Temple University Neural Instrumentation Lab » Multi-Tuned Neuron Project http://www.obeidlab.com “Integrating hardware, software, and algorithm research in pursuit of brain-machine interface instrumentation” Thu, 22 Oct 2009 02:37:04 +0000 http://wordpress.org/?v=2.8.6 en hourly 1 Pneumatic Arm http://www.obeidlab.com/pneumatic-arm http://www.obeidlab.com/pneumatic-arm#comments Wed, 29 Oct 2008 15:57:00 +0000 Rich http://www.obeidlab.com/?p=516 This article provides complete documentation for purchasing, constructing, programming, and operating a Pneumatic Arm Truss. The PAT supports a two-degree FOM pneumatic arm for inclusion in the BMI Workbench.

System Operation

Startup

  1. Inspect T-35HD Air Compressor Filter. Rinse with soap & water as necessary.
  2. Inspect T-35HD air tank for water (valve on bottom of tank). Tilt tank and drain if necessary. Ensure valve is closed prior to use. (Stem will be at its longest when closed.)
  3. Inspect LFR-D-MIDI & LF-D-5M-MIDI regulators for condensate. Release condensate if it is within 10mm below filter element by opening the lower blue plugs during system operation.
  4. Adjust the LFR-D-MIDI regulator. Pull blue pressure setting button upwards to unlock it (away from housing). Turn pressure setting button in the ‘-’ direction as far as possible.
  5. Place T-35HD power switch in auto position. The pump will automatically maintain between 100 – 140 PSIG.
  6. Turn the LFR-D-MIDI pressure setting button in the ‘+’ direction until the desired pressure is shown on its manometer. (Note: The input pressure must be at least 1 bar greater than the output pressure.)
  7. Press the the LFR-D-MIDI pressure setting button down to secure it against unintentional turning.

Operation

  1. To Be Completed.

Shutdown

  1. Remove supply pressure. Adjust the LFR-D-MIDI regulator. Pull blue pressure setting button upwards to unlock it (away from housing). Turn pressure setting button in the ‘-’ direction as far as possible.
  2. Power OFF compressor
  3. Release compressor tank pressure and excess condensate via drain valve.
  4. Power OFF setpoint voltage
  5. Power OFF supply voltage

Schematics

Software

videoInput

AutoCAD

NI

Hardware

Festo

  • http://www.festo.com/cms/en-us_us
  • 1.847.759.2600
  • Account #20344983
  • Customer Service: 1.800.993.3786
  • Our Representative: Pat Sabharwal 1.847.759.2629 (Cell: 630.487.0479)
  • Tech Support: 1.866.463.3786
Component Name Part # Pubs Notes
LFR-1/4-D-MIDI Filter/Regulator 186481 Specifications
  • Regulates the compressed air supplied to the set working pressure and compensates for fluctuations in pressure.
  • Cleans the compressed air of dirt particles and condensed water.
  • Filters to 40µm particles
LF-D-5M-MIDI Filter 186460 Specifications
  • Filters to 5µm particles
  • Max 230 PSI (16 bar)
VPPM-6F-L-1-F-0L10H-V1N-S1 Proportional Pressure Regulator 543432 Specifications
  • 24 VDC
VABM-P1-SF-G18-4-P3 Sub-base 542254 See VPPM-6F-L-1-F-0L10H-V1N-S1 Proportional Pressure Regulator  
Sub-base Blanking Plug 3570    
D-3/8I-1/2A Reducing Nipple 3585    
DMSP-10-360N(or 240N) RM-RM Fluidic Muscles 541403 Manual
  • Max Pressure: 116 PSI (8 bar)
  • Normal Operating Range: 0 -15% contraction
  • Max Operating Range: 0 – 25% contraction
  • Max Diameter (@ contraction): 22mm
  • Connecting Thread: M8
  • 541403 W908 Connectors
  • Should be inspected every 500,000 strokes for cracks and blistering
  • Service life can be increased if pressure is exhausted on opposing end from input
QSL-H-1/8-1/4-U-M Push-in/threaded L-fitting 533235 Specifications
  • Size Standard
  • Nominal size 4.5 mm
  • Type of seal on screw-in stud coating
  • Working pressure -0.95 – 10 bar
  • Ambient temperature 0 – 60 °C
  • Max. tightening torque 7 Nm
  • Product weight 14.7 g
QS-H-3/8-1/4-U-M Push-in fitting 533222 Specifications
  • Size Standard
  • Nominal size 4.2 mm
  • Type of seal on screw-in stud coating
  • Working pressure -0.95 – 10 bar
  • Ambient temperature 0 – 60 °C
  • Max. tightening torque 22 Nm
  • Product weight 26.5 g
QS-H-1/4-1/4-U-M Push-in fitting 533219    
DMSP Blanking Plug 3568    
ESK-1/4-1/4 Double Nipple 151521    
PUN-1/4X3/64-U-BL Polyurethane Tubing 546584  
  • 150 PSI (10 bar)
SIM-M12-8GD-5-PU Plug Socket with Cable 525618  
  • VPPM 5m Control Cable
  • $30.11 each

8020

Component Name Part # Pubs (Page #)  
8020 Interactive Catalog		 Notes
3060 T-Slotted Aluminum  1515 46

Specifications
  • Used for arm’s base
  • 15 Series Profile Material (Based on fractional distance between grooves.)
  • 3.0″ x 6.0″
3320 5/16-18 x 11/16 Flanged BHSCS & Economy T-Nut 3320   120  
15-Series Base Plate  2130  303  
Economy Anti-Vibration Mounts  2207  308  
8 Hole Inside Corner Bracket 4304   127  
6 Hole Inside Corner Bracket  4311  125  
6 Hole Inside Corner Bracket  4301  123  
4 Hole Tee Joining Plate  4341  143  
1515 End Cap with Fasteners  2030  266  
1530 End Cap with Fasteners  2045  266  
Pivot Joint  14017  998  
5/16-18 x 1″ Socket Head Cap Screws  3122  101  
2 Hole Inside Corner Bracket  4302  121  
Top Mount Bearing Pads  6898 337   
#10 x 5/6 Pad Screw  40-3628  976  
1/4″ Black Haircell ABS Panel  2313  251  
3″ Single Panel Retainer  7150  397  
1.5″ Single Panel Retainer  2434  242  
Velcro Fastener  3273  281  

National Instruments

Thomas

Ultra Air-Pac T-35HD Electric Air Compressor

  • 3 gallon air storage
  • Pressure switch maintains 100 PSIG (689.5 KPa) -140 PSIG (861.9 KPa)
  • Air Displacement: 4.5 CFM (127 LPM) @ 0 PSI
  • Air Delivery: 2.9 CFM @ 50 PSI (83.5 LPM @ 345 KPa)
  • 2.55 CFM @ 100 PSI (72.22 LPM @ 689.5 KPa)
  • 115V, 60Hz, 10A (@ working pressure), 15A fuse
  • Weight: 48 lbs
  • Requires no lubrication (do not apply oil or damage may result)
  • Motor is equipped with thermal overload protector. If protector trips, the user should manually turn the pump motor off and let the system cool for 5 minutes.

Relevant Papers

]]> http://www.obeidlab.com/pneumatic-arm/feed 0 Multiply Tuned Neurons http://www.obeidlab.com/multiply-tuned-neurons-development http://www.obeidlab.com/multiply-tuned-neurons-development#comments Tue, 14 Aug 2007 18:32:03 +0000 O'Doherty http://obeidlab.com/multiply-tuned-neurons Introduction

The goal of this project is to determine what tuning curves look like for neurons that are simultaneously tuned to multiple stimuli. We are using pre-existing data in which monkeys had to focus on two different visual cues at the same time. The neurons recorded in this study are in the pre-frontal cortex and were taken from two different monkeys. The data was acquired by Misha Lebedev at NIH; the paper summarizing that work can be read here. The notes Iyad took during his summer 2007 visit to see Joey and Misha can be read here.

Details of Misha’s Experiment

A single trial of Misha’s experiment proceeds as follows:

  1. Subject presses a button to start the trial
  2. Fixation point appears at center of the screen
  3. Subject fixates for 1-1.5s
  4. Target appears in one of four locations (0°, 90°, 180°, 270°)
  5. After another 1-1.5s, the target revolves around to one of the four locations (could be the same one it started at)
  6. After the target stops, there is a 1-2.5s delay
  7. The target gets either brighter or dimmer for 150ms and then extinguishes. This serves as both the trigger for the subject to saccade, as well as the instruction for which target to saccade to.
  8. Subject saccades to appropriate target; correct responses are rewarded with juice.

Data File Structure

The data come from two monkeys, each of which has had multiple neurons tested. Each neuron has been tested with multiple trials. The list of neurons are stored in the files PF_all_zach.lst and PF_all_hamp_noPMd.lst. A sample line from Zach’s file reads:

za2_1 8 a

The “za2_1″ identifies the trial day number and the electrode number; these are irrelevant details for the purpose of this experiment. The “8″ refers to the unit number and “a” refers to the subunit. There are many other units and subunits for each electrode, but only those listed in these files have been identified by Misha et al as being valid for study. The data for the corresponding electrode would be stored in za2_1_code.mat. That file would contain the results of all the different trials for that electrode, only some of which involve unit 8 and subunit a.

The data file za2_1_code.mat contains data for each of a large number of trials of the experiment. For example, the variable “spike_time” is a cell array of length 1043, meaning that each entry in the cell array is an array with the firing times of all the neurons during one trial. We can determine which channel yielded each spike time by cross-referencing with “spike_channel”. The active channels and subchannels of the “nth” trial are saved in channels{n} and subchannels{n}.

All times must be centered relative to the target blink time by executing the following line: cntr = target_blink;

The code can then march through the trials and determine how many times the target subunit fired during a given time period under different conditions (such as specific combinations of attended and remembered locations).

Determining Qualifying Neurons

According to Misha’s paper, only certain neurons qualify for study – those which are concurrently tuned to both attended and remembered target locations. This is determined by computing two custom statistics: IRem and IAtt; if both of these are significantly greater than one, the neuron is said to be concurrently tuned to both stimuli. The formulas for these statistics are given in equations (1) and (2) of Misha’s paper (page 1932). For example, if IRem equals unity for a given neuron, then there is little difference between the variance of firing rates within rows (denominator) versus between rows (numerators); such a neuron is not tuned to the remembered target location. If the within-row variance is much smaller than the between-row variance, IRem will be greater than one and the neuron will be said to be tuned. In order to test statistical significance of IRem for a particular neuron, we repeat the following procedure 1000 times: Reassign the firing rates randomly between trials of with the same Attended location and recompute IRem; in theory, this should result in an IRem of unity. If the original value of IRem is greater than 99% of the randomized IRem values, we can be fairly sure that it is significantly greater than one, and that neuron is said to be tuned for remembered target location. The same procedure can be applied for measuring and testing the significance of IAtt. If done correctly, it should be possible to re-create Figure 3D of Misha’s paper (page 1923); the randomization aspect may limit how precisely that figure can be reproduced.

The following table summarizes Iyad’s attempt to recreate Misha’s statistics. Misha’s numbers are taken from page 1923 and Table 2. The differences between the two columns are likely due to the fact that the determination of statistical significance requires randomization, which will have been different between Misha’s and Iyad’s programs. However it is a little surprising that there are as many differences as are observed – it would seem that 1000 repetitions would be enough to virtually eliminate the effects of randomization.

  Misha Iyad
IAtt 1.84 ± 0.08 1.84 ± 0.08
IRem 1.21 ± 0.02 1.22 ± 0.02
     
n Att Tuned
 186 179
n Rem Tuned
 47 53
n Both Tuned
 70 77
Total 303 309

Comparing the Fits

The 77 dual-tuned neurons were fitted to three different firing rate models:

1. The SUM of two independent preferred-direction cosine tuned curves

2. The PRODUCT of two independent preferred-direction cosine tuned curves
3. A linear fit of the difference between the two angles
The following procedure was repeated 100 times for each neuron: Use 75% of the trials to fit the model parameters. Then test the fit by using the remaining 25% of the trials to compare predicted versus actual firing rate. The results show very clearly that, for any given neuron, all three models fit the data equally well:
The code producing these traces was backed up on 12/07/2007.
Upon further review, it appears that these results are predicted by theory. This was determined by the following experiment. I created a neuron whose firing rate was the sum of the firing rates predicted cosine tuning curves for two independent inputs plus some zero-mean Gaussian random noise. A series of randomized trials were generated for this neuron. The resulting firing rates were reverse fitted to a different model, one where the firing rate is the product of the cosine tuning curves. Finally, the percent error between the “actual” firing rate (from the additive model) and the “predicted” firing rate from the fitted multiplicative model was computed. It was found that the mean percent error was roughly zero, with neither mean nor standard deviation changing dramatically as the amount of Gaussian noise summed into the additive neuron model was increased. This verifies that both the additive and multiplicative neuron models are flexible enough to fit the data reasonably well. The code generating this trial is found here.
A more interesting question is whether the competing models predict different preferred directions for attention and memory. This was tested as follows: for each of the 77 dual-tuned neurons, the two preferred directions were computed twice, once according to the “summation” model and once according to the “multiplication” model. For each neuron, the preferred directions were recalculated 100 different times and these were averaged respectively; each iteration used different trials for training versus testing the model coefficients. The results are shown below. Each plot has “preferred direction according to summation model” on the x-axis and “preferred direction according to multiplication model” on the y-axis. The left plot is predicted preferred directions for “attention” whereas the right plot is for “memory”. As is evident, they are linearly related with slope of 0.6 in both cases. The code used to produce this plot is backed up on 12/10/2007.

Background on Tuning Functions

Cosine tuning describes how the firing rate of a given neuron varies as a function of the direction of a particular motor task. It was first described by Georgopoulos in 1982 for 2-D movement and again in 1986 for 3-D movement.

Single Variable Tuning

The basic equation is given as follows:

FR = a + b*cos(\theta - \theta_p)

where a represents the mean firing rate and b represents the maximum deviation from a. The maximum firing rate is therefore a + b and the minimum firing rate is a-b. The cosine tuning equation can equivalently be represented as a function of the sine and cosine of the direction of movement:

FR = a + \beta_1sin(\theta) + \beta_2cos(\theta)

where

b = \sqrt{\beta_1^2 + \beta_2^2}

and

\theta_p=tan^{-1}\left(\frac{\beta_1}{\beta_2}\right)

The concept of a cosine tuned neuron with a preferred direction is therefore equivalent to a linear regression of firing rate against the sine and cosine of the movement direction.

Multiply Tuned Curves

Based on this original concept, there are a number of models to consider for multiply tuned neurons. In the following lines, I will assume that there are two tuning variables, \theta_{att} and \theta_{mem}:

Additive

FR = \left(a_{att}+\beta_{1,att}sin(\theta_{att})+\beta_{2,att}cos(\theta_{att})\right)+\left(a_{mem}+\beta_{1,mem}sin(\theta_{mem})+\beta_{2,mem}cos(\theta_{mem})\right)

which is equivalent to:

FR=c_1 + c_2sin(\theta_{att}) + c_3cos(\theta_{att}) + c_4sin(\theta_{mem}) + c_5cos(\theta_{mem})

The Matlab code developed for this project computes the coefficients c_1 - c_5 using a least squares approximation. From those coefficients, we can work backwards to compute the estimated preferred direction for the two tuning variables as follows:

\hat{\theta}_{att,preferred}=tan^{-1}\left(\frac{c2}{c3}\right)

\hat{\theta}_{mem,preferred}=tan^{-1}\left(\frac{c5}{c6}\right)

Multiplicative

FR = \left(a_{att}+\beta_{1,att}sin(\theta_{att})+\beta_{2,att}cos(\theta_{att})\right)\times\left(a_{mem}+\beta_{1,mem}sin(\theta_{mem})+\beta_{2,mem}cos(\theta_{mem})\right)

which is equivalent to:

FR=c_1 + c_2sin(\theta_{att}) + c_3cos(\theta_{att}) + c_4sin(\theta_{mem}) + c_5cos(\theta{mem})

+ c_6sin(\theta_{att})sin(\theta_{mem}) + c_7sin(\theta{att})cos(\theta_{mem}) + c_8cos(\theta_{att})sin(\theta_{mem}) + c_9cos(\theta_{att})cos(\theta_{mem})

The Matlab code developed for this project computes the coefficients c_1 - c_9 using a least squares approximation. Working backwards from those coefficients to compute the estimated preferred directions requires solving a system of non-linear equations. There is no closed-form expression for this. Rather, a Levenberg-Marquardt approximation can be executed in Matlab to estimate the beta values. From there, we can work backwards to compute the estimated preferred directions:

\hat{\theta}_{att,preferred}=tan^{-1}\left(\frac{\hat{\beta}_{1,att}}{\hat{\beta}_{2,att}}\right)

\hat{\theta}_{mem,preferred}=tan^{-1}\left(\frac{\hat{\beta}_{1,mem}}{\hat{\beta}_{1,mem}}\right)

I attempted to evaluate how reliable this inverse method is for estimating the preferred directions from the estimated beta values. I created a neuron that was tuned according to the product of two cosine tuned parameters. I added noise with standard deviation σn to both of the cosine tunes before multiplying them to get the estimated final firing rate. This represented one trial. The beta values were computed from a set of 100 trials and the preferred directions were computed as stated above. This procedure was again repeated 100 times. The histograms of the preferred direction prediction errors are shown below: left column is attenuation, right column is memory, top row is σn = 0.1, bottom row is σn = 0.3. As is evident from the plots, the predicted preferred direction is occasionally 180 degrees off, which biases the error measures. The code used to generate the figure below can be found here.

 

 

Ideal Tuning Curve Graphs

The above ideal tuning curves have been graphed and are shown here for reference:

Sample Curves

Backup Code

Multiply Tuned Neurons Backup Code – 12/10/2007

Multiply Tuned Neurons Backup Code – 12/07/2007

Multiply Tuned Neurons Backup Code – 10/26/2007

]]> http://www.obeidlab.com/multiply-tuned-neurons-development/feed 0