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{1353} 
ref: 0
tags: PEDOT electropolymerization electroplating gold TFB borate counterion acetonitrile
date: 10182016 07:49 gmt
revision:3
[2] [1] [0] [head]


PMID20715789 Investigation of near ohmic behavior for poly(3,4ethylenedioxythiophene): a model consistent with systematic variations in polymerization conditions.
PMID24576579 '''Improving the performance of poly(3,4ethylenedioxythiophene) for brain–machine interface applications"
PEDOTmodified integrated microelectrodes for the detection of ascorbic acid, dopamine and uric acid
 
{1356}  
PMID27705958 Chronic in vivo stability assessment of carbon fiber microelectrode arrays.
 
{1355}  
PMID25705966 Robust and fragile aspects of cortical blood flow in relation to the underlying angioarchitecture.
 
{1354} 
ref: 0
tags: David Kleinfeld penetrating arterioles perfusion cortex vasculature
date: 10172016 23:24 gmt
revision:1
[0] [head]


PMID17190804 Penetrating arterioles are a bottleneck in the perfusion of neocortex.
 
{1270} 
ref: 0
tags: gold micrograin recording electrodes electroplating impedance
date: 10172016 20:28 gmt
revision:5
[4] [3] [2] [1] [0] [head]


PMID23071004 Gold nanograin microelectrodes for neuroelectronic interfaces.
 
{1352}  
PMID27571550 Stable longterm chronic brain mapping at the singleneuron level.
 
{1327}  
PMID26436341 Threedimensional macroporous nanoelectronic networks as minimally invasive brain probes.
 
{1351}  
PMID20345247 Neural mechanisms for interacting with a world full of choices
 
{1350}  
A Wireless 32Channel Implantable Bidirectional Brain Machine Interface
 
{1349} 
ref: 0
tags: bone regrowth hyperelastic 3d print implant hydroxyapatite polycaptolactone
date: 09302016 18:27 gmt
revision:0
[head]


 
{1348}  
Heller Lecture  Prof. David Kleinfeld
 
{1347} 
ref: 0
tags: laser induced breakdown spectroscopy for surgery tissue differentiation
date: 09222016 19:26 gmt
revision:0
[head]


PMID25426327 Laser induced breakdown spectroscopy for bone and cartilage differentiation  ex vivo study as a prospect for a laser surgery feedback mechanism.
 
{1346} 
ref: 0
tags: super resolution imaging PALM STORM fluorescence
date: 09212016 05:57 gmt
revision:0
[head]


PMID23900251 Parallel superresolution imaging
 
{1345} 
ref: 0
tags: nucleus accumbens caudate stimulation learning enhancement MIT
date: 09202016 23:51 gmt
revision:1
[0] [head]


 
{1344}  
There is no such thing as free will The first half of the article you linked ruffled my feathers quite a bit; the second half did a fair job at unruffling them, but not completely. In my opinion, free will is both the manifestation and act of normal deliberative, cognitive, and emotional processes, processes that rely both geneticallyencoded substrates and sociallyingrained norms, morals, and knowledge. To say that free will arises in a deterministic processes, hence people are deterministic is true, but only philosophically (hence restrictively), and *not* scientifically. This is because:
If we could understand any individual’s brain architecture and chemistry well enough, we could, in theory, predict that individual’s response to any given stimulus with 100 percent accuracy. This is false! The brain is a stochastic, nondeterministic, quasichaotic system; signals are simultaneously reliable and highentropy to maximize information throughput (e.g. optic nerve, corpus callosum) *relative to metabolic expenditure*; synapses are almost perfectly stochastic, with a probability of transmitter release around 50% upon depolarization; most of the motor noise in e.g. high precision skilled tasks (like freethrows) are *internally generated* due to the noisy nature of the cortexbasal gangliathalamus control loop. (Professional athletes reduce this noise partly by dedicating more cortical area to a given skill, thereby averaging over more stochastic estimators. ) An important corollary of (1) is that 'free will' or 'agency' is a vehicle for causal and creditassignment networks to pierce the shell of individuality. Other people's value of certain behaviors is reflected in an individual's ability to chose based on them. In this sense, the 'free' in free will is a misnomer, as (in my conception), having free will actually creates a greater responsibility to the world. And, ecologically speaking, this is a very good thing! To be individual and to have free will is to be able to make (individual, free) choices based on some weighted assessment of personal and network effects  to be constrained. Other commentary: Advocating the perpetuation of untruths would breach their integrity and violate a principle that philosophers have long held dear: the Platonic hope that the true and the good go hand in hand. That's because 'free will is an illusion' is a false statement. Also see William James. In [Waller's] view, free will and determinism are not the opposites they are often taken to be; they simply describe our behavior at different levels. Yes, except for the fact that no level of our nervous system is deterministic. They didn’t pick their genes. They didn’t pick their parents. They didn’t make their brains, yet their brains are the source of their intentions and actions.” In a deep sense, their crimes are not their fault. Recognizing this, we can dispassionately consider how to manage offenders in order to rehabilitate them, protect society, and reduce future offending. One of the few statements I wholly agree with in the essay. The networks of causality and blame/credit assignment should both hook individuals as well as cast a net across history and group dynamics (you like my fishing analogy right); in many cases, the cause of crime is only partially a result of faulty decisionmaking, greater social issues should be brought to trial (not a very extreme or novel view here, but i wish to raise it in light of the network analogy). The current judicial system does not have a good tiein to the effector network, by constitutional design. Likewise, I disagree with his analogy between gun violence and hurricane Katrina; the purpose of the justice system is to judge the causes of an act; there are causes for violence and they should be named (and something should be done about them... just like global warming.)  
{1343}  
 
{1342}  
http://amasci.com/tesla/Tap_Drill_Chart.html by way of: https://m.reddit.com/r/engineering/comments/4ry07t/does_anyone_have_a_stored_copy_of_this_tap_and/  
{1341} 
ref: 0
tags: image registration optimization camera calibration sewing machine
date: 07152016 05:04 gmt
revision:20
[19] [18] [17] [16] [15] [14] [head]


Recently I was tasked with converting from image coordinates to real world coordinates from stereoscopic cameras mounted to the endeffector of a robot. The end goal was to let the user (me!) click on points in the image, and have the robot record that position & ultimately move to it. The overall strategy is to get a set of points in both image and RW coordinates, then fit some sort of model to the measured data. I began by printing out a grid of (hopefully evenlyspaced and perpendicular) lines via a laserprinter; spacing was ~1.1 mm. This grid was manually aligned to the axes of robot motion by moving the robot along one axis & checking that the lines did not jog. The images were modeled as a grating with quadratic phase in $u,v$ texture coordinates: ${p}_{h}(u,v)=\mathrm{sin}(({a}_{h}u/1000+{b}_{h}v/1000+{c}_{h})v+{d}_{h}u+{e}_{h}v+{f}_{h})+0.97$ (1) ${p}_{v}(u,v)=\mathrm{sin}(({a}_{v}u/1000+{b}_{v}v/1000+{c}_{v})u+{d}_{v}u+{e}_{v}v+{f}_{v})+0.97$ (2) $I(u,v)=16{p}_{h}{p}_{v}/(\sqrt{2+16{p}_{h}^{2}+16{p}_{v}^{2}})$ (3) The 1000 was used to make the parameter search distribution more spherical; ${c}_{h},{c}_{v}$ were bias terms to seed the solver; 0.97 was a dutycycle term fit by inspection to the image data; (3) is a modified sigmoid. $I$ was then optimized over the parameters using a GPUaccelerated (CUDA) nonlinear stochastic optimization: $({a}_{h},{b}_{h},{d}_{h},{e}_{h},{f}_{h}\mid {a}_{v},{b}_{v},{d}_{v},{e}_{v},{f}_{v})=\mathrm{Argmin}{\sum}_{u}{\sum}_{v}(I(u,v)\mathrm{Img}(u,v){)}^{2}$ (4) Optimization was carried out by drawing parameters from a normal distribution with a diagonal covariance matrix, set by inspection, and mean iteratively set to the best solution; horizontal and vertical optimization steps were separable and carried out independently. The equation (4) was sampled 18k times, and equation (3) 34 billion times per frame. Hence the need for GPU acceleration. This yielded a set of 10 parameters (again, ${c}_{h}$ and ${c}_{v}$ were bias terms and kept constant) which modeled the data (e.g. grid lines) for each of the two cameras. This process was repeated every 0.1 mm from 0  20 mm height (z) from the target grid, resulting in a sampled function for each of the parameters, e.g. ${a}_{h}(z)$ . This required 13 trillion evaluations of equation (3). Now, the task was to use this model to generate the forward and reverse transform from image to world coordinates; I approached this by generating a data set of the grid intersections in both image and world coordinates. To start this process, the known image origin ${u}_{\mathrm{origin}}{\mid}_{z=0},{v}_{\mathrm{origin}}{\mid}_{z=0}$ was used to find the corresponding roots of the periodic axillary functions ${p}_{h},{p}_{v}$ : $\frac{3\pi}{2}+2\pi {n}_{h}={a}_{h}uv/1000+{b}_{h}{v}^{2}/1000+({c}_{h}+{e}_{h})v+{d}_{h}u+{f}_{h}$ (5) $\frac{3\pi}{2}+2\pi {n}_{h}={a}_{v}{u}^{2}/1000+{b}_{v}uv/1000+({c}_{v}+{d}_{v})u+{e}_{v}v+{f}_{v}$ (6) Or .. ${n}_{h}=\mathrm{round}(({a}_{h}uv/1000+{b}_{h}{v}^{2}/1000+({c}_{h}+{e}_{h})v+{d}_{h}u+{f}_{h}\frac{3\pi}{2})/(2\pi )$ (7) ${n}_{v}=\mathrm{round}(({a}_{v}{u}^{2}/1000+{b}_{v}uv/1000+({c}_{v}+{d}_{v})u+{e}_{v}v+{f}_{v}\frac{3\pi}{2})/(2\pi )$ (8) From this, we get variables ${n}_{h,\mathrm{origin}}{\mid}_{z=0}\mathrm{and}{n}_{v,\mathrm{origin}}{\mid}_{z=0}$ which are the offsets to align the sine functions ${p}_{h},{p}_{v}$ with the physical origin. Now, the reverse (world to image) transform was needed, for which a twostage newton scheme was used to solve equations (7) and (8) for $u,v$ . Note that this is an equation of phase, not image intensity  otherwise this direct method would not work! First, the equations were linearized with three steps of (911) to get in the right ballpark: ${u}_{0}=640,{v}_{0}=360$ ${n}_{h}={n}_{h,\mathrm{origin}}{\mid}_{z}+[30..30],{n}_{v}={n}_{v,\mathrm{origin}}{\mid}_{z}+[20..20]$ (9) ${B}_{i}=\left[\begin{array}{c}\frac{3\pi}{2}+2\pi {n}_{h}{a}_{h}{u}_{i}{v}_{i}/1000{b}_{h}{v}_{i}^{2}{f}_{h}\\ \frac{3\pi}{2}+2\pi {n}_{v}{a}_{v}{u}_{i}^{2}/1000{b}_{v}{u}_{i}{v}_{i}{f}_{v}\end{array}\right]$ (10) ${A}_{i}=\left[\begin{array}{ccc}{d}_{h}& & {c}_{h}+{e}_{h}\\ {c}_{v}+{d}_{v}& & {e}_{v}\end{array}\right]$ and $\left[\begin{array}{c}{u}_{i+1}\\ {v}_{i+1}\end{array}\right]=\mathrm{mldivide}({A}_{i},{B}_{i})$ (11) where mldivide is the Matlab operator. Then three steps with the full Jacobian were made to attain accuracy: ${J}_{i}=\left[\begin{array}{ccc}{a}_{h}{v}_{i}/1000+{d}_{h}& & {a}_{h}{u}_{i}/1000+2{b}_{h}{v}_{i}/1000+{c}_{h}+{e}_{h}\\ 2{a}_{v}{u}_{i}/1000+{b}_{v}{v}_{i}/1000+{c}_{v}+{d}_{v}& & {b}_{v}{u}_{i}/1000+{e}_{v}\end{array}\right]$ (12) ${K}_{i}=\left[\begin{array}{c}{a}_{h}{u}_{i}{v}_{i}/1000+{b}_{h}{v}_{i}^{2}/1000+({c}_{h}+{e}_{h}){v}_{i}+{d}_{h}{u}_{i}+{f}_{h}\frac{3\pi}{2}2\pi {n}_{h}\\ {a}_{v}{u}_{i}^{2}/1000+{b}_{v}{u}_{i}{v}_{i}/1000+({c}_{v}+{d}_{v}){u}_{i}+{e}_{v}v+{f}_{v}\frac{3\pi}{2}2\pi {n}_{v}\end{array}\right]$ (13) $\left[\begin{array}{c}{u}_{i+1}\\ {v}_{i+1}\end{array}\right]=\left[\begin{array}{c}{u}_{i}\\ {v}_{i}\end{array}\right]{J}_{i}^{1}{K}_{i}$ (14) Solutions $(u,v)$ were verified by plugging back into equations (7) and (8) & verifying ${n}_{h},{n}_{v}$ were the same. Inconsistent solutions were discarded; solutions outside the image space $[0,1280),[0,720)$ were also discarded. The process (10)  (14) was repeated to tile the image space with gird intersections, as indicated in (9), and this was repeated for all $z$ in $(0..0.1..20)$ , resulting in a large (74k points) dataset of $(u,v,{n}_{h},{n}_{v},z)$ , which was converted to full realworld coordinates based on the measured spacing of the grid lines, $(u,v,x,y,z)$ . Between individual z steps, ${n}_{h,\mathrm{origin}}{n}_{v,\mathrm{origin}}$ was reestimated to minimize (for a current $z\prime $ ): $({u}_{\mathrm{origin}}{\mid}_{z\prime +0.1}{u}_{\mathrm{origin}}{\mid}_{z\prime +0.1}{)}^{2}+({v}_{\mathrm{origin}}{\mid}_{z\prime +0.1}+{v}_{\mathrm{origin}}{\mid}_{z\prime}{)}^{2}$ (15) with gridsearch, and the method of equations (914). This was required as the stochastic method used to find original image model parameters was agnostic to phase, and so phase (via parameter ${f}_{}$ ) could jump between individual $z$ measurements (the origin did not move much between successive measurements, hence (15) fixed the jumps.) To this dataset, a model was fit: $\left[\begin{array}{c}u\\ v\end{array}\right]=A\left[\begin{array}{ccccccccccccccccccccccccccc}1& & x& & y& & z& & x{\prime}^{2}& & y{\prime}^{2}& & {\textstyle \prime}z{\prime}^{2}& & {w}^{2}& & x\prime y\prime & & x\prime z\prime & & y\prime z\prime & & x\prime w& & y\prime w& & z\prime w\end{array}\right]$ (16) Where $x\prime =\frac{x}{10}$ , $y\prime =\frac{y}{10}$ , $z\prime =\frac{z}{10}$ , and $w=\frac{20}{20z}$ . $w$ was introduced as an axillary variable to assist in perspective mapping, ala computer graphics. Likewise, $x,y,z$ were scaled so the quadratic nonlinearity better matched the data. The model (16) was fit using regular linear regression over all rows of the validated dataset. This resulted in a second set of coefficients $A$ for a model of world coordinates to image coordinates; again, the model was inverted using Newton's method (Jacobian omitted here!). These coefficients, one set per camera, were then integrated into the C++ program for displaying video, and the inverse mapping (using closedform matrix inversion) was used to convert mouse clicks to realworld coordinates for robot motor control. Even with the relatively poor wideFOV cameras employed, the method is accurate to $\pm 50\mu m$ , and precise to $\pm 120\mu m$ .  
{711}  
PMID19299587[0] Optical Deconstruction of Parkinsonian Neural Circuitry.
____References____
 
{1340}  
PMID26867734 A biophysical model of the cortexbasal gangliathalamus network in the 6OHDA lesioned rat model of Parkinson’s disease
Overall, not a bad paper. Not very well organized, which is not assisted by the large amount of information presented, but having slogged through the figures, I'm somewhat convinced that the model is good. This despite my general reservations of these models: the true validation would be to have it generate actual behavior (and learning)! Lacking this, the approximations employed seem like a step forward in understanding how PD and DBS work. The results and discussion are consistent with {1255}, but not {711}, which found that STN projections from M1 (not the modulation of M1 projections to GPi, via efferents from STN) truly matter.
