Connectome analysis of a cerebellum-like circuit for sensory prediction

A, Absolute peak membrane potential (relative to baseline) of ON (red) and OFF (blue) model Output cells during the first 4 min after turning an EOD mimic on for different levels of ‘selectivity’ of the effects of sensory input on broad spikes. A value of 1 (‘selective’) indicates that sensory input solely effects broad spikes and a value of 0 (‘non-selective’) indicates equal effects on broad and narrow spikes. When selectivity is low, MG cells substantially impair cancellation compared to not having MG input at all (dashed line). This occurs because sensory drive to narrow spikes amplifies EOD-evoked responses in the Output cells. MG recurrence further amplifies this effect, producing large depolarization in MG cells (~15 mV peak response) compared with no recurrence (2.25 mV). B, Membrane potential modulation of ON (red) and OFF (blue) model Output cells after 4 min of learning (quantified as variance normalized by the variance at zero MG synapse weight). Modulation is plotted as a function of MG:Output synaptic strength for different MG:Output connectivity patterns (left two plots), and as a function of MG:MG synaptic strength for different MG recurrence patterns (right two plots). For MG:Output connectivity, “reverse” denotes MG+ :ON and MG− :OFF, whereas “random” denotes both MG types synapsing onto both Output types. For MG:MG connectivity, “reverse” denotes MG+ :MG+ and MG− :MG− , whereas “random” denotes all-to-all connectivity. Vertical gray lines indicate default synaptic strength values used in the model. C, Absolute peak membrane potential (relative to baseline) of ON (red) and OFF (blue) model Output cells during the first 4 min of learning to cancel the EOD-mimic, shown for different levels of recurrence selectivity for broad spikes. A value of 1 (‘selective’) indicates that recurrence solely effects broad spikes and a value of 0 (‘non-selective’) indicates equal effects on broad and narrow spikes. When selectivity is high, the contribution of MG cells to cancellation is comparable to the condition without MG recurrence (dashed line), because negative images transmitted exclusively to broad spikes are not transmitted to Output cells. D, The reduction in MG contribution caused by increased broad spike selectivity can be compensated by increasing MG to Output synaptic strength, suggesting an alternative biophysical mechanism for maintaining an effective MG contribution of ~0.5 to the negative image in Output cells. E, Membrane potential modulation of ON (red traces) and OFF (blue traces) model Output cells in the first 4 min of learning, shown for different plasticity rates at GC to SG synapses. A value of 1 (“SG learning”) indicates that the ratio of post-synaptic EOD response to background activity matches that of MG cells (~60), resulting in faster depression of GC synapses in SG+ cells. Introducing SG learning slightly slows cancellation in ON cells because, as SG− synapses gradually cancel their sensory inputs, ON cells must reverse previously learned cancellation. This continual rebalancing limits the net contribution of SG plasticity to overall cancellation. In these simulations, SG cells are assumed to generate two spike types but lack sufficient electrical separation to decouple learning from signaling. Thus, unlike MG cells, SGs both receive and transmit sensory input. Learning at GC-SG synapses therefore cancels predictable EOD-evoked input at the level of narrow spikes, yielding a “cleaned-up” sensory signal in which predictable components are attenuated while unpredictable components (e.g. prey signals) are preserved. In this framework, SG cells enhance Output responses to unpredictable sensory input, whereas MG cells suppress Output responses to predictable input. F, Spike rate of ON (red) and OFF (blue) model Output cells after 4 min of cancelling the EOD-mimic for the different conditions described in Fig. 5d: without any MG connectivity, which effectively restricts learning to the Output cells (dashed), with MG-Output connections to Output cells, and thus two-site learning, but no recurrence (dotted), and with both MG to Output and MG-MG recurrent connections (fully connected model, solid). Grey lines denote the initial response. G, Ratio of peak sensory-evoked firing (measured during the first second after turning the EOD mimic on) relative to their baseline (equilibrium) firing rate for broad spikes for individual MG+ cells (n = 43) and ON cells (n = 24). In Fig. 5, population-averaged responses yielded a ratio of ~60:8 (MG+ :ON), comparable to that obtained here (~100:12) from single cells. Boxplots show the median (centre line), 25th and 75th percentiles (box bounds), and minima and maxima of the non-outlier data (1.5× IQR from the box edges). H, Contribution of MG cells to the negative image in model Output cells after 4 min of learning (left two plots) and at equilibrium when the EOD is fully cancelled (right two plots). After 4 min, approximately 90% of the negative image in ON cells (red traces) arises from GC-MG plasticity, with an even larger contribution in OFF cells (blue traces). These results replicate prior experimental findings in which Output cell plasticity was blocked using intracellular current injections8. At equilibrium, half of the negative image is contributed by MG input and half is from the direct GC input. Related to Fig. 5.