Electrical coupling controls dimensionality and chaotic firing of inferior olive neurons

Abstract

We previously proposed, on theoretical grounds, that the cerebellum must regulate the dimensionality of its neuronal activity during motor learning and control to cope with the low firing frequency of inferior olive neurons, which form one of two major inputs to the cerebellar cortex. Such dimensionality regulation is possible via modulation of electrical coupling through the gap junctions between inferior olive neurons by inhibitory GABAergic synapses. In addition, we previously showed in simulations that intermediate coupling strengths induce chaotic firing of inferior olive neurons and increase their information carrying capacity. However, there is no in vivo experimental data supporting these two theoretical predictions. Here, we computed the levels of synchrony, dimensionality, and chaos of the inferior olive code by analyzing in vivo recordings of Purkinje cell complex spike activity in three different coupling conditions: carbenoxolone (gap junctions blocker), control, and picrotoxin (GABA-A receptor antagonist). To examine the effect of electrical coupling on dimensionality and chaotic dynamics, we first determined the physiological range of effective coupling strengths between inferior olive neurons in the three conditions using a combination of a biophysical network model of the inferior olive and a novel Bayesian model averaging approach. We found that effective coupling co-varied with synchrony and was inversely related to the dimensionality of inferior olive firing dynamics, as measured via a principal component analysis of the spike trains in each condition. Furthermore, for both the model and the data, we found an inverted U-shaped relationship between coupling strengths and complexity entropy, a measure of chaos for spiking neural data. These results are consistent with our hypothesis according to which electrical coupling regulates the dimensionality and the complexity in the inferior olive neurons in order to optimize both motor learning and control of high dimensional motor systems by the cerebellum.

Dynamic organization of cerebellar climbing fiber response and synchrony in multiple functional modules reduces dimensions for reinforcement learning

“…One may also expect that there are no common inputs across TC populations so that the electrical couplings remain decoupled among them. Together, as indicated by previous theoretical and experimental studies 15, 33, 39, 41, 46, 83, 84 , these results suggest that common inputs and positive feedback to IO cells are the key structures that organize neuron populations, induce changes in their cue responsiveness, resulting in changed behavior. Because synchronization within components is larger than synchronization across components and anatomical zones, and because even individual neurons multiplex several components, we suggest that components self-organize 85–87 as a result of interaction between electrical synapses in the IO and positive feedback and lateral inhibition implemented by loop dynamics between PC, CN, and IO (Fig 7A).…”

Designing AAV Vectors for Monitoring the Subtle Calcium Fluctuations of Inferior Olive Network in vivo

“…Instead, to allow comparison between varying experimental conditions and event amplitudes, we present the fluorescence intensities in units of power (fW), estimated based on photo-conversion properties of camera sensors (using a window between dark noise and full electron-saturated pixel). Obviously, future studies may employ advanced mathematical approaches (such as Hoang et al, 2020 ) to increase precision of desired measurements, but they need to be carefully designed for the question at hand.…”

“…Despite several works having employed a generic targeting approach for IO (White and Sillitoe, 2017 ; Rowan et al, 2018 ; Gaffield et al, 2019 ; González-Calvo et al, 2021 ), no systematic effort has been made to create a more specific targeting tool. Instead, experimenters have resorted to indirect methods, such as imaging IO-axon-evoked spikes in the cerebellum (Ju et al, 2019 ; Hoang et al, 2020 ; Roh et al, 2020 ; Michikawa et al, 2021 ), optogenetic modulation of IO afferents instead of IO neurons (Kim et al, 2020 ), limiting experimentation to cerebellar cortical regions where off-target transfection of axons with, e.g., the CamKII promoter is not problematic (Mathews et al, 2012 ; Gaffield et al, 2019 ) or morphological analysis (Nishiyama et al, 2007 ; Pätz et al, 2018 ; Vrieler et al, 2019 ; González-Calvo et al, 2021 ). To our knowledge, there have been no published reports of in situ, in vivo multi-cell IO network activity besides our recently-published preliminary observations (Guo et al, 2021 ).…”

Pathophysiology of Cerebellar Tremor: The Forward Model-Related Tremor and the Inferior Olive Oscillation-Related Tremor

“…For instance, a selective disruption of the long loop disorganizes the online operation of cerebellar forward model and leads to manifestation of irregular tremors, including kinetic tremor in Holmes' classic study and intention tremor, when the dysfunction exceeds a threshold. We also hypothesize that the disruption of the short loop (i.e., removal of inhibition on the gap junctions between IO neurons) shifts IO activities toward the synchronous mode like a local injection of bicuculine into IO ( 85 ) to cause regular tremors such as regular postural tremor in Holmes' classic study.…”

“…Therefore, CS activities are corrupted by increased noise (i.e., low S/N ratio) during regular tremors. Second, Hoang et al ( 85 ) recently found that high coupling strengths of IO neurons induce their synchronous firing and decrease the amount of information encoded by firing dynamics of IO neurons. The two mechanisms may gradually deteriorate the forward model and increase its prediction error, resulting in irregular tremor.…”