beyond brain

7/30/2019 beyond brain

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figure1.1

Tearchitectureofaneural

circuit.Reproductionofa

RamnyCajaloriginal

drawin

gshowinganeural

circuitform

edbymany

neurons.Onesingleneuron

anditscellu

larspecializa-

tionsarehighlighted.In

general,den

dritesserve

asthemain

neuronal

specializationreceivin

g

synapsesfromother

neurons.Axonterminals

establishtheneurons

synapseswithotherbrain

cells.(Cajalsdrawingfrom

HistologyoftheNervous

Systemwas

reproducedwith

permissiono

ftheCajal

Legacy,InstitutoCajal[CSIC],

Madrid,Spain.)


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figure 1.2 Use of graph theory to study the distribution of pathways linking

pairs of neurons. On top, a square matrix is used to represent the direct, monosyn-

aptic connectivity of a small brain circuit. In this matrix, 1 represents the existence

of a direct connection between a pair of brain structures, while 0 depicts its absence.

Next to the matrix, a graph is used to represent the circuit. Circles with numbers

represent the structures and directional arrows represent the direct connectivity

information contained in the square matrix. Te histogram below depicts the total

number of pathways linking two structures (carotid baroreceptor and cerebellum)

that did not share a direct, monosynaptic connection. Te X axis represents thenumber of synapses of the pathways and the Yaxis depicts the number of pathwaysfound. Notice that millions of pathways were found for this particular example.

(Courtesy of Dr. Miguel Nicolelis; redrawn by Dr. Nathan Fitzsimmons, Duke University.)


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figure 2.1 Tomas Youngs trichromatic theory of color. A portrait of Tomas

Young and a graphic representation of his theory on the right. Notice that any color

stimulus (P, Q, R, or S in theXaxis of the plot on the bottom le) can be representedby the graded response of three distinct color receptors, which responds maxi-mally to red, green, and blue respectively, but can also respond submaximally to

dierent colors. (Youngs portrait was reproduced with permission of the NationalPortrait Gallery, London. Te gure was originally published in M.A.L. Nicolelis,Brain-Machine Interfaces to Restore Motor Function and Probe Neural Circuits.Nature Reviews Neuroscience 4 [2003]: 41722.)


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figure 2.2 Putting the neurons supremacy in ink. Don Santiago Ramn y Cajal

at his favorite place, in front of a microscope, and a few of his masterpiece drawings

of dierent portions of the central nervous system. (Cajals three drawings from His-

tology of the Nervous System and the photo were reproduced with permission of the

Cajal Legacy, Instituto Cajal [CSIC], Madrid, Spain.)


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figure 3.1 Korbinian Brodmanns cortical cytoarchitectonic map and the six lay-

ers of the cortex. A side view of the human brain with the original numeral desig-

nations of cortical areas created by Brodmann is on the right. A comparison of the

six layers of a section through the primary motor (M1) and visual (V1) cortices is

shown on the le. Cytoarchitectonically speaking, the M1 is characterized by thepresence of large pyramidal-looking neurons (Betz cells) in layer V, while V1 exhib-

its a very dense cluster of neurons in the bottom part of layer IV and upper part of

layer VI. (Cajals two drawings from Histology of the Nervous System were reproduced

with permission of the Cajal Legacy, Instituto Cajal [CSIC], Madrid, Spain. Brodmanns

Areas, originally published in 1910, are in the public domain.)


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figure 3.2 Te homunculus meets the rattunculus. Te drawing depicts an

impossible meeting between the reconstruction of the cortical homunculus, the

distorted representation of a humans body in the primary somatosensory cortex,

based on Wilder Penelds studies, and a cortical rattunculus, the equivalent dis-

torted representations of the rats body, in the rodent primary somatosensory

cortex. Notice the overrepresentation of the lips and hands in the homunculus and

the facial whiskers, snout, and forepaws in the rattunculus. Te cheese is Swiss.

(Illustrated by Dr. Nathan Fitzsimmons, Duke University.)


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figure 3.3 Revolutionary plasticity experiment in owl monkeys by Jon Kaas and

Michael Merzenich. On the le, Jon Kaas appears with a close collaborator. On the

right, the top shelf describes how, aer a traumatic amputation of the third nger,

the territory representing this nger in the primary somatosensory cortex of an

owl monkey does not remain silent. Instead, the region that was occupied by nger 3

is now invaded by enlarged representations of ngers 2 and 4. Te lower shelf

shows that enlargement of the representations of ngers 14 can be obtained byrepetitive, selective stimulations of these digits in detriment of nger 5. o see the

eect, just compare the map on the bottom middle (before selective stimulation)

with the cortical map on the bottom right (aer stimulation). (Adapted from, M. M.

Merzenich, J. H. Kaas, J. Wall, R. I. Nelson, M. Sur, and D. Felleman, opographic Reorga-

nization of Somatosensory Cortical Areas 3B and 1 in Adult Monkeys Following Restricted

Deaerentation,Neuroscience 8, no. 1 [1983]: 3355, with permission from Elsevier.)


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figure 3.4 Te brain adopts a rubber hand. Te drawing depicts the experimen-

tal apparatus used to induce the rubber hand illusion. See the text for details.

(Illustrated by Dr. Nathan Fitzsimmons, Duke University.)


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figure 4.1 Sample of rst EEG recording ever obtained by Hans Berger. Te trace

represents a few seconds of the electrical activity of Bergers own son, recorded

using scalp sensors. (From Hans Berger, ber das Elektrenkephalogramm des Men-

schen, European Archives of Psychiatry and Clinical Neuroscience 87, no. 1 [1929]:

52770, with permission from Springer.)

figure 4.2 Te single microelectrode recording method. A single metal micro-

electrode, positioned in the extracellular space next to two neurons, is capable of

recording the extracellular action potentials of both cells. Inspection of the record-

ing trace in the oscilloscope reveals that the action potentials of the two neuronshave dierent shapes and magnitudes, allowing the distinction between the two

cells. (Illustrated by Dr. Nathan Fitzsimmons, Duke University.)


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figure 4.3 3-D image of a small patch of the sky produced by an array of radio

telescopes in Cambridge, England. Peaks identify galaxies that emit radio signals.Te height of each peak indicates the magnitude of the radio signal produced by

each galaxy. (Originally published in Joseph Silk, Te Big Bang [San Francisco, Calif.:

W. H. Freeman, 1980], with permission from Mullard Radio Astronomy Observatory

[MRAO] and Cavendish Laboratory, Cambridge.)


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figure 4.4 Engineering a better way to listen to the brain. On the le, a high-

power magnication of a multi-electrode array produced at the Duke University

Center for Neuroengineering (DUCN) by Gary Lehew and Jim Meloy. Notice that

multiple thin metal laments are clustered in a matrix format. Such laments are

exible and can be chronically implanted in the brain, remaining active for months

to years. On the right, a sample of dierent types of multi-electrode arrays created

at the DUCN in the past decade. (Courtesy of Dr. Miguel Nicolelis.)


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figure 5.1 An experimental setup designed for testing the ability of freely behav-

ing rats to use their facial whiskers to discriminate the diameter of an aperture in

the dark. Eshe is seen in the right frame performing the task with gusto! (Originally

published in D. J. Krupa, M. C. Wiest, M. Laubach, and M.A.L. Nicolelis, Layer Specic

Somatosensory Cortical Activation During Active actile Discrimination,Science 304

[2004]: 198992.)


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figure 5.2 emporal and spatial resolution of the single electrode versus multi-

electrode recording methods. Te top graph relates the spatial and temporal resolu-

tion of most techniques used to investigate brain function. Te lower graph compares

the single and multi-electrode recording method using the same parameters.

(Adapted from A. Grinvald and R. Hildesheim, VSDI: A New Era in Functional Imaging

of Cortical Dynamics.Nature Reviews Neuroscience 5 [2004]: 87485, with permission

from Macmillan Publishers Ltd.)


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figure5.3

Manysingleneurons.

Computers

creenimage

depictingth

eaction

potentialsp

roducedbya

sampleof394cortical

neuronsrec

orded

simultaneouslyinafreely

behavingprimate.Te

lemosthalfofthe

picture

showsfourdistinctfamilies

ofactionpo

tentials,

recordedsimultaneously

fromasingle

microelectrodeofanarray,

demonstratingtheelectrical

activityoffourdierent

corticalneu

ronssampled

simultaneously.Tesefour

distinctneu

ronsareshown

inisolation

inthelowerle

corner.(Cou

rtesyof

Dr.MiguelN

icolelis.)


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figure 5.4 Te whisker map of the rats face. Te le panel depicts the distribu-

tion of facial whiskers in the rat snout on four rows and many columns. Te right

half of the gure depicts a horizontal section through layer IV of the rat primary

somatosensory cortex (S1) that contains the entire rattunculus, including a whis-

ker representation (barrel cortex), nose (N), lower jaw (LJ), forepaw (FP), and hind-paw (HP), and is stained for the presence of a mitochondrial enzyme found in

neurons. Dark clusters represent clusters of neurons in layer IV. Notice that the

barrel cortex contains an isomorphic representation of whisker rows and columns.

Circles identify whisker C2 both in the rat face and the S1 cortex.

(Courtesy of Drs. John Chapin and Rick Lin.)


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figure 5.5 Te le half of the gure depicts the connectivity of some of the main

brain structures that dene the rat trigeminal somatosensory system. Excitatory (+)

and inhibitory (-) connections are shown. Te mechanical stimulation of facial whis-

kers triggers electrical responses of neurons in the trigeminal ganglion (Vg). Vg neu-

rons project to two distinct trigeminal nuclei in the brain stem: the spinal (SpV) and

principal (PrV) nuclei. Tese two send nerve pathways to three thalamic nuclei: the

ventroposterior medial nucleus (VPM), the posterior medial (POM) nucleus, and

the zona incerta (ZI). Te thalamic reticular nucleus (R) provides inhibition to the

VPM and POM. VPM, POM, and ZI provide thalamic nerve bers to the primary

somatosensory cortex. Of those, the ZI is the only one that sends inhibitory aerents

to the S1 cortex. In the right half of the gure, a stack of 3-D graphs illustrates the

simultaneously recorded tactile-evoked responses of populations of individual neu-

rons at dierent levels of the trigeminal system. (Adapted from M.A.L. Nicolelis, L. A.Baccala, R.C.S. Lin, and J. K. Chapin, Sensorimotor Encoding by Synchronous Neural

Ensemble Activity at Multiple Levels of the Somatosensory System.Science 268 [1995]:

135358; and from M.A.L. Nicolelis, A. A. Ghazanfar, B. Faggin, S. Votaw, and L.M.D.

Oliveira, Reconstructing the Engram: Simultaneous, Multisite, Many Single Neuron

Recordings.Neuron18 [1997]: 52937, with permission from Elsevier.)


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figure 5.6 VPM peri-stimulus time histograms. Four peri-stimulus time histo-

grams illustrate typical averaged electrical responses of single VPM neurons fol-

lowing the deection of facial whiskers. In each histogram, the Xaxis representsthe peri-stimulus time, 0 indicates the time of whisker deection. Te Y axisdepicts the number of spikes produced by the cell. (Courtesy of Dr. Miguel Vieira,

Duke University.)


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figure 5.7 Spatiotemporal RF and maps. (A) Spatiotemporal receptive

eld (RF) of a single VPM neuron. Each 3-D graph represents the spatial

domain (RF) of a single VPM neuron at a par ticular post-stimulus time

interval (510 ms, 2025 ms, 3550 ms). In each 3-D graph theXand Yaxesdepict the position of whiskers in the rows and columns found in the rats

face. Te Z axis represents the magnitude of the VPM neurons ringresponse when one particular whisker is mechanically deected. Notice

that at 510 ms, whisker E1 elicits the strongest ring response of the VPM

neuron, while stimulation of other whiskers produces somewhat smaller

responses. Yet, at 3550 ms aer the whisker stimulus, whisker E4 triggers

the strongest response of the same cell. Tus, the spatial center of the RF of

this VPM neuron shis as a function of post-stimulus time. (B) Spatiotem-

poral histogram depicting the tactile responses of a population of simulta-

neously recorded VPM neurons. Post-stimulus time is represented in theXaxis, with 0 marking the onset of whisker stimulation. Te Yaxis depicts anumber of individual VPM neurons recorded simultaneously. Te gray-

shaded Zaxis illustrates the magnitude of ring of these VPM neurons as afunction of time. (C) Spatiotemporal RF of a single neuron located in the

rat primary somatosensory cortex. In each of these 3-D graphs, theXaxisrepresents whisker columns, the Yaxis represents whisker rows, and the Zaxis (gray-scale) represents the magnitude of a single cortical neuron response.

Each 3-D graph depicts a particular post-stimulus time interval (812, 12

16, 1620, 2024, 2428 ms). Notice that, like in the VPM, the spatial

domain of the RF changes as a function of post-stimulus time. (Adapted from

M.A.L. Nicolelis, L. A. Baccal, R.C.S. Lin, and J. K. Chapin, Sensorimotor Encod-

ing by Synchronous Neural Ensemble Activity at Multiple Levels of the Somato-

sensory System. Science 268 [1995]: 135358; from M.A.L. Nicolelis, A. A.

Ghazanfar, B. Faggin, S. Votaw, and L.M.O. Oliveira, Reconstructing the Engram:

Simultaneous, Multisite, Many Single Neuron Recordings. Neuron 18 [1997]:

52937, with permission from Elsevier; from M.A.L. Nicolelis, and J. K. Chapin,

Te Spatiotemporal Structure of Somatosensory Responses of Many-Neuron

Ensembles in the Rat Ventral Posterior Medial Nucleus of the Talamus.Journalof Neuroscience 14 [1994]: 351132, with permission; and from A. A. Ghazanfar

and M.A.L. Nicolelis, Spatiotemporal Properties of Layer V Neurons in the Rat

Primary Somatosensory Cortex.Cerebral Cortex 9 [1999]: 34861, with permis-

sion from Oxford Journals.)


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figure 5.8 Examples of 714 Hz rhythmic mu oscillations observed in the rat tri-

geminal somatosensory system. In the le panel, dierent traces obtained simul-

taneously illustrate that mu oscillations start in the S1 cortex, spread to the VPM,

and later to the spinal complex of the trigeminal brain-stem complex (SPV) before

whisker twitching movements begin. In the right panel, a similar illustration of

the relationship between mu rhythm and whisker twitching is made between the

barrel cortex (rat S1 whisker area), the VPM, the basal ganglia (CP), and the hip-

pocampus (HI). (Originally published in M.A.L. Nicolelis, L. A. Baccala, R.C.S. Lin,

and J. K. Chapin, Sensorimotor Encoding by Synchronous Neural Ensemble Activity at

Multiple Level s of the Somatosensory System.Science 268 [1995]: 135358.)

s/sekipS

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Time (s) Time (s)64 66 68 0 5 10 15 20

0

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200

150

150

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Whisker

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Onset

Whisker

Twitching

OnsetSpV

VPM

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VPM

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HI

PC1

PC2


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figure 5.9 Peri-event histograms depict the ring response pattern of a single

cortical neuron in the rat primary somatosensory cortex under three dierentbehavioral conditions: active tactile discrimination in a freely behaving animal

(lemost panel), awake but immobilized (center panel), and immobilized and pas-

sive discrimination (rightmost panel). Notice how the pattern of this neurons

responses is totally dierent according to the animals behavioral context. For each

histogram, the X axis represents peri-event time, with 0 indicating the onset offacial whisker mechanical stimulation, and the Yaxis represents the electrical r-ing response of the neuron in spikes per second. (Originally published in D. J. Krupa,

M. C. Wiest, M. Laubach, and M.A.L. Nicolelis, Layer Specic Somatosensory Cortical

Activation During Active actile Discrimination.Science 304 [19891992, 2004].)


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figure 6.1 Te debut of the brain-machine interface. Systems engineering draw-

ing depicting the general organization of a brain-machine interface. Multi-

electrode arrays and microchips are used to record large-scale brain activity. Signal

processing techniques are then used to translate raw brain activity into digital

commands that can be employed to reproduce, in a robotic arm, the voluntary

motor intentions generated by the brain. Visual, tactile, and proprioceptive feed-

back signals from the robotic actuator are then sent back to the subjects brain.

(Originally published in M.A.L. Nicolelis and M. A. Lebedev, Principles of Neural

Ensemble Physiology Underlying the Operation of Brain-Machine Interfaces. Nature

Reviews Neuroscience 10 [2009]: 53040.)


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figure 6.2 General algorithm for translating raw neuronal electrical activity into

digital commands that can be employed to predict kinematic parameters and con-trol articial tools based on brain activity alone (see text for details). (Illustrated by

Dr. Nathan Fitzsimmons,Duke University.)


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figure 6.3 Where will Belles wrist go? Te graphs illustrate real-time predic-

tions, derived from Belles and Carmens brain activity, and how well they repro-

duce the actual position of these two owl monkeys hands. Te bottom panel shows

that the same kinematic predictions can be used to control simultaneously a robot

arm located next to the animals at Duke University or remotely, at MI. (Originally

published in J. Wessberg, C. R. Stambaugh, J. D. Kralik, P. D. Beck, J. K. Chapin, J. Kim,

S. J. Biggs, M. A. Srinivasan, and M.A.L. Nicolelis, Real-ime Prediction of Hand ra-

jectory by Ensembles of Cortical Neurons in Primates.Nature 408 [2000]: 36165.)


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figure 6.4 Aurora loses her joystick and frees her mind. On top, the experimen-

tal setup employed for Aurora to use her brain activity alone to control the move-

ments of a robotic arm. On the bottom le, a sample of the electrical activity of

96 cortical neurons from Auroras brain. On the Yaxis, each vertical bar representsan action potential produced by a single cortical neuron. Te Xaxis representstime (10 seconds). In the right panel, a graphic representation of the task Aurora

performed and examples of predictions of arm movements based on combined

brain activity. (Originally published in J. M. Carmena, M. A. Lebedev, R. E. Crist,

J. E. ODoherty, D. M. Santucci, D. R. Dimitrov, P. G. Patil, C. S. Henriquez, and M.A.L.Nicolelis, Learning to Control a Brain-Machine Interface for Reaching and Grasping by

Primates.Public Library of Science 1 [2003]: 193208.)


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figure 7.1

How many neurons does it take? wo neuron-dropping curves corre-late the number of neurons (X axis) with the accuracy of real-time predictionobtained for two distinct parameters (hand position and hand gripping force). Te

same sample of neurons from the primary motor cortex of a monkey was used to

construct these curves. (Originally published in J. M. Carmena, M. A. Lebedev, R. E.

Crist, J. E. ODoherty, D. M. Santucci, D. R. Dimitrov, P. G. Patil, C. S. Henriquez, and

M.A.L. Nicolelis, Learning to Control a Brain-Machine Interface for Reaching and

Grasping by Primates.Public Library of Science 1 [2003]: 193208.)


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figure 7.2 Gathering motor commands all over the brain. Neuron droppingcurves are used to compare the accuracy of prediction of populations of neurons

simultaneously recorded in the primary motor cortex (M1, traced line) and the

posterior parietal cortex (PP, dotted line) for two parameters (hand position and

gripping force). Notice that information about both parameters is available in both

cortical regions, but that M1 contains more information, for equal neuronal popu-

lations, for hand position. Yet, both M1 and PP yield similar levels of accurate pre-

dictions for gripping force, considering neuronal populations of the same size.

(Originally published in J. M. Carmena, M. A. Lebedev, R. E. Crist, J. E. ODoherty, D. M.

Santucci, D. R. Dimitrov, P. G. Patil, C. S. Henriquez, and M.A.L. Nicolelis. Learning toControl a Brain-Machine Interface for Reaching and Grasping by Primates. Public

Library of Science 1 [2003]: 193208.)

Position

Posterior Parietal Cortex

Posterior Parietal CortexMotor Cortex

Motor Cortex

Number of Neurons

noitciderPfo

R

10

0.2

0.4

0.6

0.8

20 40 60

2

Gripping Force

Number of Neurons

noitciderPfo

R

10

0.2

0.4

0.6

0.8

20 40 60

2

(M1)(PP)


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figure 7.3 Fine-tuning of cortical neurons that control the movements

of the body and machines.Gray-scaled polar plots of the ring rate of one

M1 neuron as a function of both arm velocity (key, top le) for dierent

lags with respect to instant of instantaneous arm velocity measurement

(0 ms). Velocity = 0 is at the center of each circle and maximum velocity (14

cms/second) is at the perimeter of the circle. Firing patterns were obtained

during dierent modes of operation (pole control and brain control with

and without hand movements), and during the use of dierent actuators

(hand or robot movements, see legends). Each of the circles also encodes

the neurons preferred direction (see scale). Gray shading indicates ring

rate (minimum is white, maximum is dark gray). (A) A neighboring neuron

that exhibits strong velocity and direction tuning only when the animal is

using its hand to play the video game, but not the robot. (B) A single M1

neuron that displays both velocity and direction tuning during both pole

and brain control and when the animal is using its hand or only the robot to

play the video game. (C) A neighboring M1 neuron that exhibits enhanced

velocity and direction (dashed arrows) tuning only when the monkey is

preparing to use its brain activity to move the robot arm, but not when it

moves its own biological arm. (Originally published in M. A. Lebedev, J. M.

Carmena, J. E. ODoherty, M. Zacksenhouse, C. S. Henriquez, J. Principe, and

M.A.L. Nicolelis. Cortical Ensemble Adaptation to Represent Velocity of an Arti-

cial Actuator Controlled by a Brain Machine Interface.Journal of Neurosci-

ence 25 [2005]: 468193, with permission.)


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figure 8.1 Experimental setup employed to create a brain-machine interface for

locomotion. (Originally published in N. Fitzsimmons, M. A. Lebedev, I. Peikon, and

M.A.L. Nicolelis, Extracting Kinematic Parameters for Monkey Bipedal Walking from

Cortical Neuronal Ensemble Activity.Frontiers in Integrative Neuroscience 3 [2009]:

119.)


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figure 8.2 Kinematic predictions, derived from combined raw brain activity, for

dierent types of bipedal locomotion behaviors: slow forward walking (top shelf),

fast forward walking (middle shelf), and variable speed (lower shelf). Black trace

depicts actual position of Idoyas leg whereas the gray traces yield real-time predic-

tions of this kinematic parameter obtained from her brain activity alone. (Originally

published in N. Fitzsimmons, M. A. Lebedev, I. Peikon, and M.A.L. Nicolelis, Extracting

Kinematic Parameters for Monkey Bipedal Walking from Cortical Neuronal Ensemble

Activity.Frontiers in Integrative Neuroscience 3 [2009]: 119.)


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figure 8.3 Idoya and CB-1s great leap across the globe. General schematic repre-

sentation of the experiment that allowed a monkey on the U.S. East Coast to use its

brain activity to control the leg movements of a humanoid robot (CB-1) in Kyoto,

Japan, while receiving visual feedback back in Durham, North Carolina, from the

robot locomotion. (Courtesy of Dr. Miguel Nicolelis.)


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figure 9.1 Alberto Santos-Dumont and his ying machine. A photograph of the

aviator (on the le) and the historic moment when he circumnavigated the Eiel

ower on October 19, 1901. (Reprinted with permission from the National Air and

Space Museum, Smithsonian Institution Archives, Washington, D.C.)


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figure 9.2 Summary of the experiments carried out by Dr. Atsushi Iriki and col-

leagues showing that the visual receptive eld of a parietal cortical neuron expands

when the animal employs a simple tool to perform a task. On the top shelf, a single

neuron with both a tactile and visual RF centered on the hand changed its visual

receptive eld to include the entire tool used by the animal to retrieve some food

reward. Notice that when the animal merely holds the tool, but does not utilize it to

perform a task, the visual RF remains centered on the animals hand alone. On the

bottom shelf, another neuron with the tactile RF centered on the animals shoulder

and a broad visual RF shows the same expansion of the visual RF when the animalutilizes a tool in a 3-D space. Notice that the visual expansion of the RF includes

the entire space in which the tool can reach. (Adapted from A. Maravita and A. Iriki,

ools for the Body (Schema).rends in Cognitive Sciences 8, no. 2 [2004]: 7986, with

permission from Elsevier.)

Before tool use

Before tool use

Distal-type neurons

Proximal-type neurons

After tool use

After tool use Passive holding

sensory

receptive

field

sensory

receptive

field

visual

receptive

field

visual

receptive

field

visual

receptive

field

visual

receptive

field

visual

receptive

field


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figure 9.3 Te le panel shows Pel in one of his characteristic shooting maneu-

vers in the 1960s. On the right, a drawing represents how the theory proposed in

this book predicts what Pels sensorimotor cortex would look like. According to this

view, the soccer ball would be incorporated into Pels foot representation in the

cortex. (From Swedish press image in the public domain.)


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figure

10.1

Teneu

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thebullring.Tis

sequenc

eofphoto-

graphsillustratesthe

classicalexperiment

carried

outbyDr.Jos

Delgadoinwhicha

chargin

gbullwasmade

tostopitschargeusing

deepele

ctricalbrain

stimulation.(Photo-

graphsu

sedwith

permissionofDr.Jos

Delgado.)


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figure 10.2 John Chapin and roborat. In

the le panel, Dr. John K. Chapin and his

creation. Below, roborat walks over a metal

mesh. (Courtesy of Dr. John Chapin.)


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figure 10.3 alking back to the brain. op shelf illustrates the experimental

paradigm utilized to deliver electrical messages to the brain of a monkey. Chron-

ically implanted microelectrode arrays are employed to deliver spatiotemporalelectrical patterns that represent dierent messages. Middle shelf illustrates the dif-

ferent types of patterns utilized to deliver messages to the primate brain: basic

amplitude discrimination, temporal discrimination, or spatiotemporal discrimi-

nation. Lower shelf illustrates learning curves for each of the three methods to

deliver messages. (Adapted from N. Fitzsimmons, W. Drake, . Hanson, M. Lebedev,

and M.A.L. Nicolelis, Primate Reaching Cued by Multichannel Spatiotemporal Cortical

Microstimulation.Journal of Neuroscience 27 [2007]: 5593602.)


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figure 10.4 Drawing depicting a future experiment in which an R6- rat will be

implanted with a magnetic eld sensor that delivers electrical microstimulation

to the animals primary somatosensory cortex proportional to magnetic elds ofdierent magnitudes. Each of these dierent magnetic elds identies dierent

objects, things like food, water, and the location of a toy rat. (Illustrated by Dr. Nathan

Fitzsimmons, Duke University.)


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figure 10.5 Te explorer rat and the decoder rat. Illustration of a true brain-to-

brain interface linking an explorer rat, which uses its facial whiskers to discrimi-

nate the diameter of a variable aperture, with a decoder rat whose main function is

to indicate the diameter of the aperture based on the pattern of brain activity trans-

mitted by the explorer rat, without ever touching the aperture with its whiskers.

Te brain-to-brain interface connects the brains of the two rats. (Illustrated by Dr.

Nathan Fitzsimmons, Duke University.)


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figure 10.6 A dierent version of a brain-to-brain interface involving an inter-

mediary layer of rats between the explorer and decoder rats. (Illustrated by Dr.



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figure 11.1 Spectrograms (top four 3D graphs) and corresponding raw local eld

potentials (5 graphs in the lower shelf) illustrate the frequency and general timepattern of dierent types of brain oscillations observed under dierent types of

behaviors: active exploration, quiet waking, whisker twitching, slow wave sleep,

and paradoxical REM sleep. In the spectrogram (top shelf) graphs, Xaxis is time(with the period of each behavioral state marked) and the Yaxis depicts the fre-quency of oscillations. Gray scale in the Zaxis depicts the magnitude or power of aparticular frequency of oscillation for each state. (Adapted from D. Gervasoni, Shih-

Chieh L., S. Ribeiro, E. S. Soares, J. Pantoja, and M.A.L. Nicolelis, Global Forebrain

Dynamics Predict Rat Behavioral States and Teir ransitions.Journal of Neuroscience

24 [2004]: 1113747.)


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figure11.2

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inthetopshelf.Itsbrain

relativeisdep

ictedinthe

state-spaceof

thelowerle

shelf.Onthelowerright

panel,thedi

erentbrain

statesthatcorrespondto

dierentlocationsinthe

state-spacear

emarkedby

ellipses.Atra

nsitionstate

betweenquietawakeand

slow-wavesleepisalso

depicted.(AdaptedfromD.

Gervasoni,Shih-ChiehL.,

S.Ribeiro,E.S.Soares,

J.Pantoja,and

M.A.L.

Nicolelis,GlobalForebrain

DynamicsPredictRat

BehavioralStatesandTeir

ransitions.Journalof

Neuroscience24[2004]:

1113747,with

permission.)


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figure 11.3 Te hypno-map. Tis 3-D graph illustrates the time spent by rats in

each of their main behavioral states. TeXand Yaxes of the graph depict the state-space shown in Figure 11.2, while the gray-scaled Zaxis represents the time spentby rats in each state. Notice that for most of their lives, our furry friends sleep,

without dreaming, in the slow wave sleep state. Quiet awake is only the secondmost common brain state in the life of rats. (Courtesy of Dr. Shih-Chieh Lin, National

Institute on Aging, NIH; and Dr. Damien Gervasoni, Claude Bernard University, Lyon,

France.)


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Slow Wave Sleep

Wire and Ball Model

Brain State-Space

Active Exploration

Quiet Waking

figure 11.4 Te wire and ball model (see text for explanation). (Illustrated by Dr.



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figure 11.5 Patterns of single neuronal ring during normal ring (top shelf)

and during the occurrence of a sensory stimulus (middle shelf). Te refractoryperiod, illustrated in the bottom shelf, denes the maximum ring rate that a neu-

ron can reach. Troughout these examples, the total ring rate produced by the

whole brain has to be maintained below a maximum cap. (Illustrated by Dr. Nathan

Fitzsimmons, Duke University.)


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figure 12.1 Cross-modal processing in the primary somatosensory and visual

cortices of rats. Peri-event histograms display the isomodal and cross-modal

sensory-evoked responses of individual S1 and V1 neurons. In the le panel, the

traditional visually evoked responses of V1 neurons and tactile-evoked responses

of S1 neurons are shown. In the right panel, samples of V1 neurons that respond

to a tactile stimulus and S1 neurons that respond to a visual stimulus are plotted.

Korbinian Brodmann would be shocked! (Courtesy of Dr. Sidarta Robeiro, Interna-

tional Institute of Neuroscience of Natal, Brazil; and Dr. Damien Gervasoni, Claude

Bernard University, Lyon, France.)


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figure 13.1 Cortical Neuroprosthesis for Restoring Motor Functions. A drawing

illustrates how a cortical neuroprosthetic device may one day help patients para-

lyzed due to a spinal cord lesion (see text for details).(Adapted from M.A.L. Nicolelis,

Brain-Machine Interfaces to Restore Motor Function and Probe Neural Circuits.Nature

Reviews Neuroscience 4 [2003]: 41722.)


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figure 13.2 Design of the whole-body exoskeleton to be utilized by the WalkAgain Project. (Courtesy of Dr. Gordon Cheng, echnical University of Munich.)


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figure 13.3 reating a Parkinsons disease-like syndrome in rats using electrical

stimulation of the spinal cord. On the top shelf, the stimulating electrodes and the

implantation approach to place them on the dorsal surface of the spinal cord.

Middle shelf shows an implanted rat that exhibits signs of Parkinsons disease.

On the bottom shelf two circles are used to identify bursts of the epileptic activity

observed in a spectrogram of the rats brain activity, that are correlated with the

akinesia produced by Parkinsons disease. At 0 time, the electrical stimulation of

the spinal cord started, using the implanted electrodes. Notice that the epileptic

activity disappears and, as a result, the rat was able to walk freely again (not

shown). Te spectrogramXaxis represents time (0 start of stimulation) and the Yaxis represents frequencies. Te gray scale of the Zaxis represents power or magni-tude of brain activity at a given frequency (see scale on the right). (Adapted from R.

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