An emergent temporal basis set robustly supports cerebellar time-series learning.

The cerebellum is considered a "learning machine" essential for time interval estimation underlying motor coordination and other behaviors. Theoretical work has proposed that the cerebellum's input recipient structure, the granule cell layer (GCL), performs pattern separation of inputs that facilitates learning in Purkinje cells (P-cells). However, the relationship between input reformatting and learning has remained debated, with roles emphasized for pattern separation features from sparsification to decorrelation. We took a novel approach by training a minimalist model of the cerebellar cortex to learn complex time-series data from time-varying inputs, typical during movements. The model robustly produced temporal basis sets from these inputs, and the resultant GCL output supported better learning of temporally complex target functions than mossy fibers alone. Learning was optimized at intermediate threshold levels, supporting relatively dense granule cell activity, yet the key statistical features in GCL population activity that drove learning differed from those seen previously for classification tasks. These findings advance testable hypotheses for mechanisms of temporal basis set formation and predict that moderately dense population activity optimizes learning.NEW & NOTEWORTHY During movement, mossy fiber inputs to the cerebellum relay time-varying information with strong intrinsic relationships to ongoing movement. Are such mossy fibers signals sufficient to support Purkinje signals and learning? In a model, we show how the GCL greatly improves Purkinje learning of complex, temporally dynamic signals relative to mossy fibers alone. Learning-optimized GCL population activity was moderately dense, which retained intrinsic input variance while also performing pattern separation.

Monosynaptic inputs to specific cell types of the intermediate and deep layers of the superior colliculus.

The intermediate and deep layers of the midbrain superior colliculus (SC) are a key locus for several critical functions, including spatial attention, multisensory integration, and behavioral responses. While the SC is known to integrate input from a variety of brain regions, progress in understanding how these inputs contribute to SC-dependent functions has been hindered by the paucity of data on innervation patterns to specific types of SC neurons. Here, we use G-deleted rabies virus-mediated monosynaptic tracing to identify inputs to excitatory and inhibitory neurons of the intermediate and deep SC. We observed stronger and more numerous projections to excitatory than inhibitory SC neurons. However, a subpopulation of excitatory neurons thought to mediate behavioral output received weaker inputs, from far fewer brain regions, than the overall population of excitatory neurons. Additionally, extrinsic inputs tended to target rostral excitatory and inhibitory SC neurons more strongly than their caudal counterparts, and commissural SC neurons tended to project to similar rostrocaudal positions in the other SC. Our findings support the view that active intrinsic processes are critical to SC-dependent functions, and will enable the examination of how specific inputs contribute to these functions.

Between-ear sound frequency disparity modulates a brain stem biomarker of binaural hearing.

The auditory brain stem response (ABR) is an evoked potential that indexes a cascade of neural events elicited by sound. In the present study we evaluated the influence of sound frequency on a derived component of the ABR known as the binaural interaction component (BIC). Specifically, we evaluated the effect of acoustic interaural (between-ear) frequency mismatch on BIC amplitude. Goals were to 1) increase basic understanding of sound features that influence this long-studied auditory potential and 2) gain insight about the persistence of the BIC with interaural electrode mismatch in human users of bilateral cochlear implants, presently a limitation on the prospective utility of the BIC in audiological settings. Data were collected in an animal model that is audiometrically similar to humans, the chinchilla (Chinchilla lanigera; 6 females). Frequency disparities and amplitudes of acoustic stimuli were varied over broad ranges, and associated variation of BIC amplitude was quantified. Subsequently, responses were simulated with the use of established models of the brain stem pathway thought to underlie the BIC. Collectively, the data demonstrate that at high sound intensities (≥85 dB SPL), the acoustically elicited BIC persisted with interaurally disparate stimulation (click frequencies ≥1.5 octaves apart). However, sharper tuning emerged at moderate sound intensities (65 dB SPL), with the largest BIC occurring for stimulus frequencies within ~0.8 octaves, equivalent to ±1 mm in cochlear place. Such responses were consistent with simulated responses of the presumed brain stem generator of the BIC, the lateral superior olive. The data suggest that leveraging focused electrical stimulation strategies could improve BIC-based bilateral cochlear implant fitting outcomes.NEW & NOTEWORTHY Traditional hearing tests evaluate each ear independently. Diagnosis and treatment of binaural hearing dysfunction remains a basic challenge for hearing clinicians. We demonstrate in an animal model that the prospective utility of a noninvasive electrophysiological signature of binaural function, the binaural interaction component (BIC), depends strongly on the intensity of auditory stimulation. Data suggest that more informative BIC measurements could be obtained with clinical protocols leveraging stimuli restricted in effective bandwidth.

Pathway-Specific Drive of Cerebellar Golgi Cells Reveals Integrative Rules of Cortical Inhibition.

Cerebellar granule cells (GrCs) constitute over half of all neurons in the vertebrate brain and are proposed to decorrelate convergent mossy fiber (MF) inputs in service of learning. Interneurons within the GrC layer, Golgi cells (GoCs), are the primary inhibitors of this vast population and therefore play a major role in influencing the computations performed within the layer. Despite this central function for GoCs, few studies have directly examined how GoCs integrate inputs from specific afferents, which vary in density to regulate GrC population activity. We used a variety of methods in mice of either sex to study feedforward inhibition recruited by identified MFs, focusing on features that would influence integration by GrCs. Comprehensive 3D reconstruction and quantification of GoC axonal boutons revealed tightly clustered boutons that focus feedforward inhibition in the neighborhood of GoC somata. Acute whole-cell patch-clamp recordings from GrCs in brain slices showed that, despite high GoC bouton density, fast phasic inhibition was very sparse relative to slow spillover mediated inhibition. Dynamic-clamp simulating inhibition combined with optogenetic MF activation at moderate rates supported a predominant role of slow spillover mediated inhibition in reducing GrC activity. Whole-cell recordings from GoCs revealed a role for the density of active MFs in preferentially driving them. Thus, our data provide empirical confirmation of predicted rules by which MFs activate GoCs to regulate GrC activity levels.SIGNIFICANCE STATEMENT A unifying framework in neural circuit analysis is identifying circuit motifs that subserve common computations. Wide-field inhibitory interneurons globally inhibit neighbors and have been studied extensively in the insect olfactory system and proposed to serve pattern separation functions. Cerebellar Golgi cells (GoCs), a type of mammalian wide-field inhibitory interneuron observed in the granule cell layer, are well suited to perform normalization or pattern separation functions, but the relationship between spatial characteristics of input patterns to GoC-mediated inhibition has received limited attention. This study provides unprecedented quantitative structural details of GoCs and identifies a role for population input activity levels in recruiting inhibition using in vitro electrophysiology and optogenetics.

Theoretically Sparse, Empirically Dense: New Views on Cerebellar Granule Cells.

Cerebellar granule cells are a popular target of neuroanatomical hyperbole, being so small and so numerous. Early theorists proposed unique roles for this vast cell population, ideas that continue to be tested through contemporary approaches. In 2017, a cluster of empirical and theoretical papers offered a fresh and singular look into the functions of granule cells and the computational advantages of their idiosyncratic circuit organization.

Morphological Constraints on Cerebellar Granule Cell Combinatorial Diversity.

Combinatorial expansion by the cerebellar granule cell layer (GCL) is fundamental to theories of cerebellar contributions to motor control and learning. Granule cells (GrCs) sample approximately four mossy fiber inputs and are thought to form a combinatorial code useful for pattern separation and learning. We constructed a spatially realistic model of the cerebellar GCL and examined how GCL architecture contributes to GrC combinatorial diversity. We found that GrC combinatorial diversity saturates quickly as mossy fiber input diversity increases, and that this saturation is in part a consequence of short dendrites, which limit access to diverse inputs and favor dense sampling of local inputs. This local sampling also produced GrCs that were combinatorially redundant, even when input diversity was extremely high. In addition, we found that mossy fiber clustering, which is a common anatomical pattern, also led to increased redundancy of GrC input combinations. We related this redundancy to hypothesized roles of temporal expansion of GrC information encoding in service of learned timing, and we show that GCL architecture produces GrC populations that support both temporal and combinatorial expansion. Finally, we used novel anatomical measurements from mice of either sex to inform modeling of sparse and filopodia-bearing mossy fibers, finding that these circuit features uniquely contribute to enhancing GrC diversification and redundancy. Our results complement information theoretic studies of granule layer structure and provide insight into the contributions of granule layer anatomical features to afferent mixing.SIGNIFICANCE STATEMENT Cerebellar granule cells are among the simplest neurons, with tiny somata and, on average, just four dendrites. These characteristics, along with their dense organization, inspired influential theoretical work on the granule cell layer as a combinatorial expander, where each granule cell represents a unique combination of inputs. Despite the centrality of these theories to cerebellar physiology, the degree of expansion supported by anatomically realistic patterns of inputs is unknown. Using modeling and anatomy, we show that realistic input patterns constrain combinatorial diversity by producing redundant combinations, which nevertheless could support temporal diversification of like combinations, suitable for learned timing. Our study suggests a neural substrate for producing high levels of both combinatorial and temporal diversity in the granule cell layer.