About whether or not L6a and L5 form another instance of the input/output layer model:
I think L5 is similar to L2/3a, but it solves a related problem. L5 generates behavior by predictively firing. The way I see it, L2/3a does something similar to predictive firing, because it represents the features it might sense on the object if the sensors were to touch the correct location. So both output layers sort of predictively fire, although it’s not the same as activating predicted cells in temporal memory.
The thick-tufted/pyramidal tract cells in L5 don’t seem to represent entire external objects. Before a saccade, each cell stops responding to the features in the receptive field and starts responding to the features expected to be in the receptive field after the saccade (Shin and Sommer, 2012). Only ~10/50 cells in that study stopped responding to the current receptive field and started responding to the post-saccade receptive field (most did one or the other). Still, it doesn’t make sense to keep responding to the current feature to generate a saccade to another feature.
That doesn’t mean L5 should be an input layer. It’s useful to narrow down possibilities using lateral connections between columns for any sort of prediction. The ability to narrow down possibilities is also useful for action selection. By narrowing down the represented set of features, it can narrow down what it wants to perceive as a result of moving sensors or changing the environment. There is biological evidence that this happens.
Maimon and Assad, 2006 suggest that a reverberant circuit involving the cortex and the basal ganglia causes ramping pre-movement activity, which triggers a burst of activity once it passes a threshold to cause movement. In the period leading up to a saccade, some neurons in superior colliculus, mediodorsal thalamus, and the frontal eye field (at least pyramidal tract cells) increase their firing rates until just before the saccade, reaching firing rates above 100 hz (Sommer and Wurtz, 2004), which is important because bursting above 100 hz might play roles in pyramidal tract cell plasticity, lateral communication, and corticothalamic signaling, and bursting is controlled by complex disinhibitory and winner-takes-all circuits.
In addition to the ramping activity of cells which will trigger the movement, cells which do not contribute to the movement probably gradually decrease their firing rates (see Maimon and Assad, 2006), especially since pyramidal tract cells stop responding to the current visual input as part of presaccadic predictive remapping and the current visual input is not the saccade target. Since the basal ganglia also exhibits ramping activity before a movement (Lee and Assad, 2003), it could selectively gate a feedback loop from FEF to SC to MD to FEF to narrow down options based on reward learning. It could also control timing based on the amount of positive feedback (and thus rate of ramping) it allows.
Another difference L5 has with L2/3a is that it should only learn to represent possibilities that the brain can cause by behavior. Otherwise, it might try to cause predicted external events. Thick-tufted cells in L5 respond well to sensory input without behavior (Oberlaender et al., 2011). However, slender-tufted cells seem to track behavior because they increase their firing rates during behavior on average, and those in barrel cortex are modulated by the phase of the whiskers (Oberlaender et al., 2011). They also receive strong input from L4 (Schubert et al., 2006) and they might be highly sensitive to the combination of self-motion and sensory input, although I’m not sure which cells this study recorded (Turner and DeLong, 2000). It seems that slender-tufted L5 cells copy L4, except they do not respond to sensory input alone, so they might track the sequence of behavior and behavioral results.
Slender-tufted cells project to thick-tufted cells, but not really vice-versa (Naka and Adesnik, 2016). Perhaps slender-tufted cells act as another input layer to thick-tufted cells. Regardless of their projection to thick-tufted cell basal dendrites, they also project to the apical tuft of thick-tufted L5 cells. Oberlaender et al., 2011 propose that this projection allows thick-tufted cells to perform coincidence detection between behavior and sensory input, since thick-tufted cells respond to temporally associated proximal and tuft input by bursting. Since pyramidal tract cells probably burst to generate movement, this is a possible mechanism to ensure that they do not burst to cause an external event.
They still might predictively fire in anticipation of an external event, which might allow them to model objects at the same time as they generate behavior. This could explain why only ~10/50 cells both stopped responding to the current input and started responding to the post-saccade input. Alternatively, since bursting might be required for LTP everywhere on the cell (Kampa et al., 2006; Ramaswamy and Markram, 2015) and cells might continue their burst for a bit after the end of the movement, thick-tufted L5 cells might learn not to predict things which behavior cannot cause.
There are other possibilities for preventing bursting unless an event was caused by behavior, such as input from motor thalamus (or e.g. POm) or inhibition of bursting by martinotti cells during quiet periods. However, those both have possible problems.
I’m not sure how the thick-tufted cells or subcortical structures convert predictions into behavior to cause those predictions. If bursting depends on slender-tufted cell input to the apical tuft, then pyramidal tract cells might learn to only ramp activity and burst if they target the correct subcortical cells to trigger the result.
I’m going to quote some parts of the paper.
Recent experimental studies found that the axons of L6 CT neurons densely ramified within layer 5a in both visual and somatosensory cortices of the mouse, and activation of these neurons generated large excitatory postsynaptic potentials (EPSPs) in pyramidal neurons in layer 5a (Kim et al., 2014) [. . .] There are three types of pyramidal neurons in L5 (Kim et al., 2015). Here we are referring to only one of them, the larger neurons with thick apical trunks that send an axon branch to relay cells in the thalamus (Ramaswamy and Markram, 2015).
In barrel cortex, thick-tufted cells are mostly confined to L5b, whereas slender-tufted cells are preferentially but not exclusively in L5a (Naka and Adesnik, 2016; Groh et al., 2009). Slender-tufted cells do not project to the thalamus, only the striatum (Oberlaender et al., 2011). I think L6 probably targets another input layer, the slender-tufted cells (which receive thalamic input from POm, part of which is primary sensory thalamus for self-movement), equivalent to L6 targeting L4. So maybe both input/output layer pair is equivalent, but they share an input from L6.
Although slender-tufted cells communicate between columns, they only do so weakly (Oberlaender et al., 2011), so the lateral connectivity is probably for predictive depolarization like in temporal memory. Also, even though they receive thalamic input from multiple whiskers via POm, they have narrow receptive fields (Bureau et al., 2006). They also communicate in both directions with L4 (Schubert et al., 2006), so they might serve similar functions.
It’s also possible that L6 really is the input layer to the L5 output layer, but slender-tufted cells are an intermediate step. This extra step could reflect the requirement to only predict results of behavior, whereas L2/3a can predict both externally- and behaviorally-caused input.
However, there is also empirical evidence our model does not map cleanly to L6a and L5. For example, (Constantinople and Bruno, 2013) have shown a sensory stimulus will often cause L5 cells to fire simultaneously or even slightly before L6 cells, which is inconsistent with the model.
As far as I know, somatosensory cortex (or at least barrel cortex) is the only region where thalamus prominently drives firing in layer 5 thick-tufted cells. Those cells only receive input from narrow receptive fields of the whiskers via VPM, yet they have wide receptive fields (Manns et al., 2004). Oberlaender et al., 2011 suggest that their wide receptive fields result from strong lateral connectivity. Even though thalamus can drive their activity before L6, it probably doesn’t drive most of their responses. Perhaps a little thalamic input to the output layer helps represent the possibilities indicated by the initial input, or perhaps it helps prevent directly sensed features from getting ruled out because they aren’t considered consistent with the other features.
Division of labor in frontal eye field neurons during presaccadic remapping of visual receptive fields (Shin and Sommer, 2012)
Parietal Area 5 and the Initiation of Self-Timed Movements versus Simple Reactions (Maimon and Assad, 2006)
The time course of perisaccadic receptive field shifts in the lateral intraparietal area of the monkey (Kusunoki and Goldberg, 2002)
Putaminal Activity for Simple Reactions or Self-Timed Movements (Lee and Assad, 2003)
What the brain stem tells the frontal cortex. I. Oculomotor signals sent from superior colliculus to frontal eye field via mediodorsal thalamus (Sommer and Wurtz, 2004)
Three-dimensional axon morphologies of individual layer 5 neurons indicate cell type-specific intracortical pathways for whisker motion and touch (Oberlaender et al., 2011)
Morphology, electrophysiology and functional input connectivity of pyramidal neurons characterizes a genuine layer va in the primary somatosensory cortex (Schubert et al., 2006)
Corticostriatal Activity in Primary Motor Cortex of the Macaque (Turner and DeLong, 2000)
Inhibitory Circuits in Cortical Layer 5 (Naka and Adesnik, 2016)
Requirement of dendritic calcium spikes for induction of spike-timing dependent synaptic plasticity (Kampa et al., 2006)
Anatomy and physiology of the thick-tufted layer 5 pyramidal neuron (Ramaswamy and Markram, 2015)
Sub- and suprathreshold receptive field properties of pyramidal neurones in layers 5A and 5B of rat somatosensory barrel cortex (Manns et al., 2004)
Cell-Type Speciﬁc Properties of Pyramidal Neurons in Neocortex Underlying a Layout that Is Modiﬁable Depending on the Cortical Area (Groh et al., 2009)
Interdigitated paralemniscal and lemniscal pathways in the mouse barrel cortex (Bureau et al., 2006)