The direction I was going with that is based on the idea you mentioned in a post that L5 has noisy activity to generate experimental behavior, or something along those lines (no citations yet, sorry!)
I think that must extend to a sensory role, seeing as L5 is noisy in sensory regions, too. It’s true that those sensory regions have motor outputs, but ~half of L5 cells in those regions are still TT cells, whereas you’d expect them to be sparsely distributed if they only had a motor role.
So to apply noisy firing to sensory things, I don’t see the noise being too useful for representation, and it’s not useful for generating behavior at any given moment, but it is still useful for learning sensory things like just for behavior. For example, let’s say L5 learns precise timing in sensory processing. Then the noise could contribute to testing slightly different timings. The same goes for processing distance, or any other continuous variable. I don’t have any ideas about the specifics, I just imagine learning mechanisms would cause it to latch onto a randomly good response.
If L5 represents where things are and when things happen, it’s in a pretty good position to generate behavior, so it might as well serve that sensory role too.
To summarize the upcoming rambling, I also think L5 generates predictions, not the same kind as in sequence memory though because it operates on precise timing scales. That ties into behavior in a vague way. I’m more thinking about a sensory role. It’s based on bursts appending spikes to the end of the event which caused the burst, and spike timing dependent plasticity.
(some rambling)
I also think L5 generates predictions, not the same kind like in sequence memory. When a cell bursts, that appends some spikes after the event which caused the burst, so it can associate that even to things maybe 40 ms later. If something occurs coincident with or even a bit after the first spike, it will undergo LTD postsynaptically from the bursting cell (because some spikes occur afterwards), assuming normal spike timing dependent plasticity. I think there must be actively represented predictions to generate behavior, even just because the time delay would otherwise be too great. Presaccadic predictive activity (presaccadic remapping) occurs in cells which project to the superior colliculus, so TT cells, and the presaccadic activity in that layer but not L4 includes a weakening of the response to the current sensory input, suggesting L5 TT has a role in active prediction. I also think active prediction is required for thought, but who knows. Anyway, the point I got a bit distracted from is, noise can help test predictions, both by generating random ones and adjusting the predictive response a bit. These effects could occur postsynaptically in the targets of L5 TT cells, or just in lateral connections between TT cells.
I was looking at my notes for burst learning rules and this effect might occur presynaptically to a bursting cell. Now that I think about it, that needs to happen for the predictive remapping signal from L5 TT to mesh with a role in firing-represented prediction. The source doesn’t clearly show the compartment on the cell where this occurs. When a cell bursts, it dissociates from presynaptic inputs which occur between 125 ms and 25 ms after the burst begins (5). It also dissociates from inputs 200 ms before to 200 ms after burst onset, but -125 to +25 ms is the window of greatest synaptic depression and there’s single firing mode plasticity to consider which might bring it to synaptic input sufficient to burst sometimes, but not too often, leading to a kind of equilibrium of plasticity caused by the two firing modes leading to opposite plasticity.
I don’t know about TT cells because I’ve done basically no research on L6, but the article you linked is about L6 CT activating L5a pyramidal cells, so that signal from L6 to L5 is to slender tufted cells. There’s still probably an input from L6 to L5 TT since that seems to be part of some ideas at Numenta.
There are probably multiple driving inputs. Things get a lot more confusing for me when I try to figure out the exact flow of signals through the circuit because each layer or sublayer is connected to a bunch of others and the single cell processes are hard to research and not well understood, so it’s easier for me to think of each layer as serving certain roles than performing a particular step in an algorithm.
As far as I know, motor thalamus connects to the same layers as sensory thalamus. But L5 thalamic input is generally not found by studies, even though it exists in V1 (1), barrel cortex (2 and many other sources), and A1 (3), so probably everywhere, and this can drive responses. I suspect this isn’t commonly accepted because the other sources of input drown out this pathway and because L5 is thought of as an output.
I got pretty frustrated when I was researching behavior because I couldn’t find clear behavioral pathways and didn’t make progress, so my opinion is that corticostriatal cells are the decision making cells. L5 TT cells can project to the basal ganglia, but they project to pretty much everything subcortex and that projection varies a lot between species (I think I’ve lost the source, but this one found only a minor projection in one species (4)). L5 TT cells drive behavior, but that could just be a reaction along the lines of, "there’s an object, so look at it/grab it etc. I have no clue how to think about proprioception and that sort of motor-like sensory thing, but maybe the same idea applies. Maybe sensory input is the same as motor input if both are about locations.
The idea that layer 5 processes which parts of the sensor are receiving input was was the starting point for the rest of those ideas. Sort of like processing entire macrocolumn states (binary input or no input), although maybe on a finer scale than that.
It’s sort of sequence processing, but maybe not in the same way that each state in temporal memory chains into the next to represent the sequence so far. I don’t see it as a replacement for temporal memory, just a step beforehand but still after spatial pooling generates the initial minicolumn states. Beyond that, it gets chaotic. What happens when propagating oscillations collide and how does that depend on the sequence of contact? It seems like they would produce chaotic patterns. I think I’m missing something and I need to figure out something more specific.
I don’t think proximal sensory input is incompatible with proximal motor input. How does the motor map in motor cortex line up with the idea of macrocolumns in sensory cortex?
Maybe each macrocolumn or the points on a finer map correspond to points in a motor space, so it represents a change in location of the sensors moved by the behavior. That would imply L5 represents a coordinate transform or movement. Jeff Hawkins says they think L5 represents displacements, I think in allocentric space: What spaces does L6a and L6b represent exactly relative to objects? Are these layers connected in any meaningful way? - #4 by jhawkins. Maybe L5 in egocentric regions does that, and L5 in allocentric regions represents the location itself because it is already relative to things since it’s allocentric.
Basically, 99% of what I write is spit-balling thoughts that pop into my head. Generally, cortex responds to the sensory input after subcortical structures, especially higher regions in the cortex and/or later parts of the cortical response, where latencies can be hundreds of millseconds. If subcortical structures are to utilize that information, the cortex needs to send predictions about that information ahead of time.
Here’s a more concrete example. In presaccadic predictive remapping, neurons start responding to the sensory input before a saccade to it. Some articles claim that, without shifting RFs ahead of time, you would see the blurred visual input caused by the saccade, so the brain skips ahead. There are some other reasons to do so, like generating rapid sequences of saccades where there isn’t a good visual input between saccades.
It’s a similar idea. There’s latency from subcortical structures to cortical structures (and latency between everything) so, at least when precise timing matters, predictive signals are required to contribute to the very initial response, or even a large part of the response for some long latency cortical responses.
I don’t have any particular structures in mind right now. Any subcortical sensory processing, which probably occurs in motor structures, too. For example, perhaps the superficial layers of superior colliculus, inferior colliculus, or pretectum. I don’t think LGN is a possibility for modulation or enhancement by L5.
After writing that, I read that they receive inputs from different types of basket cells somewhere. It’s hard to tell whether they share competition, though. Also, ST and TT don’t seem to compete with each other via martinotti cells (7), and they might form separate minicolumns (8). That latter study is based on synchronization and ST cells have longer latency sensory responses than TT cells, at least in barrel cortex, so they could still share minicolumns.
I think they’re as different as L4 and L2/3. They have a lot of similarities, so a single layer would probably work for some things and help figure out why there are separate layers despite the similarities.
[1] Three Types of Cortical Layer 5 Neurons That Differ in Brain-wide Connectivity and Function (Euiseok J. Kim, Ashley L. Juavinett, Espoir M. Kyubwa, Matthew W. Jacobs, and Edward M. Callaway, 2015) https://www.cell.com/neuron/fulltext/S0896-6273(15)00981-2
[2] Deep Cortical Layers are Activated Directly by Thalamus (Christine M. Constantinople and Randy M. Bruno, 2014) Deep Cortical Layers are Activated Directly by Thalamus - PMC
[3] Laminar Structure of Spontaneous and Sensory-Evoked Population Activity in Auditory Cortex (Shuzo Sakata and Kenneth D. Harris, 2009) https://www.cell.com/neuron/pdf/S0896-6273(09)00720-X.pdf?code=cell-site
[4] Corticostriatal cells in comparison with pyramidal tract neurons: contrasting properties in the behaving monkey (Bauswein et al., 1989) [PDF] Corticostriatal cells in comparison with pyramidal tract neurons: contrasting properties in the behaving monkey | Semantic Scholar
[5] Firing Mode-Dependent Synaptic Plasticity in Rat Neocortical Pyramidal Neurons (Barbara Birtoli and Daniel Ulrich, 2004) Firing Mode-Dependent Synaptic Plasticity in Rat Neocortical Pyramidal Neurons | Journal of Neuroscience
[6] Surround Integration Organizes a Spatial Map during Active Sensation (Scott R. Pluta, Evan H. Lyall, Greg I. Telian, Elena Ryapolova-Webb, and Hillel Adesnik, 2017)
[7] Disynaptic Inhibition between Neocortical Pyramidal Cells Mediated by Martinotti Cells (Gilad Silberberg and Henry Markram, 2007) https://www.cell.com/neuron/fulltext/S0896-6273(07)00111-0
[8] Lattice system of functionally distinct cell types in the neocortex (Hisato Maruoka, Nao Nakagawa, Shun Tsuruno, Seiichiro Sakai, Taisuke Yoneda, and Toshihiko Hosoya, 2017) http://science.sciencemag.org/content/358/6363/610