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Prefrontal cortex: categories, concepts and cognitive control Earl K. Miller Picower Center for Learning and Memory, RIKEN-MIT Neuroscience Research Center, and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology www.millerlab.org Executive (cognitive) control – The ability of the brain to wrest control of its processing from reflexive reactions to the environment in order to direct it toward unseen goals. Volition, goal-direction Sensory Motor Basic sensory and motor functions Sensory Motor Consolidation (long-term storage) Memories, habits and skills Learning and memory (Hippocampus, basal ganglia, etc.) Executive Functions goal-related information Sensory Motor Consolidation (long-term storage) Learning and memory (Hippocampus, basal ganglia, etc.) Top-down Executive Functions goal-related information Selection Bottom-up (flexibility) Sensory Motor Consolidation (long-term storage) Learning and memory (Hippocampus, basal ganglia, etc.) Top-down Executive Functions goal-related information Selection Bottom-up (flexibility) Sensory Motor Consolidation (long-term storage) Learning and memory (Hippocampus, basal ganglia, etc.) Top-down Executive Functions goal-related information Selection Bottom-up (flexibility) Sensory Motor Consolidation (long-term storage) Learning and memory (Hippocampus, basal ganglia, etc.) Top-down Executive Functions goal-related information Selection Bottom-up (flexibility) Sensory Motor Consolidation (long-term storage) Learning and memory (Hippocampus, basal ganglia, etc.) Top-down Executive Functions goal-related information Selection Bottom-up (flexibility) Sensory Motor Consolidation (long-term storage) Learning and memory (Hippocampus, basal ganglia, etc.) Top-down Executive Functions goal-related information Selection Bottom-up (flexibility) Sensory Motor Consolidation (long-term storage) Learning and memory (Hippocampus, basal ganglia, etc.) Top-down Executive Functions goal-related information Selection Bottom-up (flexibility) Sensory Motor Consolidation (long-term storage) Learning and memory (Hippocampus, basal ganglia, etc.) Top-down Executive Functions goal-related information Selection Bottom-up (flexibility) Sensory Motor Consolidation (long-term storage) Learning and memory (Hippocampus, basal ganglia, etc.) Top-down Executive Functions goal-related information Selection (flexibility) Sensory Motor Consolidation (long-term storage) Learning and memory (Hippocampus, basal ganglia, etc.) Our Methods: Train monkeys on tasks designed to isolate cognitive operations related to executive control. Record from groups of single neurons while monkeys perform those tasks. Top-down Executive Functions goal-related information Selection Bottom-up (flexibility) Sensory Motor Consolidation (long-term storage) Learning and memory (Hippocampus, basal ganglia, etc.) Perceptual Categories David Freedman Maximillian Riesenhuber Tomaso Poggio Earl Miller www.millerlab.org Perceptual Categorization: “Cats” Versus “Dogs” 80% Cat Morphs 60% Cat Morphs 60% Dog Morphs 80% Dog Morphs Prototypes Prototypes 100% Dog 100% Cat Category boundary Freedman, D.J., Riesenhuber, M., Poggio, T. and Miller, E.K. (2001) Science, 291:312-316 Freedman, D.J., Riesenhuber, M., Poggio, T. and Miller, E.K. (2002) J. Neurophysiology, 88:914-928. Freedman, D.J., Riesenhuber, M., Poggio, T. and Miller, E.K, (2003) J. Neuroscience, 23:5235-5246 . “Cats” Category boundary “Dogs” Delayed match to category task . . . . RELEASE (Category Match) (Match) Fixation 500 ms. Sample 600 ms. Delay . 1000 ms. Test object is a “match” if it the same category (cat or dog) as the sample Test (Nonmatch) HOLD (Category Non-match) A “Dog Neuron” in the Prefrontal Cortex Fixation Sample Delay Test 13 Firing Rate (Hz) P > 0.1 10 100% Dog 80:20 Dog:Cat 60:40 Dog:Cat Cats vs. Dogs P < 0.01 7 4 1 -500 100% Cat 80:20 Cat:Dog 60:40 Cat:Dog 0 500 1000 1500 2000 Time from sample stimulus onset (ms) P > 0.1 To test the contribution of experience, we moved the category boundaries and retrained a monkey 80% Cat Morphs 60% Cat Morphs 60% Dog Morphs 80% Dog Morphs Prototypes Prototypes 100% Dog 100% Cat Category boundary To test the contribution of experience, we moved the category boundaries and retrained a monkey Old, now-irrelevant, boundary New, now-relevant, boundary PFC neural activity shifted to reflect the new boundaries and no longer reflected the old boundaries Old, now-irrelevant, boundary New, now-relevant, boundary Freedman, D.J., Riesenhuber, M., Poggio, T. and Miller, E.K. (2001) Science, 291:312-316 Freedman, D.J., Riesenhuber, M., Poggio, T. and Miller, E.K. (2002) J. Neurophysiology, 88:914-928 Posterior Parietal Cortex (PPC) IPS Cs SPL As Lateral Prefrontal Cortex (LPFC) IPL Parietal Pathway “where” Ps Ls Temporal Pathway “what” Sts PIT CIT AIT ??? Inferior Temporal Cortex (IT) Freedman, D.J., Riesenhuber, M., Poggio, T. and Miller, E.K, (2003) J. Neuroscience, 23:5235-5246 . Category Effects in the Prefrontal versus Inferior Temporal Cortex Activity to individual stimuli along the 9 morph lines that crossed the category boundary C2 C3 C1 “cats” Cats Dogs C1 D1 C1 D2 C1 D3 C2 D1 C2 D2 D3 D1 D3 D2 Cats D1 D2 D3 D1 D2 D3 D1 D2 D3 C3 D2 C3 D3 C3 C2 C3 D1 Dogs C1 C1 C1 C2 C2 C2 C3 C3 category boundary “dogs” PFC ITC C1 C1 C1 C2 D1 D2 D3 D1 C1 C1 C1 C2 D1 D2 D3 D1 C2 C2 C3 C3 D2 D3 D1 D2 C2 C2 C3 C3 D2 D3 D1 D2 C3 D3 C3 D3 0 0.5 Normalized firing rate 1.0 Category Effects were Stronger in the PFC than ITC: Population 50 PFC ITC 60 Number of Neurons Number of Neurons 70 50 40 30 20 40 30 20 10 10 0 -0.4 -0.2 0 0.2 0.4 0.6 Category Index Value Category 0 -0.4 -0.2 0 0.2 0.4 index valuesCategory Index Value Stronger category effects Index of the difference in activity to stimuli from different, relative to same, category 0.6 Quantity (numerosity) Andreas Nieder David Freedman Earl Miller www.millerlab.org Behavioral protocol: delayed-match-to-number task Fixation 500 ms Test 1200 ms Match Sample 800 ms Release Delay 1000 ms Numbers 1 – 5 were used Nonmatch Tim e Hold Preventing the monkey from memorizing visual patterns: 1. Position and size of dots shuffled pseudo-randomly. 2. Each numerosity tested with 100 different images per session. 3. All images newly generated after a session. 4. Sample and test images never identical. A. Nieder, D.J. Freedman, and E.K. Miller (2002) Science, 297:1708-1711. Trained Monkeys instantly generalized across the control stimulus sets. Standard stimulus Equal area Equal circumference Low density High density Variable features ‘Shape’ Linear Standard stimulus Sample Delay Equal area Spike rate (Hz) 1 2 3 4 5 standard equal area Spike rate (Hz) 30 20 Average sample interval activity Time (ms) 10 1 2 3 Numerosity 4 5 Standard stimulus Sample Delay Variable features Spike rate (Hz) 1 2 3 4 5 20 standard Spike rate (Hz) variable features 15 10 Time (ms) Average delay interval activity 5 1 2 3 Numerosity 4 5 Low density Sample Delay High density 1 2 3 4 5 Spike rate (Hz) 10 high density low density Spike rate (Hz) 8 6 4 2 Average sample interval activity Time (ms) 0 1 2 3 Numerosity 4 5 Characteristics of Numerosity 1. Preservation of numerical order – numbers are not isolated categories. 2. Numerical Distance Effect – discrimination between numbers improve with increasing distance between them (e.g., 3 and 4 are harder to discriminate than 3 and 7) 100 Normalized response (%) Normalized response (%) PFC neurons show tuning curves for number. 75 50 25 0 0 2 4 6 8 10 12 Preferred numerosity 100 75 50 25 0 0 2 4 6 8 10 12 Preferred numerosity Characteristics of Numerosity 1. Preservation of numerical order – numbers are not isolated categories. 2. Numerical Distance Effect – discrimination between numbers improve with increasing distance between them. 3. Numerical Magnitude Effect – discrimination between numbers of equal numerical distance is increasingly difficult as their size increases (e.g., 1 and 2 are easier to tell apart than 5 and 6). Numerical Magnitude Effect Average width of population tuning curves Bandwidth of tuning curves Average population tuning curve for each number Normalized response (%) 100 75 50 25 0 1 2 3 4 Numerosity 5 3.0 2.5 2.0 1.5 1.0 05 1 2 3 4 Numerosity Neural tuning becomes increasing imprecise with increasing number. Therefore, smaller size numbers are easier to discriminate. 5 Scaling of numerical representations Linear-coding hypothesis Non-linear compression hypothesis Amplitude •symmetric distributions on linear scale (centered on numbers) •wider distributions in proportion to increasing quantities •symmetric distributions on a logarithmically compressed scale •standard deviations of distributions constant across quantities 1.0 1.0 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0 0.0 2 4 6 8 10 12 14 16 2 18 20 N um berof item s(logscale) N um berof item s(linearscale) Amplitude asymmetric on log scale asymmetric on linear scale 1.0 1.0 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0.0 2 10 N um berofitem s(logscale) 0.0 0 2 4 6 8 10 12 14 16 18 20 N um berofitem s(linearscale) Non-linear scaling of behavioral data 80 60 40 monkey T monkey P average 20 0 1 2 3 4 5 6 7 8 9 10 * 11 2 Number of items (linear scale) Logarithmic scaling 100 Performance (% correct) 1.00 Goodness-of-fit (r ) Performance (% correct) 100 80 0.95 0.90 60 linear pow(1/2)pow(1/3) Scale 40 monkey T monkey P average 20 0 1 5 Number of items (log scale) 10 log Non-linear scaling of neural data 80 60 40 1.00 20 0.95 2 Goodness-of-fit (r ) Normalized activity (%) 100 0 1 2 3 4 5 Number of items (linear scale) Logarithmic scaling 0.90 0.85 0.80 0.75 linear pow(1/2)pow(1/3) Scale 100 Normalized activity (%) * 80 60 40 20 0 1 5 Number of items (log scale) log Scaling of numerical representations Linear-coding hypothesis Non-linear compression hypothesis Amplitude •symmetric distributions on linear scale (centered on numbers) •wider distributions in proportion to increasing quantities •symmetric distributions on a logarithmically compressed scale •standard deviations of distributions constant across quantities 1.0 1.0 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0 0.0 2 4 6 8 10 12 14 16 2 18 20 N um berof item s(logscale) N um berof item s(linearscale) Amplitude asymmetric on log scale asymmetric on linear scale 1.0 1.0 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0.0 2 10 N um berofitem s(logscale) 0.0 0 2 4 6 8 10 12 14 16 18 20 N um berofitem s(linearscale) Scaling of numerical representations Linear-coding hypothesis •symmetric distributions on linear scale (centered on numbers) •wider distributions in proportion to increasing quantities Non-linear compression hypothesis •symmetric distributions on a logarithmically compressed scale •standard deviations of distributions constant across quantities 1.0 0.8 0.6 0.4 0.2 0.0 2 20 N um berof item s(logscale) asymmetric on log scale asymmetric on linear scale 1.0 0.8 0.6 0.4 0.2 0.0 0 2 4 6 8 10 12 14 16 18 20 N um berofitem s(linearscale) Number-encoding neurons A. Nieder and E.K. Miller (in preparation) Posterior Parietal Cortex (PPC) A. Nieder, D.J. Freedman, and E.K. Miller (2002) Science, 297:1708-1711. Cs IPS SPL As Lateral Prefrontal Cortex (LPFC) IPL Parietal Pathway “where” Ps Ls Temporal Pathway “what” Sts PIT CIT AIT Inferior Temporal Cortex (IT) A. Nieder and E.K. Miller (in preparation) Abstract number-encoding neurons Parietal Cortex N = 404 12 16 % 0% 3% 4% 7% 30 % Cs 5 Lateral Prefrontal Cortex N = 352 5ip As VIP 7A Ps Ls 4% Sts AIT Inferior Temporal Cortex N = 77 Inferior Temporal Cortex Standard stimulus Low density Equal circumference 25 High density 35 equal circumference low density high density Spike rate (Hz) Spike rate (Hz) standard 20 15 10 30 25 20 1 2 3 Numerosity 4 5 1 2 3 Numerosity 4 5 Behavior-guiding Rules Jonathan Wallis Wael Asaad Kathleen Anderson Gregor Rainer Earl Miller www.millerlab.org What is a rule? Rules are conditional associations that describe the logic of a goal-directed task. CONCRETE Asaad, Rainer, & Miller (1998) (also see Fuster, Watanabe, Wise et al) ABSTRACT Asaad, Rainer, & Miller (2000) task context Wallis et al (2001) Release Match Rule (same) Hold Sample Test Wallis, J.D., Anderson, K.C., and Miller, E.K. (2001) Nature, 411:953-956 Release Hold Sample Test Hold Nonmatch Rule (different) Release Sample Test Wallis, J.D., Anderson, K.C., and Miller, E.K. (2001) Nature, 411:953-956 Release Match Rule (same) Hold Sample Test Hold Nonmatch Rule (different) Release Sample Test The rules were made abstract by training monkeys until they could perform the task with novel stimuli Sample + Cue + juice OR + low tone Match + no juice OR + high tone Nonmatch Match Neuron Cue Wallis, J.D., Anderson, K.C., and Miller, E.K. (2001) Nature, 411:953-956 Rule Representation in Other Cortical Areas PMC PFC ITC Timecourse of Rule-Selectivity Across the PFC Population: Sliding ROC Analysis ROC Value PFC Note: ROC Values are sorted by each time bin independently Wallis, J.D. and Miller, E.K. (in press) J. Neurophysiology Rule Representation in Other Cortical Areas PMC PFC ITC Abstract Rule-Encoding in Three Cortical Areas PFC Wallis, J.D. and Miller, E.K. (in press) J. Neurophysiology Abstract Rule-Encoding in Three Cortical Areas PFC Wallis, J.D. and Miller, E.K. (in press) J. Neurophysiology ITC Wallis and Miller, in preparation Abstract Rule-Encoding in Three Cortical Areas PMC PFC Wallis, J.D. and Miller, E.K. (in press) J. Neurophysiology ITC Wallis and Miller, in preparation Wallis, J.D. and Miller, E.K. (in press) J. Neurophysiology Abstract Rule-Encoding was Stronger and Appeared Earlier in the PMC than PFC ROC Value PFC PMC Wallis and Miller, in press, J. Neurophysiol. Number of neurons Median = 410 Median = 310 PMC PFC Latency for rule-selectivity (msec) Abstract Rule-Encoding in Three Cortical Areas PMC PFC Wallis, J.D. and Miller, E.K. (in press) J. Neurophysiology ITC Wallis and Miller, in preparation Wallis, J.D. and Miller, E.K. (in press) J. Neurophysiology CONCLUSIONS: 1. Goal-related information, including the categories and concepts needed for executive control, is represented in the PFC while irrelevant details are largely discarded. 2. Neural representations of categories and concepts are stronger and more explicit in the PFC than in cortical areas that provide the PFC with visual input (“cats and dogs”, numbers). Highly familiar rules may be more strongly encoded in the PMC than PFC. 3. This ability of the PFC and related areas to convey categories, concepts and rules may reflect their role in acquiring and representing the formal demands of tasks, the internal models of situations and courses of action that provide a foundation for complex, intelligent behavior. A Model of PFC function: Miller, E.K. (2000) The prefrontal cortex and cognitive control. Nature Reviews Neuroscience, 1:59-65 Miller, E.K. and Cohen, J.D. (2001) An integrative theory of prefrontal cortex function. Annual Review of Neuroscience, 24:167-202 For reprints etc: www.millerlab.org The PF cortex and cognitive control Phone rings Answer Don’t answer Active Inactive The PF cortex and cognitive control At home Guest Phone rings Answer Don’t answer Active Inactive The PF cortex and cognitive control PF cortex At home Guest Phone rings Answer Don’t answer Active Inactive The PF cortex and cognitive control PF cortex At home Reward signals (VTA neurons?) Guest Phone rings Answer Don’t answer Active Inactive The PF cortex and cognitive control PF cortex At home Guest Phone rings Answer Don’t answer Active Inactive The PF cortex and cognitive control PF cortex At home Reward signals (VTA neurons?) Guest Phone rings Answer Don’t answer Active Inactive The PF cortex and cognitive control PF cortex At home Guest Phone rings Answer Don’t answer Active Inactive The PF cortex and cognitive control PF cortex At home Guest Phone rings Answer Don’t answer Active Inactive The PF cortex and cognitive control PF cortex At home Guest Phone rings Answer Don’t answer Active Inactive The PF cortex and cognitive control PF cortex At home Guest Phone rings Answer Don’t answer Active Inactive The PF cortex and cognitive control PF cortex At home Guest Phone rings Answer Don’t answer Active Inactive The PF cortex and cognitive control PF cortex At home Guest Phone rings Answer Don’t answer Active Inactive The prefrontal cortex may be like a switch operator in a system of railroad tracks: Its integrative anatomy allows it to rapidly acquire a “map” that specifies which pattern of “tracks” (neural pathways) are needed to solve a given task. PF cortex The prefrontal cortex may be like a switch operator in a system of railroad tracks: Its integrative anatomy allows it to rapidly acquire a “map” that specifies which pattern of “tracks” (neural pathways) are needed to solve a given task. PF cortex The PF cortex actively maintains this pattern during task performance, allowing feedback signals to bias the flow of activity in other brain areas along task-appropriate pathways. GOAL-DIRECTION FLEXIBILITY Miller Lab @ MIT (www.millerlab.org) Categories: Other Miller Lab members: David Freedman Max Riesenhuber (Poggio lab) Tomaso Poggio Tim Buschman Mark Histed Christopher Irving Cindy Kiddoo Kristin Maccully Michelle Machon Anitha Pasupathy Jefferson Roy Melissa Warden Numbers: Andreas Nieder David Freedman Rules: Jonathan Wallis Wael Asaad Kathy Anderson Gregor Rainer