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

• http://www.muonics.net/school/spring05/videoInput/
• a free windows video capture library

AutoCAD

• 8020 Parts

NI

• daqmx C functions
• NI DAQ A/D Interface Card Functions – Appendix D

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

8020

• http://www.8020.net/
• Distributer: TECO, tecoinc.com, 800.521.3285
• Salesman: Jim Gordon, jgordon@tecoinc.com

National Instruments

• See here.

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

• (Boblan et al 2007) A Human-Like Robot Torso ZAR5 with Fluidic Muscles: Toward a Common Platform for Embodied AI
• (Boblan et al 2006) A human-like robot torso with fluidic muscles Biologically inspired engineering
• (Boblan et al 2004) A Human-Like Robot Hand and Arm with Fluidic Muscles: Biologically Inspired Construction and Functionality

My goal for Spring 2008 is to construct an executable neural model that will translate a simulated-biological system’s angular requirements (preferred directions) into plausible neural signals as recorded by invasive electrodes. The simulator will accomplish its goal by uniting pre-existing fractal and neural simulation tools into a cohesive package to construct an executable 2-dimensional neural columnar model conforming to the topography of the human motor cortex as report by Mountcastle (1997) and Georgeopolous (2007). The model will be limited to approximately 100 roughly-hexagonal columns comprised of 64- 80 mini-columns each.

I will use this project as the subject matter for completing a formal thesis proposal.

Important Dates

Future Goals

Using fractal methods:

1. Modify the fractal map for multiple pd layers.
2. Adapt mask to mimic interneurons.

Within NEURON:

1. Replicate a 3-d columnar structure. Neurons in layers II, III & IV demonstrate similar preferred directions (pd) in the vertical dimension and are organized into slightly overlapping mini-columns and columns. Mini-columns form from the dendrites of 3 to 20 large layer V & VI pyramidal cells that, while ascending to layer I, collect layer II & III dendrites along their path to total ~80 to 100 neurons (striate cortex is ~2.5x larger). Columns form from 64 (30µm-diameter) to 80 (50 to 60µm-diameter) mini-columns normally bound together by cell-autonomous and histogenetic influences. The mini-columns are spaced at 20 to 80µm center-to-center creating columns² ranging in diameter from 240 to 600µm. As each column represents a full 360° pd, mini-columns represent roughly 5.62° pd and coalesce as doublets and triplets of similar pd’d mini-columns.

Databases

1. SenseLab

   The SenseLab Project (hosted by Yale University) is a long term effort to build integrated, multidisciplinary models of neurons and neural systems, using the olfactory pathway as a model. This is one of a number of projects funded as part of the Human Brain Project whose aim is to develop neuroinformatics tools in support of neuroscience research. The project involves novel informatics approaches to constructing databases and database tools for collecting and analyzing neuroscience information, and providing for efficient interoperability with other neuroscience databases.

2. ModelDB

   ModelDB (a component of SenseLab) provides an accessible location for storing and efficiently retrieving computational neuroscience models. ModelDB is tightly coupled with NeuronDB. Models can be coded in any language for any environment. Model code can be viewed before downloading and browsers can be set to auto-launch the models. Click here for help on how to download and/or run models from Senselab’s Model Database.

Neural Simulators

The following guide provides a synopsis provides a summary of some known open-source neural simulators.

1. DSTOOL

   by John Guckenheimer, Cornell Univ., dynamical systems on Unix machines

2. GENESIS

   by Jim Bower, Cal. Tech., general purpose simulator for neural systems on Unix machines

3. NBC

   by Jean-Francois Vibert, Fac. de Med. St-Antoine, Paris, Network simulation and analysis on Unix and VMS machines

4. NEMOSYS

   by John Tromp, Univ. Cal., Berkeley, complex single neurons on Unix machines

5. NEUROGRAPH

   by Peter Wilke, Univ. Erlangen, Germany, Simulation of artificial neural networks on Unix, DOS, VMS machines

6. NEURON

   by Michael Hines, Duke Univ., Simulations of biologically realistic single neurons and small networks on PCs and Unix machine

   NEURON is a simulation environment for developing and exercising models of neurons and networks of neurons. It is particularly well-suited to problems where cable properties of cells play an important role, possibly including extracellular potential close to the membrane), and where cell membrane properties are complex, involving many ion-specific channels, ion accumulation, and second messengers.

   For more information NIL’s use of NEURON see our NEURON Simulator Programming Guide.

7. NEURONC

   by Rob Smith, Univ. Penn., compartmental simulations of large neural circuits on Unix machines

8. NODUS

   by Eric De Schutter, Univ. Antwerp, Belgium, simulation of small networks of neurons on Macintosh machines

9. NSL

   by Alfredo Weitzenfeld, Univ. Sou. Cal., simulation of large networks on Unix machines

10. SNNAP

    by John Byrne, Univ. Texas, Houston, Simulator for neural networks on Unix machines

11. SWIM

    by Orjan Ekeberg, Royal Inst. Tech., Stockholm, simulation of network of few compartment model neurons on Unix machines

Tool Sets

Blue Brain Project

“In July 2005, EPFL and IBM announced an exciting new research initiative – a project to create a biologically accurate, functional model of the brain using IBM’s Blue Gene supercomputer. Analogous in scope to the Genome Project, the Blue Brain will provide a huge leap in our understanding of brain function and dysfunction and help us explore solutions to intractable problems in mental health and neurological disease.”

• Markram, H. (2006). The blue brain project.. Nat Rev Neurosci, 7, 153-60.
• Migliore, M., Cannia, C., Lytton, W. W., Markram, H. & Hines, M.L. (2006). Parallel network simulations with NEURON.. J Comput Neurosci, 21, 119-29.

Neuronal Time Series Analysis (NTSA) Workbench

A Database System for Neuronal Pattern Analysis

“Biologically-detailed neural simulations of the type supported by software packages such as GENESIS (developed in Jim Bower’s laboratory at Cal Tech) and NEURON (developed by Michael Hines and John Moore at Duke) are typically used to generate time-series data of the same general form as data collected in neurophysiological experiments. The volume of time-series data produced in a typical simulation study is often comparable to, or in some cases much greater than, the amount of data yielded in physiological experiments. The uniform interface that NTSA Workbench presents to both experimental and simulated data greatly facilitates comparison of modeling results with experimental data.” The status of this project is unknown as all links to the actual project are dead.

Getting Started

NEURON may be downloaded from http://www.neuron.yale.edu/neuron/install/install.html for Linux, MSWin (95 and up), and Mac OS X 10.4. NIL is running NEURON within the Fedora Linux environment on the basis that of the available OSs, Linux requires the least computational resources and may be implemented cheaply and easily across multiple hardware systems.

NEURON is installed in Linux with the command

rpm nrn-[...].i686.rpm

or installed versions may be upgraded with

rpm -Fvh nrn-[...].i686.rpm

For those operating on a non-Linux system I recommend installing the virtual os program VirtualBox. The program allows any OS (Mac, Win, or Linux) to be run as a virtual (guest) system on any host. Virtual systems have full network and host file system access. NIL is running with both pure and virtual Linux systems.

In recent years NEURON has been modified to interact with the object-oriented language Python. The language comes installed with most flavors of Linux (including Fedora) or may be obtained through their download page. Two additional libraries, ‘numby’ and ’scipy’, are also required and may be installed through the Linux package manager.

Commands

• nrnivmodl – compiles a downloaded model. Run the command within the model’s folder.
• nrngui – executes the model. (i.e. nrngui mosinit.hoc)

Resources

• NEURON home page
• What is NEURON (by the software authors)
• NEURON Quick Reference
• NEURON Instructions and Parameters
• NEURON Tutorial
• The NEURON Forum
  
• Neural Simulator Resources (internal NIL link). Before continuing with the NEURON Simulator Programming Guide, I strongly recommend that the reader investigates the Database resources. The databases provide pre-built models for immediate gratification.
• Performance Data – Parallel network simulations with NEURON (pdf)
  

Other Projects Using NEURON

• Blue Brain Project – Massive use of mNEURON (multiprocessor version).

‘.hoc’ Template

The following document outlines the sections necessary to program a structure within the neural simulator NEURON. Where possible, the first instances of keywords have been linked to reference documents for ellaboration.

//**********************************************************************
//**********************************************************************
// TEMPLATE: Basic Neuron
// AUTHOR(s): Richard Friendlich
//   This file is a composite of various tutorials and books.
//   Primary contributors include Dr. N.T. Carnevale, Dr. M. L. Hines, Kevin E. Martin (martin@cs.unc.edu)
//   Additional authors are sited as necessary.
// PURPOSE:
// PARAMETERS: None.
// VERSION HISTORY: 20 Nov 2007
// NOTES:
//   NEURON files are saved with the .hoc extension and may be executed via the nrngui command
//**********************************************************************
//**********************************************************************

load file("nrngui.hoc")	// Loads GUI and standard runtime library

//**********************************************************************
// Procedure: celldef
// Author(s): Various
// PURPOSE: Implements modular approach to cell building
// PARAMETERS: None.
// VERSION HISTORY: 20 Nov 2007
// NOTES: 'The NEURON Book' recommends dividing the cell description into: Topology, Geometry & Biophysical Properties
//**********************************************************************
proc celldef() {
  topol()
  subsets()
  geom()
  biophys()
  geom_nseg()
}

create soma, apical, basilar, axon 

/*'create' is an nrniv command which creates a list of section names. Existing sections with the same names
are destroyed and recreated. The create statement may occur within procedures, but the names must have
been previously declared with a create statement at the command level.*/

//**********************************************************************
// Procedure: topol
// Author(s): Various
// PURPOSE: Connects basic cell structure. This represents the cells framework much like a skeleton.
// PARAMETERS: None.
// VERSION HISTORY: 20 Nov 2007
//**********************************************************************
proc topol() { local i
  connect apical(0), soma(1)
  connect basilar(0), soma(0)
  connect axon(0), soma(0)
  basic_shape()
}

//**********************************************************************
// Procedure: basic_shape
// Author(s): Various
// PURPOSE: Assigns 3d coordinates to structures defined in topol
// PARAMETERS: None.
// VERSION HISTORY: 20 Nov 2007
// NOTES: The topology works fine with out pt3dadd. Upon creating a 'shape plot' NEURON creates and arbitrary 3d structure
//   if one has not been specifically designated.
//**********************************************************************
proc basic_shape() {
  soma {pt3dclear() pt3dadd(0, 0, 0, 1) pt3dadd(15, 0, 0, 1)} // Destroys the 3d location info in the currently accessed section
  apical {pt3dclear() pt3dadd(15, 0, 0, 1) pt3dadd(120, 0, 0, 1)} // and adds the 3d location and diameter point at the end of the
  basilar {pt3dclear() pt3dadd(0, 0, 0, 1) pt3dadd(-59, 45, 0, 1)} // current pt3d list.
  axon {pt3dclear() pt3dadd(0, 0, 0, 1) pt3dadd(-104, 0, 0, 1)}
}

objref all, has_HH, no_HH
/*Object variables are labels (pointers, references) to the actual objects. Thus o1 = o2 merely states that o1 and o2 are labels
for the same object. Objects are created with the new statement. When there are no labels for an object the object is deleted.
The keywords objectvar and objref are synonyms.*/

//**********************************************************************
// Procedure: subsets
// Author(s): Various
// PURPOSE: Groups sections (made via 'create' command) into manageable units
// PARAMETERS: None.
// VERSION HISTORY: 20 Nov 2007
// NOTES: Subsets a useful shorthand for assigning morphologies to groups of sections at a time.
//**********************************************************************
proc subsets() { local i
  objref all, has_HH, no_HH
  all = new SectionList()
    soma all.append()
    apical all.append()
    basilar all.append()
    axon all.append()

  has_HH = new SectionList()
    soma has_HH.append()
    axon has_HH.append()

  no_HH = new SectionList()
    apical no_HH.append()
    basilar no_HH.append()

}

//**********************************************************************
// Procedure: geom
// Author(s): Various
// PURPOSE: Specifies the geometric properties of the sections.
// PARAMETERS: None.
// VERSION HISTORY: 20 Nov 2007
// NOTES:
//**********************************************************************
proc geom() {
  forsec all {  }
  soma {  L = 30  diam = 30  }
  apical {  L = 600  diam = 1  }
  basilar {  L = 200  diam = 2  }
  axon {  L = 1000  diam = 1  }
}

//**********************************************************************
// Procedure: geom_nseg
// Author(s): Various
// PURPOSE: Establishes required number of segments per section.
// PARAMETERS: None.
// VERSION HISTORY: 20 Nov 2007
// NOTES:
//**********************************************************************
proc geom_nseg() {
  forsec all { nseg = int((L/(0.1*lambda_f(100))+.9)/2)*2 + 1  }
}

//**********************************************************************
// Procedure: biophys
// Author(s): Various
// PURPOSE: Inserts biophysical cell properties.
// PARAMETERS: None.
// VERSION HISTORY: 20 Nov 2007
// NOTES:
//**********************************************************************
proc biophys() {
  forsec all {
    Ra = 100
    cm = 1
  }
  forsec has_HH {
    insert hh // insert is used to place distributed mechanisms (membrane properties)
      gnabar_hh = 0.12
      gkbar_hh = 0.036
      gl_hh = 0.0003
      el_hh = -54.3
  }
  forsec no_HH {
    insert pas
      g_pas = 0.0002
      e_pas = -65
  }
}
access soma

celldef()

//**********************************************************************
//**********************************************************************
/* Instrumentation
//**********************************************************************
//**********************************************************************

// Synaptic input
objref syn
soma syn = new AlphaSynapse (0.5)
syn.onset = 0.5
syn.tau = 0.1
syn.gmax = 0.5
syn.e = 0

// graphical display
objref g
g= new Graph()
g.size (0,5,-80,40)
g.addvar ("soma.v(0.5)",1,1,0.6,0.9,2)

//**********************************************************************
//**********************************************************************
/* Simulation Control
//**********************************************************************
//**********************************************************************

dt = 0.025
tstop = 5
v_init = -65

proc initialize(){
  finitialize(v_init)
  fcurrent()
}
proc integrate(){
  g.begin
  while(t
    fadvance()
    g.plot(t)
  }
  g.flush()
}
proc go(){
  initialize()
  integrate()
}
tstop()

Education

• B.S., Computer Science, Virginia Tech, 1994

Current Research Topics

• Motor Cortex Simulator
• Fall 2007 Lab Goals

Research Experience

• June 2007 – Present, Temple Neural Instrumentation Lab

Professional Experience

• February 2005 – May 2006
  Systems Engineer, Innovative Multimedia, Gaithersburg, MD
• July 2004 – February 2005
  Independent Consultant, NetSetters, LLC, Philadelphia, PA
• August 2003 – July 2004
  Flight Commander/Operations Officer, US Air Force, McGuire AFB, NJ
• August 2001 – August 2003
  Chief of Squadron Safety/KC-135 Instructor Pilot, US Air Force, Kadena AB, Okinawa, Japan
• January 1998 – August 2001
  Workgroup Manager/Advanced Programs Commander/Instructor, US Air Force, USAF Academy, CO
• July 1995 – January 1998
  Workgroup Manager/Pilot, US Air Force, Grand Forks AFB, ND

Societies

• IEEE
• BMES

Honors and Awards

none

Publications

none

The purpose of this memo is to summarize this week’s developments in both N.I.L. research and lab infrastructure.

N.I.L. Research (Motor Cortex)

Description

I am developing the motor cortex vi (motorCortex.vi) component of the NIL Workbench. The purpose of this routine is to take as input the system’s angular requirements (over time) and translate them into realistic neural signals.

Weekly Accomplishments

My studies this week concentrated on constructing the Artificial Cortical Space (ACS). The full scope of research including background material and citations may be found at http://www.obeidlab.com/motor-cortex-simulator.

Lab Infrastructure

Description

I am working to develop the ObeidLab intranet’s structure and documentation. The giga-net will be a self-sustaining entity of computers and users centered on the ‘South’ server with a secure line of communication to the outside world.
South will orchestrate communication between current computers and facilitate the easy insertion of future computers.
South will facilitate user communication and flexibility by offering remote profiles, public and private folders, secure date backup, print spooling, and logon scripting to automate common startup tasks (like drive mapping).
The configuration settings and history may be found at http://obeidlab.com/.

Weekly Accomplishments

Multiple wikis (as listed on http://www.wikimatrix.org/) and one blog () wordpress were evaluated to replace our current wiki (wikimedia).
Criteria:
• Operate on Apache webserver
• Version control History
• Ease of editing
Secondary Criteria:
• Math formatting
• Media inclusion
• Stable version
• Active development community

Of those tested and installed, only two programs were installable and exceeded the initial criteria… DokuWiki and WordPress. We will evaluate both as a group next week.

The MCS is unique in its ability to automatically generate models of three dimensional cortical cell populations with biologically plausible distributions of neurons, preferred directions, and synaptic connections. This is accomplished by entering a cortical region’s cellular and directional statistical data into fractal terrain mapping algorithms. The resulting distribution maps are layered to form a virtual scaffold in which cells are embedded along with their morphological definitions. By this method, a structure of hundreds to thousands of various classes of neurons may be generated pre-runtime from an anatomical definition.

This research integrates with two major, freely available resources: (1) a simulator, NEURON, that provides efficient system management and execution of the complex neural network, and (2) the online repositories ModelDB and NeuroMorpho that catalog previously published cellular models.

View the entire proposal in PDF format (MCS Thesis Proposal).

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