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

Basic sensory and motor functions Sensory Motor 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 Learning and memory (Hippocampus, basal ganglia, etc.) Memories, habits and skills Consolidation (long-term storage)

Sensory Motor Executive Functions goal-related information Learning and memory (Hippocampus, basal ganglia, etc.) Consolidation (long-term storage)

Sensory Motor Bottom-up Learning and memory (Hippocampus, basal ganglia, etc.) Executive Functions goal-related information Top-down Selection (flexibility) Consolidation (long-term storage)

Sensory Motor Bottom-up Learning and memory (Hippocampus, basal ganglia, etc.) Executive Functions goal-related information Top-down Consolidation (long-term storage) Selection (flexibility)

Sensory Motor Bottom-up Learning and memory (Hippocampus, basal ganglia, etc.) Executive Functions goal-related information Top-down Consolidation (long-term storage) Selection (flexibility)

Sensory Motor Bottom-up Learning and memory (Hippocampus, basal ganglia, etc.) Executive Functions goal-related information Top-down Selection (flexibility) Consolidation (long-term storage)

Sensory Motor Bottom-up Learning and memory (Hippocampus, basal ganglia, etc.) Executive Functions goal-related information Top-down Consolidation (long-term storage) Selection (flexibility)

Sensory Motor Bottom-up Learning and memory (Hippocampus, basal ganglia, etc.) Executive Functions goal-related information Top-down Consolidation (long-term storage) Selection (flexibility)

Sensory Motor Bottom-up Learning and memory (Hippocampus, basal ganglia, etc.) Executive Functions goal-related information Top-down Selection (flexibility) Consolidation (long-term storage)

Sensory Motor Bottom-up Learning and memory (Hippocampus, basal ganglia, etc.) Executive Functions goal-related information Top-down Consolidation (long-term storage) Selection (flexibility)

Sensory Motor Learning and memory (Hippocampus, basal ganglia, etc.) Executive Functions goal-related information Top-down Consolidation (long-term storage) Selection (flexibility)

Train monkeys on tasks designed to isolate cognitive operations related to executive control. Record from groups of single neurons while monkeys perform those tasks. Our Methods:

Sensory Motor Bottom-up Learning and memory (Hippocampus, basal ganglia, etc.) Executive Functions goal-related information Top-down Consolidation (long-term storage) Selection (flexibility)

Perceptual Categories David Freedman Maximillian Riesenhuber Tomaso Poggio Earl Miller

Category boundary Prototypes 100% Cat 80% Cat Morphs 60% Cat Morphs 60% Dog Morphs 80% Dog Morphs Prototypes 100% Dog Perceptual Categorization: “Cats” Versus “Dogs” Freedman, D.J., Riesenhuber, M., Poggio, T. and Miller, E.K. (2001) Science, 291: Freedman, D.J., Riesenhuber, M., Poggio, T. and Miller, E.K. (2002) J. Neurophysiology, 88: Freedman, D.J., Riesenhuber, M., Poggio, T. and Miller, E.K, (2003) J. Neuroscience, 23:

“Cats” “Dogs” Category boundary

..... Fixation Sample Delay Test (Nonmatch) (Match) 600 ms ms. 500 ms. Delayed match to category task Test object is a “match” if it the same category (cat or dog) as the sample RELEASE (Category Match) HOLD (Category Non-match)

A “Dog Neuron” in the Prefrontal Cortex Time from sample stimulus onset (ms) Firing Rate (Hz) 100% Dog 80:20 Dog:Cat 60:40 Dog:Cat Test Sample Delay 100% Cat Fixation 60:40 Cat:Dog 80:20 Cat:Dog P > 0.1 Cats vs. Dogs P < 0.01

To test the contribution of experience, we moved the category boundaries and retrained a monkey Category boundary Prototypes 100% Cat 80% Cat Morphs 60% Cat Morphs 60% Dog Morphs 80% Dog Morphs Prototypes 100% Dog

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: Freedman, D.J., Riesenhuber, M., Poggio, T. and Miller, E.K. (2002) J. Neurophysiology, 88: Freedman, D.J., Riesenhuber, M., Poggio, T. and Miller, E.K, (2003) J. Neuroscience, 23:

Category Effects in the Prefrontal versus Inferior Temporal Cortex “cats” “dogs” category boundary C1 C2 C3 D2 D3D1 Activity to individual stimuli along the 9 morph lines that crossed the category boundary PFC C1 C2 C3 D1 D2 D3 D1 D2 D3 D2 D3 D1 C1 C2 C3 D1 D2 D3 D1 D2 D3 D2 D3 D1 ITC Normalized firing rate Cats Dogs C1 C2 C3 D1 D2 D3 D1 D2 D3 D2 D3 D1

Category Effects were Stronger in the PFC than ITC: Population Index of the difference in activity to stimuli from different, relative to same, category ITC PFC Stronger category effects Category index values

Quantity (numerosity) Andreas Nieder David Freedman Earl Miller

Behavioral protocol: delayed-match-to-number task 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: Numbers 1 – 5 were used Release Hold

Standard stimulus Equal area Equal circumference Variable features ‘Shape’ Linear Low density High density Trained Monkeys instantly generalized across the control stimulus sets.

Standard stimulus Equal area Sample Delay Average sample interval activity

Standard stimulus Variable features Sample Delay Average delay interval activity

Low density High density Sample Delay Average sample interval activity

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) PFC neurons show tuning curves for number Preferred numerosity N o r m a l i z e d r e s p o n s e ( % ) Preferred numerosity N o r m a l i z e d r e s p o n s e ( % )

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 Bandwidth of tuning curves Average population tuning curve for each number Neural tuning becomes increasing imprecise with increasing number. Therefore, smaller size numbers are easier to discriminate. Average width of population tuning curves Numerosity N o r m a l i z e d r e s p o n s e ( % )

Scaling of numerical representations Linear-coding hypothesisNon-linear compression hypothesis 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 asymmetric on log scaleasymmetric on linear scale

Non-linear scaling of behavioral data Logarithmic scaling

Non-linear scaling of neural data

Scaling of numerical representations Linear-coding hypothesisNon-linear compression hypothesis 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 asymmetric on log scaleasymmetric on linear scale

Scaling of numerical representations Linear-coding hypothesisNon-linear compression hypothesis 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 asymmetric on log scaleasymmetric on linear scale

Number-encoding neurons A. Nieder, D.J. Freedman, and E.K. Miller (2002) Science, 297: A. Nieder and E.K. Miller (in preparation)

Parietal Cortex N = 404 Abstract number-encoding neurons Lateral Prefrontal Cortex N = 352 Inferior Temporal Cortex N = 77 16

Low density Inferior Temporal Cortex High densityEqual circumference Standard stimulus

Behavior-guiding Rules Jonathan Wallis Wael Asaad Kathleen Anderson Gregor Rainer Earl Miller

CONCRETEABSTRACT What is a rule? Rules are conditional associations that describe the logic of a goal-directed task. Asaad, Rainer, & Miller (1998) (also see Fuster, Watanabe, Wise et al) Asaad, Rainer, & Miller (2000) task context Wallis et al (2001)

Release Hold Match Rule (same) SampleTest Wallis, J.D., Anderson, K.C., and Miller, E.K. (2001) Nature, 411:

Sample Nonmatch Rule (different) Test Wallis, J.D., Anderson, K.C., and Miller, E.K. (2001) Nature, 411: Release Hold Release Sample Test

Sample Test Release Hold The rules were made abstract by training monkeys until they could perform the task with novel stimuli Match Rule (same) Nonmatch Rule (different) Hold Release

+ juice + no juice Match + low tone + high tone OR Sample + Cue Nonmatch

Match Neuron Cue

Wallis, J.D., Anderson, K.C., and Miller, E.K. (2001) Nature, 411:

Rule Representation in Other Cortical Areas PFC ITC PMC

SAMPLE TEST ROC Value Number of neurons (All recorded neurons) Time from sample onset (ms) PFC Timecourse of Rule-Selectivity Across the PFC Population: Sliding ROC Analysis 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 PFC ITC PMC

PFC Abstract Rule-Encoding in Three Cortical Areas Wallis, J.D. and Miller, E.K. (in press) J. Neurophysiology

PFC ITC Abstract Rule-Encoding in Three Cortical Areas Wallis and Miller, in preparation Wallis, J.D. and Miller, E.K. (in press) J. Neurophysiology

Abstract Rule-Encoding in Three Cortical Areas PFC ITC PMC 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 SAMPLE TEST PMC SAMPLE TEST ROC Value Number of neurons (All recorded neurons) Time from sample onset (ms) PFC Latency for rule-selectivity (msec) Number of neurons Median = 410Median = 310 PFC PMC Wallis and Miller, in press, J. Neurophysiol.

Abstract Rule-Encoding in Three Cortical Areas PFC ITC PMC Wallis and Miller, in preparation Wallis, J.D. and Miller, E.K. (in press) J. Neurophysiology

1. Goal-related information, including the categories and concepts needed for executive control, is represented in the PFC while irrelevant details are largely discarded. 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: For reprints etc: 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. CONCLUSIONS:

Active Inactive The PF cortex and cognitive control Phone rings Answer Don’t answer

Active Inactive The PF cortex and cognitive control Phone rings Answer Don’t answer At home Guest

Active Inactive The PF cortex and cognitive control Phone rings Answer Don’t answer At home Guest PF cortex

Active Inactive The PF cortex and cognitive control Phone rings Answer Don’t answer At home Guest PF cortex Reward signals (VTA neurons?)

Active Inactive The PF cortex and cognitive control Phone rings Answer Don’t answer At home Guest PF cortex

Active Inactive The PF cortex and cognitive control Phone rings Answer Don’t answer At home Guest PF cortex Reward signals (VTA neurons?)

Active Inactive The PF cortex and cognitive control Phone rings Answer Don’t answer At home Guest PF cortex

Active Inactive The PF cortex and cognitive control Answer Don’t answer PF cortex Phone rings Guest At home

Active Inactive The PF cortex and cognitive control Answer Don’t answer PF cortex At home Guest Phone rings

Active Inactive The PF cortex and cognitive control Answer Don’t answer PF cortex At home Guest Phone rings

Active Inactive The PF cortex and cognitive control Answer Don’t answer PF cortex Phone rings Guest At home

Active Inactive The PF cortex and cognitive control Answer Don’t answer PF cortex At home Guest Phone rings

PF cortex 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. The prefrontal cortex may be like a switch operator in a system of railroad tracks:

PF cortex 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. 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. The prefrontal cortex may be like a switch operator in a system of railroad tracks: GOAL-DIRECTION FLEXIBILITY

Categories: David Freedman Max Riesenhuber (Poggio lab) Tomaso Poggio Numbers: Andreas Nieder David Freedman Rules: Jonathan Wallis Wael Asaad Kathy Anderson Gregor Rainer Other Miller Lab members: Tim Buschman Mark Histed Christopher Irving Cindy Kiddoo Kristin Maccully Michelle Machon Anitha Pasupathy Jefferson Roy Melissa Warden Miller MIT (