Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall Lecture 13. FARS 2 1 Michael Arbib: CS564 - Brain Theory and Artificial Intelligence University of Southern California, Fall 2001 Lecture 13. The FARS model of Control of Reaching and Grasping 2 Reading Assignments: FARS Model: Fagg, A. H., and Arbib, M. A., 1998, Modeling Parietal-Premotor Interactions in Primate Control of Grasping, Neural Networks, 11: The class also reviewed material on serial order and basal ganglia contained in the slides for Lecture 9. FARS Model 1

Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall Lecture 13. FARS 2 2 Coarse Coding/Population Coding To code some variable x lying in an interval [a,b) we could take n cells, with cell i (i = 0, …, n-1) firing if and only if the current value of x lies in the i th subinterval In coarse coding, we achieve much greater discrimination by taking into account the continuously varying firing level f i of each cell, and then we can decode values of x actually varying across each interval, using some such formula as Note: In the Georgopoulos study we saw “negative votes” for firing below the neuron’s resting discharge rate.

Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall Lecture 13. FARS 2 3 The “Visual Front End” of the FARS Model Visual Cortex Parietal Cortex VIP PIPAIP F4 How (dorsal) IT What (ventral) (position) (shape, size, orientation) (object/grasp transform) F5 (grasp type) (arm goal position)

Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall Lecture 13. FARS 2 4 cIPS*  IT connections are hard-wired for a simple set of objects Note the use of coarse coding * In the paper we spoke of PIP where we now say cIPS.

Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall Lecture 13. FARS 2 5 cIPS  AIP connections are hard-wired for a simple set of affordances

Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall Lecture 13. FARS 2 6 IT  AIP The mapping from object identity in IT to maps directly to both the grasp type and the aperture of grasp in AIP when the nature of the object implies such data: E.g., in the case of AT, the projection from IT can provide the necessary grasp type and parameters for a lipstick but not for a cylinder. A bottle cap activates a precision grasp with a narrow aperture. A jar top maps to a precision grasp with a wide aperture.

Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall Lecture 13. FARS 2 7 cIPS  IT connections are hard-wired for a simple set of objects Note the use of coarse coding

Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall Lecture 13. FARS 2 8 F5 activity during execution of a precision grasp The top two traces show the position of the thumb and index finger. Left: The next five traces represent the average firing rate of five F5 neurons (set-, extension-, flexion-, hold-, and release-related). The remaining five traces represent the various external (Ready, Go, Go2) and internal (SII) triggering signals. Right: Illustrating the temporally distributed coding of F5 cells.

Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall Lecture 13. FARS 2 9 Positioning F2, F6 and Areas 46 and SII in Monkey

Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall Lecture 13. FARS 2 10 Prefrontal Influences on F5 F4 Inferior Premotor Cortex F5 (grasp type) F6 46 (arm goal position) F2 (abstract stimuli) pre-SMA Dorsal premotor cortex Frontal Cortex

Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall Lecture 13. FARS 2 11 Grasp Selection in F5 Within F5, the active grasps compete through a winner-take-all Area 46, working in conjunction with F6, supplies task-dependent biases for grasp selection in F5, based upon  task requirements (such as what is going to be done after the grasp), or  a working memory of a recently executed grasp. The biasing can  be on the class of grasp (e.g. power versus precision), or  include the parameters of the grasp (e.g. width of aperture) mechanism which incorporates any biases that might be received from area 46.

Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall Lecture 13. FARS 2 12 Supplementary Motor Area (SMA) in Monkey  a unilateral lesion of the SMA disrupts the monkey's ability to allocate his hands to different subtasks of a bimanual task (Brinkman, 1984)  SMA is involved in the temporal organization of complex movements (Tanji, 1994). Luppino, Matelli, Camarda, & Rizzolatti subdivide SMA: SMA-proper (F3; the caudal region) has heavy projections to the limb regions of F1 and related portions of the spinal cord. F6 (pre-SMA) does not project to the spinal cord, and has only moderate projections to areas F3 and F2 (the dorsal premotor cortex), but has a very heavy projection to area F5.

Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall Lecture 13. FARS 2 13 Pre-SMA F6 (pre-SMA) has a very heavy projection to area F5. Inputs into area F6 include VIP, and area 46. F6 contains neurons that become active when an object that the monkey is about to grasp moves from being out of reach into the peripersonal space of the monkey Interpretation: this class of pre-SMA (F6) neuron is responsible for generating a go signal when it is appropriate for the monkey to begin a reaching movement. F4 Inferior Premotor Cortex F5 (grasp type) F6 46 (arm goal position) F2 (abstract stimuli) Frontal Cortex pre-SMA Dorsal premotor cortex

Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall Lecture 13. FARS 2 14 Area 46 Area 46 has been implicated as a working memory in tasks requiring information to be held during a delay period (Quintana & Fuster, 1993). This memory has been posited to participate in learning tasks involving complex sequences of movements (Dominey, 1995). Area 46 projects to F6, and also exchanges connections with area F5 (Luppino, et al., 1990; Matelli, 1994). When a human is asked to imagine herself grasping an object, activated areas (PET or fMRI) involved include:  Area 46 (Decety, Perani, Jeannerod, Bettinardi, Tadary, Woods, 1994)  Area 44 (a possible F5 homologue)  A site along the intra-parietal sulcus (Grafton, et al., 1996).

Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall Lecture 13. FARS 2 15 Role of Area 46 for Grasping in the Dark During the performance of the task in the light, area 46 maintains a memory of those F5 cells that participate in the grasp. To simulate performance in the dark, PIP and IT are then cleared. If a new trial is initiated soon enough, area 46 provides positive support to those F5 cells that were active during the first trial. The area 46 working memory provides a static description of the grasp that was recently executed, in the sense that the temporal aspects of the grasp are not stored - only a memory of those units that were active at some time during the execution. [Area 46 is involved in human imagination of grasp execution.] F4 Inferior Premotor Cortex F5 (grasp type) F6 46 (arm goal position) F2 (abstract stimuli) Frontal Cortex pre-SMA Dorsal premotor cortex

Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall Lecture 13. FARS 2 16 Conditional Tasks and Area F2 Dorsal premotor cortex (F2) is thought to be responsible for the association of arbitrary stimuli (an IS) with the preparation of motor programs (Evarts, et al., 1984; Kurata & Wise, 1988; Mitz, Godshalk, & Wise, 1991; Wise & Mauritz, 1985). In a task in which a monkey must respond to the display of a pattern with a particular movement of a joystick:  some F2 neurons respond to the sensory-specific qualities of the input. However,  many F2 units respond in a way that is more related to the motor set that must be prepared in response to the stimulus. When a muscimol lesion in this region is induced, the monkey loses the ability to correctly make the arbitrary association. F4 F5 (grasp type) F6 46 (arm goal position) F2 (abstract stimuli) pre-SMA Dorsal premotor cortex In addition to simulating the Sakata task, we simulate conditional tasks.

Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall Lecture 13. FARS 2 17 F5 activity during execution of a precision grasp The top two traces show the position of the thumb and index finger. Left: The next five traces represent the average firing rate of five F5 neurons (set-, extension-, flexion-, hold-, and release-related). The remaining five traces represent the various external (Ready, Go, Go2) and internal (SII) triggering signals. Right: Illustrating the temporally distributed coding of F5 cells.

Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall Lecture 13. FARS 2 18 The Complete FARS Model

Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall Lecture 13. FARS 2 19 Thumb and index finger temporal behavior as a function of cylinder size

Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall Lecture 13. FARS 2 20 F5 cell responses during precision grasps of seven different apertures This particular cell is active only for narrow precision pinches

Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall Lecture 13. FARS 2 21 F5 movement-related cell (A) and a hold-related (B) cell during the perturbation experiment 20mm/30mm traces correspond to presentation and grasping of a 20mm and a 30mm cylinder, respectively; traces labeled 20  30 and 30  20 indicate perturbation trials, in which a 20mm cylinder is switched for a 30mm cylinder, and a 30mm cylinder for a 20mm one, respectively.

Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall Lecture 13. FARS 2 22 Two objects that map to the identical grasp

Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall Lecture 13. FARS 2 23 Comparison of population responses towards two different objects (but identical grasps). Lighted movement task, AIP (A) and F5 (B) cells; and AIP populations during fixation (C) and dark movement (D) tasks.

Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall Lecture 13. FARS 2 24 F5 Feedback to AIP  Visual-related AIP receive object-specific inputs; motor-related cells receive recurrent inputs from F5, which do not demonstrate object-specific activity.

Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall Lecture 13. FARS 2 25 A single object mapping to two possible grasps  Before execution, one grasp must be selected based upon the current context (e.g., based upon an Instruction Stimulus).

Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall Lecture 13. FARS 2 26 Two F5 units (A, B) in response to the four conditions: (c,pr), (c,pw), (nc,pr), and (nc,pw). c = conditional; nc = non- conditional; pr = precision pinch; pw = power grasp.

Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall Lecture 13. FARS 2 27 Four boxes of different dimensions Grasping is performed along the horizontal axis. The two blocks in the left column are grasped using a precision pinch of a 10mm aperture; the blocks on the right require a 20mm aperture.

Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall Lecture 13. FARS 2 28 Comparison of AIP visual responses for objects of the same (A) and different (B) widths; and AIP motor- related responses (dark movement condition) for objects of same (C) and different (D) widths.