Multimodal sensory integration in single cerebellar granule cells in vivo

By T. Ishikawa, M. Shimuta, Michael Häusser. 2015

144 citations recorded by the source corpus.

The mammalian cerebellum is a highly multimodal structure, receiving inputs from multiple sensory modalities and integrating them during complex sensorimotor coordination tasks. Previously, using cell-type-specific anatomical projection mapping, it was shown that multimodal pathways converge onto individual cerebellar granule cells (Huang et al., 2013). Here we directly measure synaptic currents using in vivo patch-clamp recordings and confirm that a subset of single granule cells receive convergent functional multimodal (somatosensory, auditory, and visual) inputs via separate mossy fibers. Furthermore, we show that the integration of multimodal signals by granule cells can enhance action potential output. These recordings directly demonstrate functional convergence of multimodal signals onto single granule cells. DOI: http://dx.doi.org/10.7554/eLife.12916.001

Subfields: Cognitive Neuroscience, Neurology, Sensory Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. N. A. Cayco-Gajic
2. R. Silver
3. Mark S. Goldman
4. J. Gilmer
5. Abigail L. Person
6. Liqun Luo
7. Surya Ganguli
8. Mark J. Schnitzer

In Neurology

1. N. A. Cayco-Gajic
2. R. Silver
3. J. Gilmer
4. Abigail L. Person
5. Surya Ganguli

In Sensory Systems

1. J. Gilmer
2. Abigail L. Person
3. N. A. Cayco-Gajic
4. Liqun Luo
5. Jonathan Kadmon
6. R. Silver

Citations in the corpus

• Re-evaluating circuit mechanisms underlying pattern separation (2019, positive): “…the past decade, anatomical (Huang et al., 2013) and physiological evidence (Proville et al., 2014) has accumulated for mixed representations generated through multimodal integration in single granule cells of the cerebellar cortex (Arenz et al., 2008; Chabrol et al., 2015; Ishikawa et al., 2015).”
• Population-scale organization of cerebellar granule neuron signaling during a visuomotor behavior (2017, background): “…facilitating learning (Marr, 1969; Albus, 1971; Sejnowski, 1977; Fujita, 1982; Dean and Porrill, 2008), and support for expansion recoding has been found in electric fish (Sawtell, 2010; Kennedy et al., 2014) and rodents (Huang et al., 2013; Chabrol et al., 2015; Ishikawa et al., 2015).”
• Computational Principles of Supervised Learning in the Cerebellum (2018, unknown): “Although cerebellar neurons and synapses have nonlinear intrinsic properties (Hoebeek et al. 2010, Ishikawa et al. 2015, Jahnsen 1986, Loewenstein et al. 2005, Person & Raman 2011), these properties are disengaged or compensated in ways that keep the cerebellar network operating in a linear regime…”
• Shared cortex-cerebellum dynamics in the execution and learning of a motor task (2019, background): “…(2) the relative strength of each MF input to a GrC (whether some inputs or classes of inputs are more effective at driving spiking than others), and (3) the degree of clustering of similar types of MFs in individual GrCs (Ishikawa et al., 2015) (e.g., by selective wiring during development).”
• Morphological Constraints on Cerebellar Granule Cell Combinatorial Diversity (2017, methodology): “Based on the temporal dynamics of GrCs and the limit of learned timing in delay eyelid conditioning, we modeled GrC activity such that each GrC was active for a 20 ms epoch (GrCresponse) over a 500 ms window (trespmax) (Schneiderman and Gormezano, 1964; Smith et al., 1969; Ishikawa et al., 2015).”