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.