bims-climfi 2021-11-14 papers

Issue of 2021–11–14
three papers selected by
Jun Maruta, Mount Sinai Health System

Cell Rep. 2021 Nov 09. pii: S2211-1247(21)01445-5. [Epub ahead of print]37(6): 109966

Distributed sensory coding by cerebellar complex spikes in units of cortical segments.

Takayuki Michikawa, Takamasa Yoshida, Satoshi Kuroki, Takahiro Ishikawa, Shinji Kakei, Ryo Kimizuka, Atsushi Saito, Hideo Yokota, Akinobu Shimizu, Shigeyoshi Itohara, Atsushi Miyawaki.

Sensory processing is essential for motor control. Climbing fibers from the inferior olive transmit sensory signals to Purkinje cells, but how the signals are represented in the cerebellar cortex remains elusive. To examine the olivocerebellar organization of the mouse brain, we perform quantitative Ca2+ imaging to measure complex spikes (CSs) evoked by climbing fiber inputs over the entire dorsal surface of the cerebellum simultaneously. The surface is divided into approximately 200 segments, each composed of ∼100 Purkinje cells that fire CSs synchronously. Our in vivo imaging reveals that, although stimulation of four limb muscles individually elicits similar global CS responses across nearly all segments, the timing and location of a stimulus are derived by Bayesian inference from coordinated activation and inactivation of multiple segments on a single trial basis. We propose that the cerebellum performs segment-based, distributed-population coding that represents the conditional probability of sensory events.

Keywords: Bayesian inference; Purkinje cell; cerebellum; climbing fiber; fluorescence resonance energy transfer; genetically encoded calcium indicator; neural coding; olivocerebellar system; somatotopy; transgenic mouse

DOI: https://doi.org/10.1016/j.celrep.2021.109966

Nat Commun. 2021 Nov 09. 12(1): 6475

Mid-lateral cerebellar complex spikes encode multiple independent reward-related signals during reinforcement learning.

Naveen Sendhilnathan, Anna Ipata, Michael E Goldberg.

Although the cerebellum has been implicated in simple reward-based learning recently, the role of complex spikes (CS) and simple spikes (SS), their interaction and their relationship to complex reinforcement learning and decision making is still unclear. Here we show that in a context where a non-human primate learned to make novel visuomotor associations, classifying CS responses based on their SS properties revealed distinct cell-type specific encoding of the probability of failure after the stimulus onset and the non-human primate's decision. In a different context, CS from the same cerebellar area also responded in a cell-type and learning independent manner to the stimulus that signaled the beginning of the trial. Both types of CS signals were independent of changes in any motor kinematics and were unlikely to instruct the concurrent SS activity through an error based mechanism, suggesting the presence of context dependent, flexible, multiple independent channels of neural encoding by CS and SS. This diversity in neural information encoding in the mid-lateral cerebellum, depending on the context and learning state, is well suited to promote exploration and acquisition of wide range of cognitive behaviors that entail flexible stimulus-action-reward relationships but not necessarily motor learning.

DOI: https://doi.org/10.1038/s41467-021-26338-0

Cerebellum. 2021 Nov 10.

Gating by Memory: a Theory of Learning in the Cerebellum.

Mike Gilbert.

This paper presents a model of learning by the cerebellar circuit. In the traditional and dominant learning model, training teaches finely graded parallel fibre synaptic weights which modify transmission to Purkinje cells and to interneurons that inhibit Purkinje cells. Following training, input in a learned pattern drives a training-modified response. The function is that the naive response to input rates is displaced by a learned one, trained under external supervision. In the proposed model, there is no weight-controlled graduated balance of excitation and inhibition of Purkinje cells. Instead, the balance has two functional states-a switch-at synaptic, whole cell and microzone level. The paper is in two parts. The first is a detailed physiological argument for the synaptic learning function. The second uses the function in a computational simulation of pattern memory. Against expectation, this generates a predictable outcome from input chaos (real-world variables). Training always forces synaptic weights away from the middle and towards the limits of the range, causing them to polarise, so that transmission is either robust or blocked. All conditions teach the same outcome, such that all learned patterns receive the same, rather than a bespoke, effect on transmission. In this model, the function of learning is gating-that is, to select patterns that trigger output merely, and not to modify output. The outcome is memory-operated gate activation which operates a two-state balance of weight-controlled transmission. Group activity of parallel fibres also simultaneously contains a second code contained in collective rates, which varies independently of the pattern code. A two-state response to the pattern code allows faithful, and graduated, control of Purkinje cell firing by the rate code, at gated times.

Keywords: Cerebellum; Circuit; Hypothesis; Learning; Network; Synapse; Theory

DOI: https://doi.org/10.1007/s12311-021-01325-9