A temporal basis for predicting the sensory consequences of motor commands in an electric fish

Abstract

Mormyrid electric fish are a model system for understanding how neural circuits predict the sensory consequences of motor acts. Medium ganglion cells in the electrosensory lobe create negative images that predict sensory input resulting from the fish's electric organ discharge (EOD). Previous studies have shown that negative images can be created through plasticity at granule cell-medium ganglion cell synapses, provided that granule cell responses to the brief EOD command are sufficiently varied and prolonged. Here we show that granule cells indeed provide such a temporal basis and that it is well-matched to the temporal structure of self-generated sensory inputs, allowing rapid and accurate sensory cancellation and explaining paradoxical features of negative images. We also demonstrate an unexpected and critical role of unipolar brush cells (UBCs) in generating the required delayed responses. These results provide a mechanistic account of how copies of motor commands are transformed into sensory predictions.

Figure 1. Corollary discharge responses in mossy fibers, UBCs, and Golgi cells

(a) Schematic of negative image formation and sensory cancellation in a medium ganglion cell. The question mark indicates that temporal patterns of corollary discharge response in granule cells are the critical unknown in current models of sensory cancellation. (b) Schematic of the circuitry of the EGp and ELL. Corollary discharge signals related to the EOD motor command are relayed via several midbrain nuclei (not shown) and terminate in EGp as mossy fibers (MF, black inputs from top). UBCs give rise to an intrinsic system of mossy fibers that provide additional excitatory input to granule cells (GC). Golgi cells inhibit granule cells and UBCs. Medium ganglion (MG) cells in ELL receive both sensory input and granule cell input via parallel fibers. (c) Corollary discharge responses of units recorded in the paratrigeminal command associated nucleus (PCA) and the preeminential nucleus (PE). Each row shows the smoothed (5 ms Gaussian kernel) and normalized average firing rate of a single unit. In this and subsequent figures time is defined relative to the EOD motor command (Cmnd), which is emitted spontaneously by the fish at 2–5 Hz. Color bar in e applies also to c and f. (d) Example spike rasters (grey dots) and smoothed firing rates (black curves) for putative mossy fibers recorded extracellularly in EGp illustrating four temporal response classes (early, medium, late, and pause). (e) Corollary discharge responses of putative mossy fibers recorded extracellularly in EGp. Each row represents the smoothed and normalized average firing rate of a single mossy fiber, with 10 examples of each class shown. (f) Corollary discharge responses of UBCs (n = 19) and Golgi cells (n = 8) recorded intracellularly. Each row represents the smoothed and normalized average firing rate of a single cell. Note the similarity with late and pause mossy fibers, shown in e.

Figure 2. Mechanisms for delayed and diverse corollary discharge responses in UBCs

(a) Two overlaid traces illustrating prominent rebound firing in response to hyperpolarizing current injections (−10 and −20 pA) in a UBC. This cell was filled with biocytin allowing for post-hoc morphological identification (inset, scale bar 10 μM). (b) Late corollary discharge response in the same UBC recording shown in a. The strength of late action potentials bursts (bottom traces) is related to the degree of preceding membrane potential hyperpolarization (top traces), suggesting rebound from command-locked hyperpolarization as a possible mechanism underlying late responses observed in UBCs. (c) Two UBCs in which a brief hyperpolarizing current injection (−50 pA, top; −200 pA, bottom) results in an entrainment of tonic firing, similar to temporal patterns of action potential firing observed in pause mossy fibers. Similar effects were seen in 7 additional UBCs. (d) Pause-type corollary discharge response in a UBC, note the small hyperpolarization time-locked to the command and the entrainment of tonic action potential firing after the pause.

Figure 3. Experimental characterization and modeling of corollary discharge responses in granule cells

(a) Average subthreshold corollary discharge responses of 170 granule cells. Responses are grouped by category (see d) and then sorted by the latency of their peak membrane potential. (b) Left, examples of recorded granule cell subthreshold responses (black trace) and model fits (green). Right, EPSPs computed from the recorded mossy fiber inputs used to fit each granule cell, labeled according to the class to which they belong. (c) The distribution of response categories assigned to recorded granule cells based on model fits (black bars). Bars labeled E, M, L and P indicate the fraction of early, medium, late and pause inputs used to fit the recorded granule cell responses. “Mixed” bars show these fractions for combinations of inputs used in the same way. These fractions are consistent with a four-parameter random mixing model (RMM; parameters are the probability of early, medium, late, and pause inputs) in which each input to a granule cell is assigned independently of the others (red bars). This suggests that the combinations of inputs granule cells receive are random. (d) Average subthreshold corollary discharge responses of 170 randomly constructed model granule cells selected from a total of 20,000. In this sample, the number of model cells from each granule cell category was matched to the experimental data, but the selection process was otherwise random. Note that the temporal response properties of the model granule cells closely resemble those of the recorded granule cell shown in a.

Figure 4. Patterns of corollary discharge-evoked action potential firing in recorded and model granule cells

(a) Corollary discharge responses of four recorded granule cells that spiked in response to the EOD command. Granule cell membrane potentials from several commands are shown overlaid. Spikes are truncated to show details of subthreshold membrane potentials. (b) Spiking responses of the recorded granule cells shown in a. Spike trains on 50 individual trials are shown in gray, and the smoothed (5 ms Gaussian kernel) trial-averaged firing rate of the cell is overlaid in black. (c, d) Corollary discharge responses of four model granule cells selected from among the pool of 20,000 generated cells. Displays for model granule cells are the same as for recorded cells. e, Sources of mossy fiber input to each model granule cell, as computed EPSPs from the trial-averaged mossy fiber firing rates. Both subthreshold corollary discharge responses and spiking in model granule cells closely resembles that seen in recorded granule cells.

Figure 5. Granule cell corollary discharge responses provide an effective basis for cancelling natural patterns of self-generated sensory input

(a) Top, Cancellation of the change in membrane potential caused by sensory input locked to the EOD motor command in a model medium ganglion cell. The medium ganglion cell receives 20,000 model granule cell inputs with synaptic strengths that are adjusted by anti-Hebbian spike-timing dependent plasticity. Bottom, select trials showing the time course of cancellation. The temporal profile of the sensory input (trial 0) was chosen to resemble the effects of the EOD on passive electroreceptors recorded in a previous study. (b) The negative image (blue line) effectively cancels the sensory input (black line), with small command-to-command variability (shaded region shows ± 1 std across trials.) (c) Different input signals used for the tests of sensory cancellation rates shown in d. The top trace is the same input used in a, and resembles natural self-generated inputs due to the EOD. The blue traces are selected from a set of 1,000 synthesized inputs with the same power spectrum as the natural input but with randomized phases. (d) Comparison of the time course of cancellation for the natural sensory input (black) versus the synthesized inputs (blue; shaded region is ± 1 std). Note that cancellation is faster for the natural input, suggesting that the structure of granule cell responses is matched to the temporal pattern of the self-generated signal. Cancellation is also much slower and less effective if the model granule cells are generated without UBC inputs (green line).

Figure 6. Non-uniform temporal structure of granule cell responses predicts specific features of negative images in medium ganglion cells

(a) Changes in corollary discharge responses induced by pairing with medium ganglion dendritic spikes at 7 different delays after the EOD command. Green traces are membrane potential differences derived from the model with fitted values for the magnitudes of associative depression and non-associative potentiation (panel c). Black traces are experimentally observed membrane potential differences averaged across medium ganglion cells (outlines represent SEM; 0 ms, n = 6; 25 ms, n = 8; 50 ms, n = 6; 75 ms, n = 6; 100 ms, n = 10; 125 ms, n = 4; 150 ms, n = 3). The bottom right panel compares these predictions with those for a delay line basis (dashed green line). (b) Design of the pairing experiment. Intracellular traces from an medium ganglion cell showing the average (black) and standard deviation (gray outline) of the corollary discharge (CD) response before (pre) during (pairing), and after (post) three minutes of pairing during which a brief (12 ms) intracellular current injection evoked a dendritic spike at a fixed delay after the EOD command (arrow). The small spikes are axonal spikes and do not contribute to plasticity. The bottom trace (post-pre) shows the difference in the membrane potential induced by the pairing, corresponding to the traces shown in a. Note the complex pattern of change—a relative hyperpolarization around the time of the paired spike as well as a large relative depolarization just after the command, as predicted by the model. (c) Synaptic plasticity rule and parameters used for the fits shown in a. Δ₊ is the magnitude of the non-associative potentiation and Δ− is the magnitude of the associative depression.