Recruitment in Retractor Bulbi Muscle During Eyeblink Conditioning: EMG Analysis and Common-Drive Model

Abstract

To analyze properly the role of the cerebellum in classical conditioning of the eyeblink and nictitating membrane (NM) response, the control of conditioned response dynamics must be better understood. Previous studies have suggested that the control signal is linearly related to the CR as a result of recruitment within the accessory abducens motoneuron pool, which acts to linearize retractor bulbi muscle and NM response mechanics. Here we investigate possible recruitment mechanisms. Data came from simultaneous recordings of NM position and multiunit electromyographic (EMG) activity from the retractor bulbi muscle of rabbits during eyeblink conditioning, in which tone and periocular shock act as conditional and unconditional stimuli, respectively. Action potentials (spikes) were extracted and classified by amplitude. Firing rates of spikes with different amplitudes were analyzed with respect to NM response temporal profiles and total EMG spike firing rate. Four main regularities were revealed and quantified: 1) spike amplitude increased with response amplitude; 2) smaller spikes always appeared before larger spikes; 3) subsequent firing rates covaried for spikes of different amplitude, with smaller spikes always firing at higher rates than larger ones; and 4) firing-rate profiles were approximately Gaussian for all amplitudes. These regularities suggest that recruitment does take place in the retractor bulbi muscle during conditioned NM responses and that all motoneurons receive the same command signal (common-drive hypothesis). To test this hypothesis, a model of the motoneuron pool was constructed in which motoneurons had a range of intrinsic thresholds distributed exponentially, with threshold linearly related to EMG spike amplitude. Each neuron received the same input signal as required by the common-drive assumption. This simple model reproduced the main features of the data, suggesting that conditioned NM responses are controlled by a common-drive mechanism that enables simple commands to determine response topography in a linear fashion.

Cerebellum and Cognition: Evidence for the Encoding of Higher Order Rules

“…However, this has not been supported by previous neuroimaging studies, which mostly show a decrease in cerebellar activity during learning and automaticity (Imamizu et al 2000;Doyon et al 2002;Penhune and Doyon 2005;Balsters and Ramnani 2011). This could relate to the decreases in complex and simple spikes seen in electrophysiology studies of the cerebellum during skill acquisition (Gilbert and Thach 1977;De Zeeuw and Yeo 2005;Medina and Lisberger 2008;Lepora et al 2009), or it could suggest that the cerebellum is involved in adapting and tuning cortical processes but does not act as a storage for these processes (Doyon et al 2003;Debas et al 2010). This is an area that requires further investigation, and we would suggest that future studies investigate cortico-cerebellar connectivity ( possibly using dynamic causal modeling) to try and establish how neocortical and connected cerebellar areas interact during learning (Apps et al 2009;Saalmann et al 2009).…”

Cerebellar Plasticity and the Automation of First-Order Rules

“…Gilbert and Thach (1977), recording from PCs in cerebellar lobules III, IV, and V of rhesus monkeys, found a high frequency of complex and simple spikes at the start of learning, which declined to background levels as learning progressed. Classical eyeblink conditioning, a simple form of cerebellar-dependent motor learning (De Zeeuw and Yeo, 2005;Lepora et al, 2009), is also accompanied by a learning-dependent decline in the frequency of simple spikes following the onset of predictive conditioned stimuli that generate conditioned responses (Jirenhed et al, 2007). Similarly, Medina and Lisberger (2008) investigated excitability changes in PCs in the monkey cerebellum during smooth pursuit eye movement adaptation, and also reported a progressive depression in simple spike activity and decreases in complex spike probability in PCs.…”

Timing in trace conditioning of the nictitating membrane response of the rabbit (Oryctolagus cuniculus): Scalar, nonscalar, and adaptive features

“…In fact, interstimulus interval (ISI)-dependent activity has appeared in Purkinje cells (Jirenhed et al 2007). This cerebellar activity ultimately drives the motor processes underpinning eyelid movement (Bartha and Thompson 1992;Lepora et al 2007Lepora et al , 2009Mavritsaki et al 2007). …”

“…These models have generally concerned primarily the method by which the passage of time since CS onset is represented in a spectrum of microstimuli, whose neural counterparts reside in cerebellar pathways (e.g., Buonomano and Mauk 1991;Moore and Choi 1997;Mauk et al 2000;Lepora et al 2010). Some models include CR generation rules that take account of the neural and mechanical lags between cerebellar output and movement of the NM (e.g., Bartha and Thompson 1992;Lepora et al 2009). By combining these lags plus delays in the cerebellar-olivary feedback loop (see below), it is possible to model more accurately the entire time course of the CR, including systematic delays in the CR peak relative to the US (Lepora et al 2010).…”