Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall Lecture 14. FARS and Synthetic PET Michael Arbib: CS564 - Brain Theory and Artificial Intelligence University of Southern California, Fall 2001 Lecture 14. FARS and Synthetic PET Reading Assignment: Arbib, M.A., Billard, A., Iacoboni, M., and Oztop, E., 2000, Synthetic Brain Imaging: Grasping, Mirror Neurons and Imitation, Neural Networks, 13: In addition to the material on FARS and Synthetic PET covered in class, the paper contains material on imitation - fMRI data, a model by Aude Billard and a Synthetic fMRI study, as well as some notes on the MNS model.

Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall Lecture 14. FARS and Synthetic PET Synthetic PET: Analyzing Large-Scale Properties of Neural Networks Arbib, M.A., Bischoff, A., Fagg, A. H., and Grafton, S. T., 1994, Synthetic PET: Analyzing Large-Scale Properties of Neural Networks, Human Brain Mapping, 2: The issue here is to how to map  simulated activity of the neurons in models of interacting brain regions based on, say, single-cell recordings in behaving monkeys into  predictions of activity values to be recorded from corresponding regions of the human brain by imaging techniques such as positron emission tomography (PET).

Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall Lecture 14. FARS and Synthetic PET Modeling activation PET typically measures local cerebral blood flow (CBF). The key hypothesis of our method is that  the counts acquired in PET scans are correlated with the synaptic activity within this region. However, PET studies typically do not work directly with raw PET activity but rather with the  comparative values of this activity in a given region for two different tasks or behaviors. to estimate task specific modulations of local activity.

Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall Lecture 14. FARS and Synthetic PET Localization Each array in the neural network model represents a neural population in a region identified anatomically and physiologically in the monkey brain. A synthetic PET comparison requires explicit hypotheses stating that each such region A is homologous to a region h(A) in the human brain. Comparison of a synthetic PET study with the results of a human brain scan study will, inter alia, be a test of the hypothesis  "h(A) in human is homologous to A in (a given species of) monkey".

Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall Lecture 14. FARS and Synthetic PET Assumptions: H1. All synaptic contacts are made within the region in which the cell body is located. H2. The contribution to the blood-flow measured by PET in both inhibitory and excitatory synapses is defined by the integral of the absolute value of the synaptic weight times the spike rate incident upon the synapse. H3. We can lump all cells within a neural region into a single sum for an individual region. duration of the scan. Note that our methodology will put H2 to specific test. Computing raw PET activity

Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall Lecture 14. FARS and Synthetic PET Creation of the synthetic PET comparison where rPET A (i) is the value of rPET A in condition i. We may convert the value of the "synthetic PET comparison" PET A (1/2) to a color scale, and display the colors on the region h(A) homologous to A on slices based on the Talairach Atlas. As a computational plus (going beyond the imaging technology), we can also collect the contributions of the excitatory and inhibitory synapses separately, based on evaluating the integral in (1) over one set of synapses or the other.

Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall Lecture 14. FARS and Synthetic PET Another View of FARS The precision pinch and power grasp pools in F5 and AIP are connected through recurrent excitatory connections. The precision pinch pool contains more neurons than other grasps, which effects the Synthetic PET measure in these and downstream regions. F6 (pre-SMA) represents the high-level execution of the sequence, phase transitions dictated by the sequence are managed by the basal ganglia (BG). The dorsal premotor cortex (F2) biases the selection of grasp to execute as a function of the presented instruction stimulus.

Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall Lecture 14. FARS and Synthetic PET Conditional Tasks and Area F2 In addition to simulating the Sakata task, we simulate conditional tasks. Here the crucial observation is that: Dorsal premotor cortex (F2) is thought to be responsible for the association of arbitrary stimuli (an IS) with the preparation of motor programs. 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.

Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall Lecture 14. FARS and Synthetic PET Modeling Interactions of F2, F5, and AIP Basal Ganglia (BG) appears implicitly - mediating the recurrent inhibitory connections back to F5 and AIP.

Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall Lecture 14. FARS and Synthetic PET The PET Grasping Experiments: Conditions Control  No movement; only watch lights Power Grasp  Green Light indicates which block to grasp Precision Pinch  Green Light indicates which switch to pinch Conditional vs. Non-Conditional Task  Grasp a cylinder using either a precision pinch or a power grasp, the choice being determined by an instruction stimulus (the color of a light).

Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall Lecture 14. FARS and Synthetic PET The PET Grasping Experiments: Apparatus

Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall Lecture 14. FARS and Synthetic PET Precision Pinch versus Power Grasp Basic Model Assumptions Reflected Here:  F5 has more cells to code precision than pinch  SII has more cells to code expectation  MCx has to do more work for precision

Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall Lecture 14. FARS and Synthetic PET PET Results : Precision versus Power Precision versus power grasp (lower panel). +50: L - SMA Proper R - Dorsal Premotor Cortex. No show: F5  Ventral Premotor Cortex!! +32:L - Inferior Parietal  AIP - 16:L - Cerebellar Vermis Also: Contralateral Occipital Lobe Model predicts increases in AIP and F5. AIP and F5 are co-sensitive in the current model. Data only see AIP. Why no F5?  AF: Stereotypical grasp or Force recruitment in power grasp  MA: Few cells code a specific pinch Coding issues are crucial.

Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall Lecture 14. FARS and Synthetic PET PET Results: Precision versus Power +50: L - SMA Proper R - Dorsal Premotor Cortex. No show: F5  Ventral Premotor Cortex!! +32:L - Inferior Parietal  AIP - 16:L - Cerebellar Vermis Also: Contralateral Occipital Lobe Model predicts increases in AIP and F5. AIP and F5 are co-sensitive in the current model. Data only see AIP. Why no F5?  AF: Stereotypical grasp or Force recruitment in power grasp  MA: Few cells code a specific pinch Coding issues are crucial.

Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall Lecture 14. FARS and Synthetic PET Conditional Task versus (Precision, Power) Average Basic Model Assumption Reflected Here:  F2 processes the instruction stimuli only in the conditional task  Second order effect on F5 due to F2 input: this activity level is passed to AIP and BG

Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall Lecture 14. FARS and Synthetic PET PET Results: Conditional versus Non-Conditional Conditional versus Non-Conditional (upper right panel). R - Area 18 (2 sites) L - Area 18/19 R - Cerebellar cortex F2 and AIP are activated; F5 is not.  F2: Data and model agree  AIP: Human has more activation than model  F5: Human has no change but model does increase slightly.

Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall Lecture 14. FARS and Synthetic PET Model and Data: Toward Reconciliation In current model, F2  F5 is a free parameter. F2  F5  AIP forces AIP to change less than F5. AF: A possible solution - reroute F2  F5 to F2  AIP

Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall Lecture 14. FARS and Synthetic PET PP (Posterior Parietal) affordances for PM precise motor coordinates for motor cortex PM (Premotor Cortex) codes action fairly abstractly Motor Cortex and MPGs tell PM the possible categories “here’s my choice” the details - action parameters overall action Cerebellum Tuning and coordinating MPGs Should we Accept this Perspective on F5-AIP Interactions?

Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall Lecture 14. FARS and Synthetic PET Synthetic PET: A Tender Seedling Synthetic PET forces attention to details of human data while highlighting assumptions made in monkey models.  Assumptions must be made to bridge from a limited data set to explanations of coherent functioning at cellular and behavioral levels. Synthetic PET both benefits from and contributes to better understanding of homologies between human and monkey. We need further research on metabolic correlates of neural information processing: synaptic activity axonal activity synaptic plasticity gene expression, glial function, etc., etc.

Michael Arbib CS564 - Brain Theory and Artificial Intelligence, USC, Fall Lecture 14. FARS and Synthetic PET And the Method is Much More General  Homologies with non-primate species  Extension to fMRI and other imaging techniques - with further research on metabolic correlates of neural information processing. The Grand Aim:  Increased Progress in Systems Neuroscience by Developing Modeling Tools that Catalyze Progress through the Integrated Use of Human and Animal Data