Motor Cortex Feedback Influences Sensory Processing by Modulating Network State

Zagha, E., Casale, A.E., Sachdev, R.N.S., McGinley, M.J., McCormick, D.A. (2013). Motor Cortex Feedback Influence Sensory Processing by Modulating Network State. Neuron 

Neuromodulators classically recognized as modulating network state, but slow and spatially distributed. Glutamate may play a role in modulation. Analyzing motor and sensory cortical areas of a specific whisker.

Non-whisking: low-frequency rhythms in M1 and S1. Highly coherent
Whisking: increased gamma in M1 and S1 - "activated state". Could see "activated state" even when no whisking or other obvious behavior.

Inactivation of M1 with muscimol reduced whisking, slowed network activity, reversed phase offset of coherence, and generally reduced frequencies in S1. Low freqs increase, high freqs decrease.

ChR2 activation of M1 (not sure which neurons - AAV virus and EMX1 Cre expression) decreases delta power in S1 and increases activity. In anesthitized animals, graded activation of M1 decreases delta and increases gamma. Very rapid - tens of milliseconds. Laminar recordings show that slow oscillations were eliminated in all layers, increased spiking primarily in infraganular neurons.

Now they're doing some layer analysis. Stimulate the whisker and you get current sinks in 2/3, 4, 5, current sources in 1 and 6 in S1. Activate M1 get almost opposite pattern: sinks in 5, 6, 1; sources in 2/3.

Figure 5. Evidence for Involvement of the Corticocortical Feedback Pathway
(A and B) CSD plots of average S1 responses from an example experiment. Brief (5 ms) deflections of the principal whisker (A) evoked onset current sinks in layers IV, II/III, and V and current sources in layers I and VI. Brief (5 ms) vM1 stimuli (B) evoked onset current sinks in layers V, VI, and layer I and current sources in layers II/III. Stimulus durations are depicted by the colored boxes in the bottom left of each plot. Color scales represent ±10 mV/mm stimuli and ±5 mV/mm2 for vM1 stimuli.
(C) Synaptic responses from layer V S1 neurons in vitro, evoked by stimulating axons and terminals of vM1 neurons in S1. The 2 ms light pulses are indicated by blue dots below traces. Responses from a regular spiking (RS) neuron, consisting of a short latency EPSP at rest (top) and an EPSP-IPSP sequence (middle) when depolarized to just below spike threshold. Bottom: EPSP from a fast spiking (FS) neuron at rest.
(D) Population data, quantifying connection probabilities (left), and response amplitudes (right) from vM1 inputs onto regular spiking and fast spiking neurons in S1.
(E) In vivo S1 response to stimulation of vM1 axons in S1. Limiting direct stimulation to the corticocortical vM1 axons was sufficient to evoke S1 activation. Error bars represent SE. See also Figure S3.

They applied CNQX on the surface. Low concentration just blocked the L1 projection of M1, high concentration blocked L1 and L5 projection. L5 neurons still activitated with low concentration.

They next blocked thalamus with mucimol. M1 activation doesnt need thalamus.

Suppressing M1 during stimulation shifts S1 response to biphasic activation. There's an initial stimulus driven burst, followed by silence and LFP rebound, followed by another slower burst. M1 activation during stimulation reduces the variability of responses in S1.


In general it seems that the feedback pathway puts S1 in the "up state", which is also useful because more sensory information can be processed in the up state. There's probably up and down states for different layers. The M1 feedback to the different layers is probably for different purposes -- S1 both needs regulation based on M1 information, and the informatoin itself (S1 needs to know motor state for correct processing).

Schmidhuber

Looking at some of Schmidhuber's papers. Here's a few:

Ciresan, D., Meier, U., Masci, J., Schmidhuber, J. (2012). Multi-column deep neural network for traffic sign classification. Neural Networks.

Ciresan, D.C., Meier, U., Gambardella, L., Schmidhuber, J. (2012). Deep, Big, Simple Neural Nets for Handwritten Digit Recognition. Neural Computation.

Schmidhuber, J. (2009). Ultimate Cognition a la Godel. Cogn Comput.

Here's the first author, Ciresan, website: http://www.idsia.ch/~ciresan/. Theres some interesting things here.

Going to go through the first one. This won the final phase of hte "German traffic sign recognition benchmark" with better-than-human recognition rate of 99.46%.

The Deep Neural Network (DNN) is hierarchical neural network that alternates convolution (i.e. receptive fields/simple cells) with max-pooling (i.e. complex cells). They cite this paper in reference to the implementation of their machine -- takes days instead of months to learn the signs with GPU acceleration.

Here's the basic architecture:

 The convolution layer is basically just a receptive field for each neuron -- the same receptive field shape is spread out over all the pixel space. Just weighted sum, bias and non-linear activation function. Next is a max-pooling layer, which outputs the maximum activation over non-overlapping rectangular regions. The last layers are fully connected and form 1D feature vector. Softmax such that the output is the probability of the image belonging to a particular class.

Each instance of the training set is distorted by affine transforms to get more samples: translation, rotation, scaling. This helps prevent overfitting and adds in wanted invariances.

The output is essentially the average of several DNN columns. Various columns are trained on the same inputs, or inputs preprocessed in different ways. Preprocessing does contrast normalization, histeq, adaptive histeq, and imadjust.

We use a system with a Core i7–950 (3.33 GHz), 24 GB DDR3, and four graphics cards of type GTX 580.

Looking at his other stuff, he has some good ideas. One big one is "Flat Minima Search" http://www.idsia.ch/~juergen/fm/, where the idea is that you are looking for a "flat" local minima, where the error remains approximately constant with nearby weight parameters. This adds a simplicity bias and thus results in better generalizations. He also talks about this idea of detectors being independent, and basically that you have predictors which try and guess what a detector does based on what everything else is doing, and then the detectors try and do something different than everybody else.

Jeremy Biane and Motor Cortex

Just went to Jeremy's defense. It was good, he used retrograde markers to label L5 spinal cord projecting pyramidal cells that projected either to C4 or C8 which control proximal and distal forearm muscles respectively. Upon training of a distal muscle task he recorded and tested to see if C4 populations and C8 population were more connected to each other after learning the motor skill.

Since it was a primarily distal forelimb behavior only C8 projecting neurons showed differences in their structure and connectivity after training. They formed more connections (from about 2% connectivity to 6%), but they were weaker on average (but that may because there are just so many more new connections). No changes C4<->4 or C4<->8.

Its particularly interesting because motor cortex has basically no layer 4. In fact he said that layer 5 in M1 is 5a and 5b, but no layer 4. Layer 5 seems to be a major "output" layer of cortex, and perhaps it is the layer that can set up these arbitrary patterns (like the heteroclinic channel layer, or the CPG layer). It seems that layer 5 projects mainly up the hierarchy. L5 from V1 goes to higher-order thalamus, L6 goes feedback to LGN.

Characterization, Stability and Convergence of Hierarchical Clustering Methods

Carlsson, G., Memoli, F. (2010). Characterization, Stability and Convergence of Hierarchical Clustering Methods. Journal of Machine Learning Research 11: 1425-1470.

Kleinberg, 2002: There exists no clustering algorithm that satisfies scale invariance, richness and consistency. Natural question is what about hierarchical clusters?

Going to skip some math and notation. Just formalities as this is a math paper.

A hierarchical clustering method is a map that assign a dendrogram to a finite metric space. HC methods operate on a metric space, where the points in the space are denoted as X and the distances between points are denoted as D. D is the distance matrix and is X x X in size.

Yeah, so this goes into some deep theory about HC methods and that HC is the same as mapping from a metric space to an ultrametric space (a metric space satisfies the triangle inequality, and in an ultrametric space all triangles are isocoles(?)) . Not a lot of practicality in this paper.

They also talk about similarities between dendrograms.

What can different brains do with reward?

Murray, E.A., Wise, S.P., Rhodes, S.E.V. (2011) What can different brains do with reward? Neurobiology of Sensation and Reward. ed. Gottfried, J.A. Boca Raton (FL): CRC Press; 10

This looks like a good perspective of brain evolution in the context of reward-based learning/problem solving.

Animals evolved as award seekers, evolutionary view of reward from 3 main clades: early vertebrates, early mammals, and primates.

The opening section talks about the history of brain-evolution science. Many pitfalls, controversy and disargreement, lots of people were just completely wrong.

Some definitions:
Homology: A structure or behavior is homologous to another if two or more descendant species have inherited it from their most recent common ancestor.

Analogy: ancestor. Analogy is a statement about function, not ancestry. A structure or behavior is analogous to another if it subserves the same function. The classic example involves wings. Insects, birds, and bats have wings

Homoplasy: something similar that has evolved in different lineages through parallel or convergent evolution.

Invertebrates are arbitrary grouping, protostomes are group of insects, mollusks and segmented worms. Also deuterostomes, which separated 600 MYA. Protostomes and vertabrates have evolved tremendously since our common ancestor. Vertabrates evolved with deuterostomes.

Three cladograms, arranged from top to bottom. The middle and bottom cladograms each develop one of the lineages from the cladogram above, as shown by the connecting arrows. The circled letters, A, B, and C, reference common ancestors referred to in the text. Beneath the names of selected clades, which are outlined in boxes, shared derived traits appear in italics. Abbreviation: DA, dopamine.


The telencephalon and early dopamine system were most important evolutionary developments. Early telencephalon was olfactory bulb and a homologue of piriform cortex. Contained homologue of basal ganglia and probably amygdala and hippocampus.

Mammals use a mixture of old and new features to deal with reward. New: neocortex. Hipp and piriform are still allocortex, there's some transition cortical areas. Rodents have similar prefrontal architecture, but not completely homologous to primates.

Primates have main type of frontal lobe: agranular cortex (no layer 4) and areas with subtle layer 4, collectively called granular prefrontal cortex (PFg). This is a primate innovation as all primates have PFg. Rodents have the agranular parts.


Nonvertabrates can deal with reward, associative learning (pavlovian) seems to be something before protostome/deuterostome split. Instrumental conditioning also shown in invertabrates.

Dopamine System: regulates the classical conditioning method, error signal etc. Dopamine could be acting across several orders of time to influence reward-based behavior.

Basal ganglia: confounded because of its role in both reward and movement processing, but seems to be movement regulated by reward -- computes the cost of energy requirements. "Bradykinesia represents an implicit decision not to move fast because of a shift in the cost/benefit ratio of the energy expenditure needed to move at normal speed."

Amygdala: Reinfocer devaluation == stop eating once you're satieted. Amygdala lesions remove this ability. Lots of other roles, delay signal, affective signals, controls quicker behavioral changes.

Hippocampus: Spatial computations. Large place fields may involve recognition of contexts and could be important for reward processing.


Neocortex in mammals has allowed for even more control over reward. Agranular FC is homologous in rodents and primates although there are now differences from the last 10 MYA of evolution. Mammals have imporved "executive function", mediated by agranular frontal cortex, including top-down modulatory function that biases competition among different brain systems engaged in and competing for control of behavior.

Mammals have: anterior cingulate (AC), infralimbic (IL), prelimbic (PL), agranular orbital frontal (OFa) and agranular insular (Ia). These different parts afford mammals greater flexibility in their reward-seeking behavior. Ia gets many visceral signals -- Ia functions in interoception pain, itch, temperature, metabolic state, lungs, heart, baroreceptors and digestive tract.

Several dissociated memory systems combine to guide reward-seeking behavior. Nonmammalian vertebrates (birds, reptiles), appear to have problems overriding their innate behavioral responses.

Anterior Cingulate: biases behavioral control towards one among multiple competing stimuli. Weighs the cost-benefit: is reward worth the effort? AC allows the animal to weigh more behavioral options (it can present the reward system with more possible behavioral choices).

Prelimbic cortex: involved in regulating goal-directed behaviors in cases where they compete with habitual stimulus-responses. Helps encode the response-outcome associations (but not execution of them).

Inframlimbic cortex: seems to be opposite of PL, promotes behavioral control of S-R associations. Plays a role in extinction learning -- biases behavior towards more recent newly learned rules (that a stimulus no-longer gives reward) than to the older more stongly associated rules).

Orbitofrontal Cortex: problems between rodent and primate OFC -- no homolog in rodents with granular orbitorfrontal (PFo), only agranular has homolog (OFa). Neural activity in OFa reflects reward expectation, especially sensory-specific properties of the reward. OFa lesions impair ability to make decisions on basis of reward expectations.The OFa contributes more to learning the associations between CSs and the sensory aspects of reward (e.g. taste). It doesn't compute the biological value per se.

Agranular Insular Cortex: Relates sensory properties of the reward to the instrumental motivations, playing a complementary role to OFa. This means that Ia and OFa likely store the sensory related properties of rewards such that they can be used during recall to help evaluate different reward-based decisions.


Primates have PFg -- granular parts of frontal cortex. Also extra sensory areas like IT. Granular orbital frontal (PFo) gets strong projections from IT and other posterior sensory areas including auditory cortex. PFo is one of earliest sites for convergence of visual information with visceral inputs. Primates can then link visceral, olfactory and gustatory inputs with high-order visual stimuli. PFo represents high-level details and conjunctions of sensory features for rrewards, the magnitude of reward, the probability of reward and the effort required to obtain it. Computes reward in a common currency to pit risk vs. reward in decisions.

Can learn rules and strategies for rewards instead of stimulus and action relations to outcomes. Can dissociate the emotional value of reward with a value-less reward signal.

Humans can do longer term learning, and "mental time-travel" which help them with reward processing. Further they can talk about reward and have secondary rewards -- i.e. I don't want to want to smoke.

Generating Coherent Patterns of Activity from Chaotic Neural Networks

Sussillo, D., Abbott, L.F. (2009). Generating Coherent Patterns of Activity from Chaotic Neural Networks. Neuron 63 544-557.

I wanted to get into the Larry Abbott stuff as it seemed to pertain to the local bending model I was envisioning. So this paper covers these ideas of using chaotic random networks as the basis for learning arbitrary patterns. Essentially because of the high dimensional space the pattern is easy to read out with some simple linear combination. They expand upon previous work and make the model more complex and incorporate "FORCE" learning.

The idea of FORCE learning is that you start with a fast learn (pretty easy because its just a linear thing), but you don't immediately try to get the error all the way to zero. You keep a small amount of error that then decreases slowly. The reason for this is that the networks tend to destabilize and ruin the output. Part of the key of keeping the network in line is that the output feedsback into the network, and the chaos gets overcome by the pattern. Essentially with no stimuli the network tends to be chaotic, but when given a periodic stimuli to learn, the network loses its chaos.

From the examples its quite good at learning arbitrary patterns. They do an interesting PCA analysis of the network after its learned a particular pattern. The network is initiated with the same random connectivity, and then taught to learn the same pattern. The readout weights are initiated at different random strengths. Once the pattern is learned, the major components (the first few PCs) converge to the same point, regardless of where the readout weights were initialized. The later components end up being at a different place.

The learning performs best right on the verge of chaos. By tuning a network parameter (essentially the average synaptic strength), they can vary the amount of chaos in the network. It performs poorly at learning tasks when the network is not in the chaotic regime, but it also cannot work when the network is too chaotic. The learned pattern has to be able to overcome the intrinsic network chaos, and the network needs some chaos so that it can learn the pattern.

Finally they show a network that can do interesting input-output transformations. They add some control inputs, which essentially connect to the network randomly and train the outputs when the appropriate control inputs come on.

(A) Network with control inputs used to produce multiple output patterns (synapses and readout weights that are modifiable in red).
(B) Five outputs (one cycle of each periodic function made from three sinusoids is shown) generated by a single network and selected by static control inputs.
(C) A network with four outputs and eight inputs used to produce a 4-bit memory (modifiable synapses and readout weights in red).
(D) Red traces are the four outputs, with green traces showing their target values. Purple traces show the eight inputs, divided into ON and OFF pairs associated with the output trace above
them. The upper input in each pair turns the corresponding output on (sets it to +1). The lower input of each pair turns the output off (sets it to  1). After learning, the network has implemented a 4-bit memory, with each output responding only to its two inputs while ignoring the other inputs.


The 4-bit memory is a pretty cool example. For each bit there is an on and off input pulse, which flips the state of the corresponding readout unit. So in the network there are 16 fixed points corresponding to all possible bit combinations.

Then they have a much more complex example of the network implementing running and walking like behaviors. They got some motion capture data and trained the network to mimic the joint angles for 95 different joints. And the network was able to learn both walking and running.

The network synaptic structure is linear. Firing rate model that goes through a tanh output non-linearity

Leech mechanosensory model - Fractional derivative

Ok, now I've incorporated the T cells, but I thought a simple linear filter would enable rates like in the animal. But it wasn't quite right -- the T cells didnt fire on the off phase of the steps. So instead of doing the linear filters, I implemented fractional derivatives for the "filters". So now the P cell filter is the 0.3 order fractional derivative of the sensory signal, and the T cell filter is the 1.6 order fractional derivative.

Figure 1: Sensory signal, P and T cell currents based on fractional derivative of signal.

Figure 2: receptive fields of P and T cells.

Figure 3: P cell responses to sensory signal.

Figure 4: T cell responses to stimulus

Leech mechanosensory model - sensory representation

Ok, I'm going to spend a few days just working on this new model of the leech sensory system.

It starts with the touch as being a set of "pixels" that go around the skin and the value for each pixel is the instant pressure on the skin. So here is like what the touch stimuli look like:

Figure 1: The upper panel is the actual touch stimulus. Here there are just 10 pixels that go around the circumference of the body. We are going to transform this touch stimulus to a train of P cell spikes by both incorporating the receptive field and a temporal filter. In the second panel is the touch stimulus convolved with the P cell temporal filter. This is just a very stupid filter right now that is supposed to fit the P cell a little, but is nowhere near accurate. The final panel is the actual currents that will be injected into each P cell (note that there are 4 rows for each P cell).

Figure 2: Here the P cells are just single compartment IF neurons, and this is there responses to the touch stimulus shown in Figure 1.. The first panel is the voltages, with threshold of -58 mV and Reset of -62. The second panel is the actual spike times of the 4 P cells, and the final panel is the ISI for all 4 P cells. 

Figure 3: This is the P cell temporal filter. 

Multi-subject dictionary learning to segment an atlas of brain spontaneous activity

Varoquaux, G., Gramfort, A., Pedregosa, F., Michel, V., Thirion, B. (2012). Multi-subject dictionary learning to segment an atlas of brain spontaneous activity. Information Processing in Medical Imaging 6801.

Ok so a lot of the registration algorithms for fMRI might be relevant for my VSD data. This paper was recommended to me by Stephan Gerhard (from NS&B).

They take an approach that combines ICA with dictionary learning.

Start with a linear signal decomposition. Y is data time series (n timeponts x p voxels). V describes functional processes or structured measurement artifacts with k spatial latent variables (p x k). U describes the time series of the latent variables (n x k). Y = U * V

So they are setting up the problem to create a hierarchical generative model of the data, and they have an optimization procedure to find U and V.

The algorithms are written into the text as pseudo-code, varoquaux's website: http://gael-varoquaux.info/, and maybe some related code: https://github.com/GaelVaroquaux/canica

Theres also a package NiPy that has a lot of neuro-imaging algorithms. There may be some relevant algorithms there for my data. And also: http://scikit-learn.org/stable/

The rest of the paper is validation, but there are some other good references. I need to dive into the code to start getting some real insights into the applicability of this to my data.

Arbitrary heteroclinic orbits

Yeah so after hearing about crawling a bunch of times up here in MBL it seems clear that heteroclinic like orbits are more closely related to how CPGs work. This is in contrast to the Shilnikov style of doing CPGs, as they rely on intrinsic dynamics and pacemaker neurons -- effectively making CPGs out of coupled oscillators. The heteroclinic orbits don't require coupled oscillators and have the nice properties of being able to hold up the period in certain places like real CPGs. Plus, if you looked at a single CPG neuron that was part of a heteroclinic orbit, then you wouldn't see any intrinsic oscillatory dynamics. This seems to be the case most of the time for neurons.

So the question is how to make arbitrary types of heteroclinic orbits with neural-like models. There would have to be some population that begins to turn on and starts off as an attractor through some kind of recurrent feedback. But once the population turned on enough it acts like a source and the dynamics flow towards the next population. Each population would be a fixed point, but as you approach from one direction its attractive, and then it sends you off in another direction. But how to make this type of system is the question.

Dan Gerschwin

He came and gave a talk today at MBL. It was about genetics -- well really the "transcriptome", as reading all of the RNA is apparently much easier than the genome or the proteome. It was very interesting and the analysis he was doing was quite remarkable.

Most people look at up or down regulation of RNAs and try and correlate that with phenotype. He was actually looking at how different RNAs were correlated together, and then relating that to phenotype. Basically based on the correlation of different RNA expression levels he could build a distance matrix and then do graph-based analysis. He was showing how different RNAs cluster into "modules" and that modules were organized in reasonable ways -- many of the modules were related to the cell types and other pretty obvious differences. He had some cool ways of clustering and dimensional analysis that could be relevant to thesis work.

Neural pointers

So how does info get routed into working or hippocampal memory? Pointers! Working and hippocampal memory are not encoding the information about a memory, but rather pointing to PTV where the information is stored. These pointers are stored in the sequence of spikes during each theta cycle. When Frontal wants to remember something, then it burst activates the neurons which leads to some plasticity. This plasticity is bi-directional. The PTV neurons that are currently active reinforce together (so that the memory can be recalled more easily ala a hopfield net/local minima), and then reinforce with a hippocampal sequence. The hippocampal sequence also reinforces with all of PTV, so that when the hippocampal sequence gets activated all of the info in PTV will be reactivated as well.

Lets think about working memory a little more. I vaguely remember some talks/papers I've read about how working memory would be implemented by the brain. there is a region in frontal cortex where a slow oscillation similar to theta dominates. During this slow oscillation, about 7 neural pointers can be stored (in the human brain). Each pointer was reinforced with some frontal burst feedback, so that each of the pointers can reactivate the PTV that has all of the information. You can imagine how the working memory structure can just maintain the 7 pointers in its slow oscillation through some sort of recurrent activation and plasticity or through that STP mechanism. This is how the working memory is maintained over longer time-scales. Then the different pointers being stored in working memory can be reactivated by another frontal area (another burst signal or something), which brings back the information stored in PTV.

Hippocampus is basically the same, but the pointers are built over a slow timescale (during each theta cycle), and then a memory over a larger time-scale can be recalled more quickly by activating a sharp-wave ripple.

PTV and Frontal

So we are starting to build this picture about what the PTV (Parietal-Temproal-Visual) cortex is doing and fitting it in with the rest of the brain. What it seems is that PTV is encoding/representing/building the current and recent state of the world. When you open your eyes and see the world around you, recognize the objects, hear the sounds, the 3D structure, etc. all of this information is being represented in PTV. . There is a tremendous amount of information continually being encoded by this circuit, and this information is the immediate state of the mind you are experiencing.

Now, this is too much information to make sense of all at once. And this is where frontal cortex comes into play. Frontal cortex essentially feedsback to all of PTV (via layer 1). Frontal cortex enhances the activity in subsets of PTV and can route subsets of PTV into working or long-term memory. To encode a long term memory, only the parts of PTV that are strongly modulated by top-down attentional feedback make it. Essentially attentional feedback activates the neurons and gets them to burst. This doesnt really change the information anywhere in PTV, but enhances is it. The burst is a signal that carries the same info about the sensory environment but tells other structures to operate differently. So to work something into say hippocampal memory (I guess this is more like medium-term), frontal cortex burst synchronizes every region of the brain it wants to remember. The entire hierarchy can be encoded in the local cortical circuits, and the hippocampus plays the role as the pointer and relates the cortical information being saved with a time-place pointer in the hippocampus. When the memory is recalled, the hippocampus can be reactivated which then reactivates the cortical areas.

PTV is creating a hierarchical model of the world from all of the information from the senses. This model is actually non-temporal (ugh, confusing in this case im referring to temporal as in time) -- it has temporal structure, but it is time-invariant. Your phone stays your phone, etc. Frontal cortex is more in charge of time. Frontal cortex is building a hierarchy to predict the future at different temporal scales. Frontal cortex is pulling information about the current state of the world from temporal cortex and routing it through working and long-term memory. Frontal routes PTV into working memory, and can reactivate hippocampal memory to route into working memory to make better predictions about the future.

mixed selectivity

nature paper about "mixed selectivity" neurons in PFC. Sounds a lot like population coding...

http://www.nature.com/nature/journal/vaop/ncurrent/full/nature12160.html

Tirin Moore

Just went to a talk by Tirin Moore from stanford. He studies visual attention and was doing physiology work in FEF and V4. He had a lot of interesting data, but it was kind of all over the place.

His model had neurons with D1 receptors in 2/3 of FEF, and these were the ones that fedback to V4. He showed through antidromic AP annihilation that it was mainly the persistent firing cells that fedback to V4. He showed that modulation of D1 and D2 receptors in FEF have the same effect of behavior -- biasing the saccades to the RFs in FEF (although it was D1 antagonist and D2 agonist, which he said didnt matter because they have "U" shape properties). D1 changes had effects, however, on the properties of V4 neurons -- typically looking like enhanced attention. D2 had no real effect on V4, but had the behavioral result. D2's seemed to be more involved in the motor part of the saccade, and D1 in the attentional part.

It seemed like a clear case of Modulatory type of feedback projections. The neuron's were enhanced by FEF feedback, but required FF stimulation. The fact that the feedback was from the persistent firing neurons made sense to me -- keeping the modulation up in preparation for the stimulus to appear. Although, he said that the selectivity was narrowed, which goes against the kind of gain control modulation that I think of. However, this may just be because you're altering FEF in a non-sensical fashion, and that causes selectivity changes. And he was measuring V4 selectivity with oriented bars.