Takashi Yoshioka

Director, BA/MS program in Neuroscience

Adjunct Assistant Professor

Department of Psychological and Brain Sciences

Krieger Mind/Brain Institute

338 Krieger Hall

Johns Hopkins University

3400 N. Charles St.

Baltimore, MD 21218

U.S.A.

Phones: 410-516-4955 (office)

410-516-6417 (lab)

410-516-8640 (institute)

FAX: 410-516-8648 (inst.)

e-mail: takashi@jhu.edu

web: http://www.mb.jhu.edu/

Research Interests Teaching Publications

The focus of my research is to understand how we perceive and process tactile information. To understand the information flow in the early stages of sensory processes, my studies have focused on the neural mechanisms of texture perception. The combined psychophysical and neurophysiological experiments in my previous work have shown that roughness perception depends on SA1 (slowly adapting, type 1) mechanoreceptors (computation details).

Roughness perception is just one of many forms of tactile perception. Just as in vision in which different aspects of visual stimuli such as color, form, and motion are processed, tactile perception is also multi-faceted sensation derived from texture features such as roughness, hardness and stickiness. In our recent human psychophysical study, we have identified that these three texture dimensions comprise perceptual space for tactile textures in both direct touch (through the finger) and indirect touch (through a tool; i.e., stylus-like probe); see Figure 1.

Figure 1. Perceptual space in direct touch (finger scanning) and indirect touch (probe scanning): Relative locations of 16 stimulus textures are shown in the MDS (multidimensional scaling) space model based on perceived dissimilarity ratings (dark blue dots with vertical gray lines) of texture pairs and their relation to perceived roughness (red line), hardness (green line), and stickiness (dark blue line) of individual textures. Left panel shows a plot in the finger scanning condition and right panel shows probe scanning condition. The radii of the spheres represent the overall mean of adjective ratings (i.e., rough, hard, and sticky), and angle values provide the degree of orthogonality between the two adjective axes. Orthogonality represents how independent two texture dimensions are. Thus, if two adjective scales (e.g., rough and hard) are close to orthogonal (i.e., 90 degree angles), it suggests that ratings along those scales contribute independently to the MDS perceptual space. These angles were measured between the high ends of the two adjective axes (where the words “Rough”, “Hard”, or “Sticky” are placed). MDS solutions of dissimilarity ratings are based on 3D models in which each axis (Dimension 1-3) is chosen arbitrarily to attain best fit between the model and normalized ratings. Averaged data over 8 subjects in each scanning condition were used. Note a large difference in angle between the hardness and stickiness axes across two modes of scanning (47^o: finger scanning, 150^o: probe scanning), demonstrating that the correlations of the ratings along these two continua are different across two modes of scanning.

Contact with a surface by means of a probe or tool generates considerable information about its texture. It is as though the tool becomes an extension of the hand, and we perceive the surface as if the hand were in direct contact with it (Katz, 1925/ Krueger, 1989). It has been suggested in a broader context, that the intelligent use of tools is a major characteristic distinguishing humans from other animals. Despite the fact that this type of interaction with the environment underlies such diverse activities as the use of canes by the blind, the manipulation of surgical instruments, and receiving feedback from prosthetic hands, relatively little is known about the perception of texture information via a probe and about its neural basis. The study of texture perception in indirect touch provides an opportunity to determine the essential dimensions in perception of textures or objects examined through a probe.

The results of our psychophysical study indicate that the roughness (and smoothness) information is carried by vibratory information, whereas hardness (and softness) information is given by compliance of texture surfaces. Stickiness (and slipperiness) information appears to be correlated with coefficient of friction at the interface between a probe and the surface (Figure 2). These data provide useful information in designing neural prosthetic device in which understanding of essential sensory information is crucial. These studies also provide better understanding of tactile perception through haptic interfaces such as remote tools used in laparoscopic surgery and tele-surgery.

Figure 2. Physical quantities associated with perceived roughness, hardness and stickiness when exploring textures through probes. A: Log power of texture-elicited vibrations vs. subjective roughness magnitude. Correlation coefficients between log vibratory power and perceived roughness, hardness, and stickiness were 0.92, 0.04 and 0.23, respectively. B: Perceived hardness vs. log relative compliance. Relative compliance was given by the ratio between the displacement of a Delrin 3-mm diameter probe into a textured surface and the weight that produced it (in cm/g). Correlation coefficients between log relative compliance and perceived roughness, hardness, and stickiness were 0.43, - 0.93, and 0.59, respectively. C: Perceived stickiness vs. the log coefficient of friction. Correlation coefficients between log coefficient of friction and perceived roughness, hardness, and stickiness were 0.57, -0.54, and 0.82, respectively. Thus, perceived roughness is associated with vibratory information, perceived hardness with relative compliance, and perceived stickiness with friction.

Figure 3. A model illustrating how the inputs from peripheral afferent neurons can form excitatory (E) and inhibitory (I) subfields of neurons in the cortex. This model depicts the basis for the spatial variation hypothesis of SAI neurons in perceiving roughness of grating patterns, in which the degree of roughness is embedded in the variation among peripheral afferent firings in spatial domain, closely matching the separation between excitatory and inhibitory regions of the cortical receptive field. The hypothetical E and I areas receive inputs from 6 SA1 afferents each as an example. The gray bars in the row marked E (I) represent the summed impulses in 12.5 ms bins in the excitatory (inhibitory) afferents. The solid line represents a smoothed (Gaussian kernel, 5 msec SD) estimate of the instantaneous rate. The row marked E-I plots the difference between E and I expressed as summed impulse rates. It is meant to represent the net excitatory drive that, when positive, produces a mean firing rate proportional to the difference in firing rates between afferents from the E and I subfields.

Computation of roughness E, I model:

Mean firing rate of the cortical neuron (= positive area of E –I) at zero threshold is:

where x is instantaneous firing rate, μ is mean of E-I, and P is probability distribution function for finding particular firing rate x.

If we substitute x by t + μ,

The first and second integrals defined as I₁and I₂ can be calculated as follows:

(More detailed calculation of I₁ is described below.)

One needs to find derivation of g(t)(i.e., d/dt(g(t)) that is equal to the content inside the integral (i.e., f(t)).

From the equation above,

For the second integral I₂ :

where G(μ) is Gaussian function. Mean μ is 0 because we made a substitution for x – μ to be t. Combining the solutions of two integrals, mean rate is going to be:

There are three situations as the solutions for this equation.

1) When E = I, μ = 0.

2) When E >> I, μ >> 0. This leads to I₁ = 0 and G(μ) = 1 because μ = infinity. Therefore,

3) When I << E, there will be no spikes.

In a realistic situation E and I are fairly balanced, and mean rate (positive area of E-I) is proportional to the standard deviation of E and I as the situation (1) shows. (It should also be noted that the variance of spikes are correlated with mean firing rate.

Following the psychophysical studies of roughness, we are now characterizing the properties of cortical neurons in areas 3b, 1 and 2 with multiple microelectrodes to determine how neurons in each cortical area integrate or segregate the mechanoreceptive information from the hand. Several types of neurons have been identified in areas 3b and 1 for their spatiotemporal receptive fields (STRF). The relationship between these neurons and texture perception will be explored.

Example of area 3b neuron with excitatory (red) and inhibitory (blue) regions

Example of area 1 neuron with excitatory (red) and inhibitory (blue) regions

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The BA/MS program in Neuroscience is designed to provide a full-year of intense research experience in a laboratory (plus a semester of thesis writing) to students who have a serious interest in clinical or basic neuroscience research. It provides necessary skills to be successful in the future MD/PhD or PhD program, while it also allows the students to develop the logical thought process in pursuing a degree in the MD program.

Students are expected to concentrate fully on their research, attend seminars and journal clubs, and write extensively about their research and related topics. This program offers both BA and MS degrees in 4 or 5 years. Applications from students in either junior or senior year are accepted in every fall semester. (For details, please contact Ms. Bobbie Tchopev, the Program Coordinater, bobbie@jhu.edu)

Over the summer 2006, the Neuroscience Program Committee reviewed the Bachelors Masters Concurrent Study Program (BA/MS) and instituted a number of changes in an effort to streamline the program. The subcommittee overlooking the review was headed by Dr. Brenda Rapp. After careful consideration, the steering committee approved all of the recommendations submitted by the subcommittee to put forth the new BA/MS Program this fall. The following is a summary of an interview with the BA/MS Program Director, Dr. Takashi Yoshioka.

TY: As we look ahead for the future of the Undergraduate Neuroscience Program, we needed to evaluate every aspect of the program and determine what areas should be strengthened. The BA/MS program in Neuroscience was identified as a valuable program that provides both undergraduate (BA) and graduate (MS) degrees in 4 or 5 years. With the intense research experience offered through this program, often culminating in the publication of their research, students have distinct advantage over other candidates when they apply to either MD/PhD, MD or PhD programs. The records show that 100% of our students who applied to medical schools were accepted, and a smaller number of students who applied to PhD graduate programs were all accepted as well. These medical school or graduate schools are top-notch schools in the country, and we are very proud of the achievements of our students. Rather than sitting on our laurels, we wanted to improve the program, and continue to effectively meet the needs of our students as they pursue their research goals, and assist them when they make career choices.

TY: One of the things we discussed was to make the program requirement more flexible for the students who wish to graduate in 4 years. We questioned how we could achieve it while maintaining and improving the quality of the program. In the end, we agreed to reduce certain course requirements, which used to take 3-4 semesters to complete, so that the students can now finish the MS portion of the program as short as in one year (i.e., 2 regular semesters) if all other requirements have been fulfilled. To maintain the quality of the program, however, we have established more stringent thesis evaluation system by a committee composed of the student’s mentor, the BA/MS program director, and one other faculty member.

CC: How did the idea of capping the maximum number of students in the program arise?

TY: The committee deemed appropriate to set a limit for the number of students admitted to the program based on the current number of applicants and the resources we have. This is no different from the class size limit each instructor assigns. Although the BA/MS program is selective in its nature, the size of the program could change depending on the demands in the future.

CC: How would the revised program benefit the students' educational experience?

TY: Students now have greater flexibility in their selection of courses. We used to have a policy of not allowing students to take a course during their research year so that they could fully devote themselves to their research. We are now allowing them to take one course per semester, provided that it is directly related to their research. This gives students the opportunity to choose a course that they realize will benefit their work once they start the full-year of thesis research. This may also help them spend less number of semesters to graduate since not all courses are offered in every semester.

CC: Were the students' and research mentors' perspectives taken into account?

TY: I had taken a survey from current students and some of those who graduated in the past before I went to meet with other members of the BA/MS subcommittee. The views and suggestions from the students are reflected in many of the changes we made. One change we implemented was to decrease the statistics course requirement from 8 credit hours to 3 credit hours. Although the new statistics course has less number of credit hours, it is closely tied to the neuroscience research application and we felt that the students will benefit from its relevance to their work. Many of the faculty members on the subcommittee are research mentors of the BA/MS students and their perspectives were also taken into account. For example, we will have a stronger collaborative relationship with the student’s mentors by sharing the responsibility of grading students’ assignments, and by being on the thesis defense committee.

TY: I believe that our future is bright, and I am fully committed to my students in this program. There is a trend among the admission committees of medical schools and graduate schools that places a high value on the previous research experience when they evaluate their applicants. They see students who have research experience as candidates who are trained to think logically and tackle problems with a solid scientific approach. The Neuroscience Undergraduate Program has become one of the largest majors on campus (third largest at the moment with nearly 250 students and counting), and the role of the BA/MS program is increasing. The BA/MS program now faces the challenge of accommodating the needs of the students who wish to have more intense research experience than those available in the regular BA program. To assist students with the financial responsibilities of staying at the school for an extra year, we currently offer a 50% tuition assistance package to those who are in the 5th year of the program. We are also working closely with the university administration, Dr. Adam Falk, the Dean of the School of Arts and Sciences, Dr. Guy McKhann, the director of the Krieger Mind/Brain Institute, and the leaders of the related academic departments so that we can develop a better and improved BA/MS Neuroscience Program. Their supports are overwhelming, and I am very optimistic about our future.

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(Official class for the students who have been admitted to the BA/MS program in Neuroscience)

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Gardner EP, Martin JH, Jessell TM (2000) Bodily senses. In: Principals of Neural Science, 4^th Edition (Kandel ER, Schwartz JH, Jessell TM eds) New York: McGraw-Hill, pp 430-450.

Hendry SHC, Hsiao SS, Bushnell MC (1999) Somatic sensation. In: Fundamental Neuroscience (Zigmond MJ, Bloom FE, Landis SC, Roberts JL, Squire LR eds), pp 761-789. San Diego: Academic Press.

Mountcastle VB (2005) The Sensory Hand. Neural Mechanisms in Somatic Sensation. Cambridge, MA: Harvard Uni Press. Chapters 4 and 5 (Sensory innervation of the primate hand, Large-fibered peripheral interface)

Johnson KO (2001) The roles and functions of cutaneous mechanoreceptors. Current Opinion in Neurobiology 11: 455-461.

Andrew D, Craig AD (2001) Spinothalamic lamina I neurons selectively sensitive to histamine: A central neural pathway for itch. Nat Neurosci 4: 72-77.

Olausson H, LaMarre Y, Backlund H, Morin C, Wallin BG, Starck G, Ekholm S, Strigo I, Worsley K, Vallbo AB, Bushnell MC (2002) Unmyelinated tactile afferents signal touch and project to insular cortex. Nat Neurosci 5: 900-904.

Gardner EP, Kandel ER (2000) Touch. In: Principals of Neural Science, 4^th Edition (Kandel ER, Schwartz JH, Jessell TM eds) New York: McGraw-Hill, pp 451-470.

Hendry SHC, Hsiao SS, Bushnell MC (1999) Somatic sensation. In: Fundamental Neuroscience (Zigmond MJ, Bloom FE, Landis SC, Roberts JL, Squire LR eds), pp 761-789. San Diego: Academic Press.

Mountcastle VB (2005) The Sensory Hand. Neural Mechanisms in Somatic Sensation. Cambridge, MA: Harvard Uni Press. Chapters 7-10

Kaas JH, Jain N, Qi HX (2002) The organization of the somatosensory system in primates. In: The Somatosensory System: Deciphering the Brain's Own Body Image (Nelson RJ, ed), pp 1-25. Boca Raton: CRC Press.

Eickhoff SB, Schleicher A, Zilles K and Amunts K (2006) The Human Parietal Operculum. I. Cytoarchitectonic Mapping of Subdivisions. Cerebral Cortex 16:254-267.

Schleicher A, Amunts K, Geyer S, Morosan P, and Zilles K\(1999) Observer-Independent Method for Microstructural Parcellation of Cerebral Cortex: A Quantitative Approach to Cytoarchitectonics. NeuroImage 9: 165–177.

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Conduction velocity, CV, depends on axon diameter. The relationship is different for myelinated and unmyelinated axons. For unmyelinated axons

For example, the squid giant axon that Hodgkin and Huxley used to figure out the mechanisms of the action potential was about 1 mm in diameter (1000 microns) and conducted action potentials at about 30 m/sec. For myelinated axons

a. A typical median nerve is about 2 mm in diameter and although there are more unmyelinated than myelinated axons, myelinated axons take up almost all the space. So, the speed gained by myelination is costly but not as costly as if there was no myelination. Suppose the myelinated axons conduct action potentials at an average rate of 50 m/sec. How large would the median nerve be if those same axons were unmyelinated but still conducted action potentials at 50 m/sec? How large would the spinal cord be if it was composed of unmyelinated fibers? Would a nervous system as fast and complex as a mammalian nervous system be possible without myelination?

b. What are the CVs of nociceptive, thermoreceptive, cutaneous mechanoreceptive, and proprioceptive afferent fibers?

c. Suppose that these CVs are not accidents of nature, i.e. that they are optimum for the functions that they serve. Why would the nociceptive and thermoreceptive afferent fibers, which signal impending danger, have the lowest CVs?

1) The median nerve is crushed so that all axons at one location are ruptured but the nerve is not severed.

a. What are the expected symptoms immediately after the injury (within the first week or so)?

b. What are the expected symptoms one or two years after the injury (i.e., when the long term effects have stabilized)?

c. Is there a functional reason? Does columnar organization affect brain wiring?

Q1a. How large would the median nerve (spinal cord, brain) be if its axons were unmyelinated but conducted at an average rate of 50 m/sec?

A1a. The myelinated axons conducting at 50 m/sec are about 8 μm in diameter. The unmyelinated axons would have to be 2500 μm in diameter (square root of 2500 = 50), i.e. 300 times larger. The median nerve would have to be about 60 cm in diameter. The spinal cord is about 1 cm in diameter. If it was composed of unmyelinated axons and conducted at an average rate of 50 m/sec it would have to be 3 meters in diameter. I think it could be proven that a reasonably fast complex nervous system would be impossible without myelination. For example, on the same 300 to 1 rule, the brain would have to be about 50 meters in diameter it it was composed of unmyelinated axons with average speeds of 50 m/sec. But then it would take one second for a message to get from occipital to frontal cortex. To reduce that to a reasonable time (e.g., 10 msec) the CV would have to be increased to 5 km/sec but then the brain would have to be 500 km in diameter (with unmyelinated axons) and it would take 100 sec for a message to get from occipital cortex. To reduce that to 10 msec.

Q1b. What are the conduction velocities of nociceptive, thermoreceptive, cutaneous mechanoreceptive, and proprioceptive afferent fibers?

Q1c. Suppose that these conduction velocities are not accidents of nature, i.e. that they are optimum for the functions that they serve. Why would the nociceptive and thermoreceptive afferent fibers, which signal impending danger, have the lowest conduction velocities?

A1c.The most distant point from the spinal cord in humans is approximately one meter. The fastest Aδ nociceptive fibers conduct information to the cord in 30 msec or less. Since the withdrawal reflexes are much slower than 30 msec not much would be accomplished by faster conduction times.

A1d. Higher conduction velocities require larger axon diameters. The cross-sectional area occupied by an axon increases as the square of its diameter. Aα fibers (mean diameter = 16μ) occupy 25 times more cross-sectional area than do Aδ fibers (mean diameter = 3.5μ).

Q1e. Why do proprioceptive afferent fibers need the highest conduction velocities?

A1e. Proprioceptive afferent fibers are critical for the control of movement. Muscle spindle afferents relay information about muscle length and velocity. Golgi tendon organ afferents relay information about muscle force. Many motor acts are extremely rapid. For example, a typist who types at 100 words per minute, which is not uncommon, are typing 10 characters per second, i.e. one character per 100 msec. Typing one character involves a complex sequence of movements, each of which is executed in a fraction of 100 msec. The problem with the small afferent fibers is not only lack of speed but also variation in speed between afferents. Aδ conduction velocities range from 6 to 36 m/sec. The Aδ conduction times from the fingers to the cord range from 30-160 msec (1 m divided by 6 to 36 m/sec). That is, signals that start out synchronized become dispersed by up to 130 msec. Under those conditions, separate signals separated by less than 130 msec become smeared together. The maximum typing speed would probably be 10 words per minute (one character per second) if proprioceptive signals were sent by Aδ fibers. Aα conduction times from the fingers to the spinal cord range from 8-14 msec, which involves dispersion of only 6 msec. Six milliseconds is much closer to the temporal precision required for rapid, coordinated movements.

Q1f. Why do cutaneous mechanoreceptors need moderately high conduction velocities?

A1f. The arguments are similar to those for proprioceptors above. Braille reading rates among the blind vary as do sighted reading rates. Reading rates of 100 words per minute are not uncommon. This corresponds to a scanning rate of 20-50 mm/sec, depending on the type of Braille. Therefore Braille dots, which are separated by about 2 mm, follow at intervals of 40-100 msec in ordinary reading. The information from separate Braille dots would blur together if the information was transmitted by Aδ fibers (temporal dispersion = 130 msec). The dispersion among Aβ afferent fibers (cond. vel. 36-60 m/sec) between the fingers and spinal cord is about 15 msec.

Q2a. What are the expected symptoms immediately after the injury (within the first week or so)?

A2a. Nerve axons are contained within a series of sheaths: axons < Schwann cells < endoneurial tubes < funiculi < nerve (<, within). Endoneurium surrounds each axon; perineurium encircles each funiculus (up to 100 funiculi in sciatic nerve); epineurium surrounds the funiculi to make up the entire nerve. The sciatic nerve, e.g., is composed of approximately 100 funiculi. Immediately following an injury that ruptures all of the axons within a nerve, the innervated region is anesthetic but the person may feel unnatural sensations, which are most likely caused by impulses produced at the injured endings. (The preceding is the answer to the first question.) Recovery depends on the degree of injury. Nerve injuries are graded (S. Sunderland, 1978). A 1st degree injury is just a local conduction block. In a 2nd degree injury, axons rupture but the endoneurial tubes remain intact. In that case, the distal part of the axon and its Schwann cells degenerate. Then, the proximal axon sprouts and grows out into the intact endoneurial tube and on to its original destination. Recovery is complete. In a 3rd degree injury, the axon and the endoneurial tube are ruptured (this happens, e.g., in stretch and crush injuries). When the axons sprout they may or may not end up in their original tubes or they may not find one at all. Only a fraction of axons may grow back to their original locations so recovery is incomplete. In a 4th degree injury, the funiculi are ripped apart but the nerve is not completely severed. When axons sprout and regrow, they often end up in the wrong funiculi. In a 5th degree injury, the nerve is completely severed. In this case, the ends can be sutured back together and nerves grow out through the distal nerve in large numbers. The reinnervated skin may have normal sensitivity to touch but have impaired spatial acuity because the axons no longer innervate the skin in a way that the brain can interpret (like scrambling the pixels in a video display). An interesting case is presented by transplanting glabrous skin from the sole of the foot to the hand along with its nerve supply (i.e., nerve from foot skin is sutured to a nerve in the palm of the hand). In that case, people can sometimes perceive location well and even discriminate the direction of motion across the skin surface. The mechanism is unknown. Does the brain somehow learn what the patterns mean or does the nerve grow into the new tissue in some reasonably organized way?

Q2b. What are the expected symptoms one or two years after the injury (i.e., when the long term effects have stabilized)?

A2b. When the nerve is severed permanently, humans experience an area of total anesthesia corresponding to the territory of the damaged nerve and paresthesias such as burning sensations or tingling when tapping of the site of nerve injury. When a limb is amputated (and therefore all the nerves are severed), 95-100% of humans experience some kind of phantom limb sensation (sensation in the missing limb). Phantom limbs usually have a tingling feeling and have a definite shape. Initially, the phantom limb feels real, but over time the sensation feels less distinct and often fades. The existence of phantom limb sensations suggests that neurons in the central nervous system retain their original sensory meaning and that activation of these neurons, which occurs for example when the stump of the amputation is touched, causes the original sensations to reappear. Another mechanism that accounts for these phantom sensations is cortical remapping in which adjacent sites on the body map send new projections to areas of cortex that originally responded to the skin innervated by the severed nerve. Stimulation of these adjacent skin sites now drives neurons previously driven by stimulation of the amputated skin (but they retain their original sensory meaning).

A3a. Cortical columns are vertical cell columns with two characteristics: (1) neurons with similar functional properties and (2) boundaries where the functional properties change more or less abruptly. The second characteristic is critical. Otherwise, the properties could just change gradually across the cortex and there would be no columns. The shape of the column is typically cylindrical, but in some cases slab-like (i.e., stripes), spanning all six cortical layers from the pia to the white matter. They are ubiquitous among mammalian cerebral cortices, and found in the somatosensory, motor, visual, auditory, and other cortical areas. In primary somatosensory cortex, columns of neurons have the same receptive field locations and they receive their inputs from a single primary afferent type (as far as we know). In the visual cortex, a clear segregation of columns has been reported for processing of specific orientations, directions of stimulus movement, specific eye input, etc.

The size of columns varies between cortical areas and species, measuring approximately 200-600 μm across. The columns are composed of groups of neurons whose apical dendrites, axons, and cell bodies form the basic columnar structure running through all six cortical layers. In the primary sensory cortex, the incoming axons from the thalamus typically terminate on spiny stellate neurons in layer IV. From layer IV, axons project vertically to dendrites in layer II/III of pyramidal neurons whose somata reside in either layer II/III or layer V. Both supragranular (layer II/III) and infragranular (layer V) pyramidal neurons often project their axons horizontally within their own layers and form patchy axon terminals of 300 mm in diameter. These intrinsic horizontal connections travel up to a few mm from the cell body with average inter-patch distance of 600- 800 mm. Because of these recurrent connections of excitatory neurons within the column and inhibitory interneurons which sharpen the columnar boundary, physiological properties of neurons within a single column tend to be very similar, and the receptive fields of neurons from different layers overlap.

Q3b. Why would the cerebral cortex be organized as separate functional modules?

A3b. The general plan of information processing in the brain, so far as we understand it, is to break the primary sensory information into stimulus categories (e.g., form, texture, motion, force, and location, in the somatosensory system; form, motion, location, color, and depth in the visual system) and process them separately. The brain doesn’t do this all in one step. It does it over many steps. The process is called neural computation. The most economical way to do this is to bring all the information required for one kind of computation to a local circuit. But the computations required to compute motion are different from those required to compute form, and so on. So, the cortex is divided into modules that do different tasks. It is a way of mapping many functions onto the two-dimensional cortex.

A3c. The neurons that do a certain computation don’t have to be together. Neurons performing different functions could be spread out but the wring (the axons that interconnect the neurons) would be much longer, the cortical volume would be proportionately larger, and the developmental issues might be much more complex.

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Yoshioka T, Bensmaia SJ, Craig JC, Hsiao SS (2007) Texture perception through direct and indirect touch: An analysis of perceptual space for tactile textures in two modes of exploration. Somatosens Mot Res 24: 53-70.

Hsiao SS, Fitzgerald PJ, Thakur PH, Denchev P, Yoshioka T (2007) Receptive Fields of Somatosensory Neurons. In: New Encyclopedia of Neuroscience.

Bensmaia SJ, Craig JC, Yoshioka T, Johnson KO (2006) SA1 and RA afferent responses to static and vibrating gratings. J Neurophysiol 95: 1771-1782.

Muniak, M. A., Hsiao, S. S., Dammann, J. F., Yoshioka, T., and Bensmaia, S. (2006) The peripheral representation of vibrotactile intensity: Correlating psychophysics with neurophysiology. Society for Neuroscience Abstracts. Atlanta, GA, SFN36.

Sripati AP, Yoshioka T, Denchev P, Hsiao SS, Johnson KO (2006) Spatiotemporal receptive fields of peripheral afferents and cortical area 3b and 1 neurons in the primate somatosensory system. J Neurosci 26: 2101-2114.

Yoshioka, T., Bensmaia, S., Craig, J. C., Hsiao, S. S., Ray, S., Watson, A. C., Carey, L. E., and Johnson, K. O. (2005) Tactile texture perception using probe or bare finger. Society for Neuroscience Abstr. 626.8.

Hsiao SS, Johnson KO, Yoshioka T (2003) Processing of tactile information in the primate brain. In: Comprehensive Handbook of Psychology, Volume 3: Biological Psychology (Gallagher M, Nelson RJ, eds), pp 211-236. New York: Wiley.

Hsiao SS, Yoshioka T, Johnson KO (2002) Neural basis of somesthesis. In: Encyclopedia of Cognitive Science Macmillan.

Johnson KO, Hsiao SS, Yoshioka T (2002) Neural coding and the basic law of psychophysics. Neuroscientist 8: 111-121.

Johnson KO, Yoshioka T (2002) Neural mechanisms of tactile form and texture perception. In: The Somatosensory System: Deciphering the Brain's Own Body Image (Nelson RJ, ed), pp 73-101. Boca Raton: CRC Press.

Yoshioka, T., Lawson, J. J., Denchev, P., Vega-Bermudez, F., and Johnson, K. O. (2002) Spatiotemporal receptive fields in SI cortex of the alert monkey. Society for Neuroscience Abstracts , 650.8. Soc for Neurosci.

Yoshioka T, Gibb B, Dorsch AK, Hsiao SS, Johnson KO (2001) Neural coding mechanisms underlying perceived roughness of finely textured surfaces. J Neurosci 21: 6905-6916.

Fitzgerald, P. J., Mikula, S. A., Presad, S., Yoshioka, T., and Hsiao, S. S. (2000) Functional Organization And Cortical Connections Of Macaque Second Somatosensory Cortex. Society for Neuroscience Abstracts 547.1. New Orleans, LA, SFN30.

Johnson KO, Yoshioka T, Vega-Bermudez F (2000) Tactile functions of mechanoreceptive afferents innervating the hand. J Clin Neurophysiol 17: 539-558.

Hsiao SS, Johnson KO, Yoshioka T, Pawluk D (1999) Tactile processing of afferent information from the periphery through the central nervous system. In: Human and Machine Haptics: (Howe RD, Cutkosky MR, Salisbury JK, Srinivasan MA, eds), Cambridge, Mass: MIT Press.

Yoshioka T (1999) Modular organization of macaque monkey primary visual cortex (area V1). Brain Science (Japanese, No-no-Kagaku), 21: 507-518.

Yoshioka T, Dow BM (1996) Color, orientation and cytochrome oxidase reactivity in areas V1, V2 and V4 of macaque monkey visual cortex. Behav Brain Res 76: 71-88.

Yoshioka T, Dow BM, Vautin RG (1996) Neuronal mechanisms of color categorization in areas V1, V2 and V4 of macaque monkey visual cortex. Behav Brain Res 76: 51-70.

Yoshioka T, Blasdel GG, Levitt JB, Lund JS (1996) Relation between patterns of intrinsic lateral connectivity, ocular dominance, and cytochrome oxidase-reactive regions in macaque monkey striate cortex. Cereb Cortex 6: 297-310.

Levitt JB, Yoshioka T, Lund JS (1995) Connections between the pulvinar complex and cytochrome oxidase-defined compartments in visual area V2 of macaque monkey. Exp Brain Res 104: 419-430.

Xiang M, Zhou L, Macke JP, Yoshioka T, Hendry SH, Eddy RL, Shows TB, Nathans J (1995)

Date\student	AM	CE	HM	Comment
9/4/2008				roundtable discussion with Dr. Ed Connor
9/11/2008	x
9/18/2008		x
9/25/2008			x
10/2/2008				Yoshioka lecture: History of Neuroscience
10/9/2008	x
10/16/2008		x
10/23/2008			x
10/30/2008	x
11/6/2008		x
11/13/2008				Neurosci meeting prep
11/20/2008				Neurosci meeting
11/27/2008				Thanksgiving break
12/4/2008			x	Last class

X:presents

9/4/2008			JHU first day of class
12/8/2008			last day of class
12/9~12/11/08			reading period
12/12~12/19/08			final exam period
12/20-1/4/2009			Mid-year vacation