Positive feedback of efferent copy via pontine nucleus facilitates cerebellum-mediated associative learning

Xiao, Na; Wu, Guohao; Zhou, Zhanhong; Yao, Juan; Wu, Bing; Sui, Jian-feng; Tin, Chung

doi:10.1016/j.celrep.2023.112072

Cited by 9 publications

(6 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This new model effectively accounts for the cerebellar language functions by incorporating the recently identified recurrent pathway into the feedforward circuit, which is the fundamental structure of the conventional cerebellar circuit models. Regarding the recurrent pathway, numerous reports have demonstrated the existence of the vital part of the pathway, the output cellinput cell projections (that contain the direct projection [39][40][41][42][43] and indirect projections through the pontine or red nuclei [35][36][37] ), and its functional significance on the cerebellar prediction 38,41,45 . The recurrent pathway has also been implemented in cerebellar modeling 67 .…”

Section: Discussionmentioning

confidence: 99%

“…The recurrent pathway contains indirect [35][36][37] and direct output cell-input cell pathways. The direct pathway is known as the nucleocortical pathway [38][39][40][41][42][43][44] , that has been demonstrated to be essential for prediction in the cerebellum 38,41,45 . The cANN circuit also incorporates the climbing fiber pathway (gray, Fig.…”

Section: The Cerebellar Ann Model As a Next-word Prediction Circuitmentioning

confidence: 99%

See 1 more Smart Citation

Emergence of syntax and word prediction in an artificial neural circuit of the cerebellum

Ohmae,

Ohmae

2024

Nat Commun

View full text Add to dashboard Cite

The cerebellum, interconnected with the cerebral neocortex, plays a vital role in human-characteristic cognition such as language processing, however, knowledge about the underlying circuit computation of the cerebellum remains very limited. To gain a better understanding of the computation underlying cerebellar language processing, we developed a biologically constrained cerebellar artificial neural network (cANN) model, which implements the recently identified cerebello-cerebellar recurrent pathway. We found that while cANN acquires prediction of future words, another function of syntactic recognition emerges in the middle layer of the prediction circuit. The recurrent pathway of the cANN was essential for the two language functions, whereas cANN variants with further biological constraints preserved these functions. Considering the uniform structure of cerebellar circuitry across all functional domains, the single-circuit computation, which is the common basis of the two language functions, can be generalized to fundamental cerebellar functions of prediction and grammar-like rule extraction from sequences, that underpin a wide range of cerebellar motor and cognitive functions. This is a pioneering study to understand the circuit computation of human-characteristic cognition using biologically-constrained ANNs.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: The Cerebellar Ann Model As a Next-word Prediction Circuitmentioning

confidence: 99%

Emergence of syntax and word prediction in an artificial neural circuit of the cerebellum

Ohmae,

Ohmae

2024

Nat Commun

View full text Add to dashboard Cite

show abstract

“…For EMG analysis 23, 59 , we first aligned the length of EMG same to locomotion or calcium transient by resampling the EMG original data according to the TTLs of CS and US. Then, we calculated the baseline of the EMG (∼930 data, 60s duration before CS onset) by choosing the minimum of the mean values in the two parts (1∼450 frames and 451∼900 frames).…”

Section: Methodsmentioning

confidence: 99%

“…The rotary encoder was triggered at scanning frame rate. And the TTLs of the whisker stimulation and air puff were output via the PulsePal to the two-photon system which were used to align the image For EMG acquisition 23,59 , the eyelid movement signal was band-pass filtered with 50-500 Hz and amplified using a 4-channel differential amplifier (model 1700, A-M Systems) and acquired with the 16-bit data acquisition system (Digidata 1322A, Axon Instrument) through an analog input channel sampled at 1000 Hz. And the TTLs of the whisker stimulation and air puff were output via the PulsePal to the data acquisition system which were used to align the EMG signal.…”

Section: Data Acquisitionmentioning

confidence: 99%

Learning induced neuronal identity switch in the superficial layers of the primary somatosensory cortex

Dai,

Sun

2023

Preprint

View full text Add to dashboard Cite

During learning, multi-dimensional inputs are integrated within the sensory cortices. However, the strategies by which the sensory cortex employs to achieve learning remains poorly understood. We studied the sensory cortical neuronal coding of trace eyeblink conditioning (TEC) in head-fixed, freely running mice, where whisker deflection was used as a conditioned stimulus (CS) and an air puff to the cornea delivered after an interval was used as unconditioned stimulus (US). After training, mice learned the task with a set of stereotypical behavioral changes, most prominent ones include prolonged closure of eyelids, and increased reverse running between CS and US onset. The local blockade of the primary somatosensory cortex (S1) activities with muscimol abolished the behavior learning suggesting that S1 is required for the TEC. In naive animals, based on the response properties to the CS and US, identities of the small proportion (~20%) of responsive primary neurons (PNs) were divided into two subtypes: CR (i.e. CS-responsive) and UR neurons (i.e. US-responsive). After animals learned the task, identity of CR and UR neurons changed: while the CR neurons are less responsive to CS, UR neurons gain responsiveness to CS, a new phenomenon we defined as 'learning induced neuronal identity switch (LINIS)'. To explore the potential mechanisms underlying LINIS, we found that systemic and local (i.e. in S1) administration of the nicotinic receptor antagonist during TEC training blocked the LINIS, and concomitantly disrupted the behavior learning. Additionally, we monitored responses of two types of cortical interneurons (INs) and observed that the responses of the somatostatin-expressing (SST), but not parvalbumin-expressing (PV) INs are negatively correlated with the learning performance, suggesting that SST-INs contribute to the LINIS. Thus, we conclude that L2/3 PNs in S1 encode perceptual learning by LINIS like mechanisms, and cholinergic pathways and cortical SST interneurons are involved in the formation of LINIS.

show abstract

“…83 It is worth noting that the cerebellum also maintains a sparse but direct association with the periaqueductal gray 84 and the dorsal pons/pontine. 85 Autonomic dysfunction could cause abnormal cerebellar function, 86 and the abnormal cerebellum could result in autonomic dysfunction. 87…”

Section: Autonomic Dysfunction and Cerebellummentioning

confidence: 99%

Cerebellum in Alzheimer's disease and other neurodegenerative diseases: an emerging research frontier

Yang,

Liu,

Chen

et al. 2024

MedComm

View full text Add to dashboard Cite

The cerebellum is crucial for both motor and nonmotor functions. Alzheimer's disease (AD), alongside other dementias such as vascular dementia (VaD), Lewy body dementia (DLB), and frontotemporal dementia (FTD), as well as other neurodegenerative diseases (NDs) like Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS), Huntington's disease (HD), and spinocerebellar ataxias (SCA), are characterized by specific and non‐specific neurodegenerations in central nervous system. Previously, the cerebellum's significance in these conditions was underestimated. However, advancing research has elevated its profile as a critical node in disease pathology. We comprehensively review the existing evidence to elucidate the relationship between cerebellum and the aforementioned diseases. Our findings reveal a growing body of research unequivocally establishing a link between the cerebellum and AD, other forms of dementia, and other NDs, supported by clinical evidence, pathological and biochemical profiles, structural and functional neuroimaging data, and electrophysiological findings. By contrasting cerebellar observations with those from the cerebral cortex and hippocampus, we highlight the cerebellum's distinct role in the disease processes. Furthermore, we also explore the emerging therapeutic potential of targeting cerebellum for the treatment of these diseases. This review underscores the importance of the cerebellum in these diseases, offering new insights into the disease mechanisms and novel therapeutic strategies.

show abstract

Positive feedback of efferent copy via pontine nucleus facilitates cerebellum-mediated associative learning

Cited by 9 publications

References 64 publications

Emergence of syntax and word prediction in an artificial neural circuit of the cerebellum

Emergence of syntax and word prediction in an artificial neural circuit of the cerebellum

Learning induced neuronal identity switch in the superficial layers of the primary somatosensory cortex

Cerebellum in Alzheimer's disease and other neurodegenerative diseases: an emerging research frontier

Contact Info

Product

Resources

About