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6.6.2.1 Activity during turning

Predictive coding posits that comparator circuits be present which compute the difference between predicted and actual input (of any type). Displacement of the target results in a sensory error present as the difference between where the brain expects the target to be and where it actually was located. This visual error is hypothesized to drive motor adaptation (Miall and Wolpert, 1996) and corrective turns could be the behavioral correlates in this task. The optogenetics experiments (Figure 6 – 10) have shown that motor cortex is involved in learning of the task and corrective turning. With the activity recordings, I attempted to understand the computations underlying the behavior. Supporting the idea that motor cortex might be site of internal model learning, using disruption of motor cortex activity by repetitive transcranial magnetic stimulation in humans, it has been suggested that internal models are retained in M1 after motor learning (Cothros et al., 2006).

Mice, as they navigate the virtual tunnel, can capitalize on existent movements: Motor commands (Figure 30A) do not have to be learned anew, hence the known relationship between motor command activity and motor effectors producing state changes can be re-used. Classic robotics would assume that, after a state change has happened, external sensors collect information about the new state and relay this information back to control centers. This is clearly not the way movements are controlled since the collection of information before the next movement step would cause long transduction delays which is not the way animals behave. Instead, likely an efference copy of the motor command is used to update internal models in which predictions are stored of what the new state should be given the employed motor command. The output of the internal model - a predicted sensory state - is then used to compare with the intended movement and generate input to the controller. I would like to speculate in the following that comparator and controller map onto either layer 2/3 and layer 5 PT units, respectively (Figure 30B).

During learning, the likely site of plasticity is the internal model of the environment. Mice learn the physics of the environment and the way by which their own movement changes the virtual environment. Thus, in the course of learning, the predictability of the environment increases alongside with the efficiency of motor behavior execution. If layer 2/3 circuits compute

the deviation between intended motor program and the prediction of its consequences (Figure

30B) this deviation should become less with learning because the sensory consequences of the

mice’s movement are as expected. In other words: As predictability increases, the error between prediction and the outcome of actions is reduced, in line with current theories of brain function (Friston, 2010). Thus, if layer 2/3 neurons were such error units, their activity should decrease over the course of learning. This is exactly what I observed. In parallel, on a similar timescale, units which provide predictions would increase their activity as predictions become refined and the predictive power of the internal model increases. Cell types which convey predictions should therefore be more active at late stages of learning. This maps well onto layer 5 PT neurons (Figure

17). In summary, layer 2/3 neurons in motor cortex could functionally mediate error computations

in a predictive coding framework while layer 5 PT neurons, those thought to mediate the motor output, are the neurons conveying predictions.

Motor command-like activity driving motor adaptation will be indistinguishable from prediction signals which might update internal models. As previously elaborated, the cerebellum is a likely site for storage and maintenance of internal models. Layer 5 PT neurons project subcortically and form a major termination zone in the pontine nuclei of the brainstem which relays information to the cerebellar cortex and a smaller fraction of ponto-cerebellar axons also targets the cerebellum’s motor output nuclei (Caligiore et al., 2016). These axon branches might carry prediction signals essential for updating cerebellar internal models for example by comparing sensory input impinging via the inferior olive with motor efference copies. An experiment which could shed light on the question if information conveyed by layer 5 PT neurons differs depending on the target could be recording at different sites of termination, for example in pontine nuclei or dorsal striatum.

Interestingly, in a human reaching task it was found that the sensory prediction error alone (without the actual motor correction) was sufficient for motor adaptation (Tseng et al., 2007). I speculate that for driving motor learning, mice do not actually need to execute the movement. This could explain why layer 5 PT neurons, although their activity is reminiscent of motor commands, do not actually need to drive movement in order to be essential for motor learning. It might also provide a rationale for why these neurons, the activity of which has generally been considered as motor commands, innervate many more sites throughout the brain than they do innervate actual motor effectors (Kita and Kita, 2012).

6.6.2.2 Activity during running

During self-initiated locomotion-onsets (Figure 19) I found in layer 2/3 neurons, similar to spontaneous turns (Figure 18), that activation is decreased as mice learned the task. In layer 5 PT neurons, albeit noisy, I found an increase in activation. Layer 5 IT neurons did not change their

responses.

This is in excellent agreement with the previously outlined predictive coding scheme (Figure 30C). Layer 2/3 neurons might represent error units signaling the deviation between predicted and intended change of the environment. As behavior becomes learned, this error decreases, hence activity in layer 2/3 should decrease, which is what I observed. Interestingly, this learning-related decrease in activation seemed to be mediated mainly by a large reduction in preparatory activity (Figure 19C1). This opens up the possibility that preparatory activity is not signifying a state of activity devoted to movement planning as suggested during primate instructed-delay studies. Rather, in a predictive coding interpretation, preparatory activity could signal the anticipated uncertainty in the internal model at the beginning of learning. This would suggest that the activity of error units would not only signal the deviation of predicted from actual state but part of their activity could signal an overall estimate of uncertainty of the environment. If layer 5 PT neurons were prediction units, their activity should increase. Albeit noisy, I can observe such an increase, confirming the position of layer 5 PT neurons as prediction units within the predictive coding framework (Adams et al., 2013).

An interesting observation arose from comparing data obtained when mice were running in VR or in darkness (Figure 19). In running speed-matched comparisons I observed that:

1) Running-related activity in darkness was significantly higher than when running in VR. 2) During running in darkness, running-related activity did not change in a learning-

dependent manner.

This is in good agreement with predictive coding schemes which posit that activity depends on the context and is situational (see Claim 2 above). In particular, if layer 2/3 neuronal activity was representative of prediction errors, I would have expected such a pattern. In darkness, mice are deprived of visual feedback, thus the environment is highly uncertain to the mice. This makes the prediction meaningless and thereby produces many prediction errors which are reflected in high activity of presumptive layer 2/3 error units. Additionally, prolonged experience in darkness does not help finessing the internal model since the feedback about the success of mice’s movement is seemingly random. Hence, activation in layer 2/3 does not change in a learning-related manner when mice run in darkness.

From human psychophysics it has been concluded that internal models derived from different senses can compensate for one another (Sainburg et al., 1993) and multiple internal models can co-exist and be learned in parallel (Krakauer et al., 1999). This raises the question if internal model-specific error signals exist which might map onto movement types. My data are agnostic to this assumption: Running and turning likely fall in grossly similar movement categories (e.g. both largely use the same muscles) and, more important, learning to navigate the VR will affect both internal models similarly, making it hard to experimentally distinguish either internal

model with this task. To address this question, I would train mice on two distinct tasks in which execution of behavior relies on different sensory modalities. This might allow assessing if the same neurons mediate distinct internal models or if the models map onto discrete cell populations.

6.6.3 Local sensorimotor transformation could enable