent prefrontal cortex during maze navigation, and multielectrode recordings of macaque motor cortex during brain machine interface learning.

neuroscience↗