The state transition of the neural circuit behind the proficient behavior of Cell

Written | Qi Inspired by the phase transitions of physical matter, theoretical neuroscientists have thoroughly studied the neural network models between different dynamic states
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State transitions are usually between ordered or coherent activity states and disordered activity states [1].
Therefore, it is important to determine state transitions and how they reshape cell activity and animal behavior
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Specifically, in the motor system, the active regulation and internal synchronization of neural components may help coordinate muscles to complete specific actions
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This view believes that there should be a loop that can adjust its internal dynamic cohesion to serve motion control
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Neurons in the lower olive nucleus have subthreshold voltage oscillations and electrical synapses that synchronize these rhythms.
The resulting synchronous discharges drive synchronous complex discharges in the Purkinje cells of the cerebellum.
Each cell starts from a climbing fiber of the olive nucleus.
Receiving effective input can evoke complex discharges when activated, and the inhibitory feedback from the cerebellum may regulate the electrical coupling and synchronous discharge behavior of olive cells
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The synchronous and complex discharges of Purkinje cells are accompanied by rhythmic motor behavior, but it is unknown whether this represents a transition to a coherent dynamical state that coordinates skilled movements
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It should be noted that the existence of synchronous activity does not mean switching from a discrete state to a coherent state
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For example, when a car accelerates, the wheels and engine components run faster, but this in itself does not mean that the existence of a transmission will suddenly change the basic time relationship of the interaction of mechanical components
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Therefore, the rhythmic discharge of Purkinje cells may only reflect the rhythmic input of motion lock, rather than state transition.

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In sports neuroscience, state changes are assumed to be time-locking the neural components that coordinate complex movements, but there is still little evidence for this
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On July 1, 2021, the team of Mark J.
Schnitzer and Mark J.
Wagner from Stanford University published an article in Cell magazine entitled A neural circuit state change underlying skilled movements.
In this study, the author passed The target arm task test imaged the complex firings of Purkinje neurons in the mouse cerebellum, and proved whether the change from autonomous firing to coherent firing is the basis for skilled exercise
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First, the researchers trained the mice to use their right front paws to point the handle from the starting position close to the body to the target area.
The rewards are 1s after reaching the target position.
They often deviate from the target in the early training period, but after training, the error rate is obvious Decline (see Figure 1)
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Subsequently, in order to examine Purkinje cell dynamics, the researchers used a 16 laser beam two-photon mid-view mirror to photograph a 4-mm2 cerebellar vermis region, most of which received afferents from the ipsilateral forelimb movement and were activated
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As the training progresses, the dynamics of Purkinje cells are more related to the dynamics of other cells than 1 mm.
The temporal and spatial consistency of complex firing activities increases with training, which means that the synchronization of Purkinje cells can reflect The proficiency of training
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Figure 1.
Schematic diagram of the target arm task.
In the test, it can be observed that the synchronous discharge phenomenon occurs at the beginning of the arm extension, but during the test there is also a neural silence period of about 200-300ms in the entire visual field, which the researchers call it "Coordinated Silence"
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In order to determine whether the coordinated firing and silence reflect inherent synchronization, or whether the cells have similar but independently regulated firing rates, the researchers randomly disturbed the cell’s activity trajectory throughout the experiment.
This "shuffle" is to distinguish between task-driven and The established method of internal time lock synchronization [2], after analysis, it is concluded that the coherent dynamics in the original data have inherent synchronization
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Considering that the olive nucleus-cerebellum transforms into a coherent state during the skillful arm extension training, the author focused on the heterogeneous feedback from the deep cerebellar nucleus to the olive nucleus.
This approach may regulate the synchronization of the olive nucleus discharge.
Thereby regulating the synchronicity of the complex discharge of Purkinje cells [3], but the role of this feedback in autonomous movement is unclear
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To this end, the author expressed an opsin in the olive projection cells of the cerebellar nucleus to activate or inhibit this pathway through light
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In a randomized experiment, the author continued to irradiate the cerebellar fastigial nucleus from the beginning of the arm extension to after the completion.
The results showed that inhibiting this approach can increase the complex peak rate and peak level, and it is more obvious at the beginning of the arm extension, while the light excitation Has the opposite effect
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Finally, the researchers explored whether a simple Kuramoto [4] olive core network model can capture the core results of this study about complex discharge and time reorganization during arm extension.
The results show that the model supports the olive core neural network in the task The view that the collective dynamics undergo transformation during the execution process
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In general, this study proposes that neurons change from main autonomous firing to synchronous firing to prepare for behavior, and this is a state change that occurs during the process of skill learning
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The researchers emphasized the importance of collective phenomena in the brain by identifying the coherent neural activity generated during the acquisition and execution of motor skills, as well as focusing on the characteristics of single cells to understand the limitations of how neural circuits drive behavioral output
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Original link: https://doi.
org/10.
1016/j.
cell.
2021.
06.
001 Platemaker: Eleven References 1.
Chialvo, DR (2010).
Emergent complex neural dynamics.
Nat.
Phys.
6, 744–750.
2 .
Cafaro, J.
, and Rieke, F.
(2010).
Noise correlations improve response fidelity and stimulus encoding.
Nature 468, 964–967.
3.
Lefler, Y.
, Yarom, Y.
, and Uusisaari, MY (2014).
Cerebellar inhibitory input to the inferior olive decreases electrical coupling and blocks subthreshold oscillations.
Neuron 81, 1389–1400.
4.
Kuramoto, Y.
(1984).
Chemical Oscillations, Waves, and Turbulence (Springer).
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