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Summary1. Cognitive flexibility requires dynamic routing of
information dependent on the current context and goals.
2. Synchronous sub-networks within PFC support rules. These networks organized task-selective neural activity and correlate with behavior. This ability to dynamically bring together task-relevant neural activity may be key to behavioral flexibility.
3. Sub-networks supporting the dominant rule (orientation) show increased alpha synchrony during the competing rule (color), possibly reflecting suppression of the dominant rule.
4. Together, these results suggest a “push-pull” mechanism underlying cognitive competition: beta selects the relevant rule while alpha suppresses alternative rules.
The strength of synchrony in rule-selective PFC sub-networks was correlated with behavioral performance. Furthermore, trials with quicker responses showed earlier onset of alpha and beta coherence than trials in which the animals responded slowly (alpha: 166 ms and 104 ms before stimulus onset for fast and slow trials, beta: 46 ms before and 3 ms after stimulus onset). Moreover, the alpha coherence ended later in trials where the animals’ response was slower (12 ms before stimulus onset on fast trials, 51 ms after on slow trials).
The correlation between behavioral performance and the degree of synchrony in the rule-selective sub-networks supports our hypothesis that the sub-networks play a role in cognitive flexibility.
Sub-Network Synchrony Correlates with Reaction Time
Time since Onset of Stimulus (ms)
Freq
uenc
y (H
z)
−300 −200 −100 0 100 200
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20
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Slow Reaction Times
Average A
bsolute z-Score
of Difference in C
oherence
0.5
1.0
1.5
2.0
2.5
−300 −200 −100 0 100 200
10
20
30
40
50 Fast Reaction Times
Freq
uenc
y (H
z)
Average A
bsolute z-Score
of Difference in C
oherence
0.5
1.0
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2.5
Time since Onset of Stimulus (ms)
Spatial distributions of sub-networksare heavily overlapping...
Beta Color Network
AS-inf
AS-sup
PS
Beta Orientation Network
AS-sup
PS
AS-inf
1 mm A/P
M/L
...but the topologicalstructure is not random
Beta Color Network
Beta Orientation Network
Sub-networks are Extracted from Overlapping Topographies
We hypothesize cognitive flexibility relies on the dynamic reorganization of information flow through the brain. Rule-specific synchronous sub-networks may be the mechanism by which such flexibility is achieved: synchrony acts to dynamically modulate the effective connectivity between task-relevant neurons.
We found support for this model: PFC local field potentials showed rule-selective changes in synchrony. Two distinct groups of PFC electrode-pairs were more strongly synchronized for either the color or orientation rule at beta-band frequencies (19-40 Hz), around the time of stimulus presentation (when the rule was being followed). These rule-selective synchronous groups of electrodes defined a ‘sub-network’ within PFC for each rule.
Interestingly, the orientation sub-networks (defined by stronger beta synchrony during orientation trials) were synchronized at ‘alpha’ (6-16 Hz) frequencies during color rule trials. Since the alpha band is associated with de-selection, it may reflect suppression of the dominant, orientation, ‘beta’ sub-network when following the color rule.
Left
Left
Right
Right
Red
Red
Blue
Blue
Stim
ulus
-Sel
ectiv
e N
euro
ns
Rule-Selective Neurons Response-Selective N
eurons
Color Rule Orientation Rule
Dynamic Sub-Networks within PFC Support Flexible Rules
Time since Onset of Stimulus (ms)
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-200 -100 0 100 200
Stronger for Orientation Rule (N=117)
-200 -100 0 100 200
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Stronger for Color Rule (N=90)
−3
−2
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3 Difference z-C
oherence Betw
eenC
olor and Orienation R
ulesM
ore Coherence
during Color R
uleM
ore Coherence
during Orient. R
ule
Time since Onset of Stimulus (ms)
Freq
uenc
y (H
z)
Synchrony Between Electrodes Changes with Rule
Test Stimulus Response Test Stimulus ResponseRule Cue Rule Cue
z−S
core
of P
hase
-lock
ing
Val
ue
Orientation Rule Trials
0
1
2
10 30 50 70 90Frequency (Hz)
**
**
Synchrony between Stimulus-Selective Neuronsand Rule-Selective Local Field Potentials was Task Specific
Color-PreferringLocal Field Potentials: Orientation-Preferring
0
1
2
10 30 50 70 90
****
*
*
*
Frequency (Hz)
Color Rule Trials
Dynamic Rule-Selective Sub-NetworksSynchronize Task-Relevant Information
Greater beta-band synchrony between stimulus-selective neurons and the orientation sub-network during the orientation rule.
Greater beta-band synchrony between stimulus-selective neurons and the color sub-network during the color rule.
Color Rule
Orientation Rule
Fixation
Rule Cue(86-496 ms) Stimulus Response
...CCCCCCOOOOO...
Switch Trial(Color to Orientation)
Repetition Trial(Orientation to Orientation)
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M
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M
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M
M
M
M
L LL
LR R
R
R
Color
Orienta
tion
80
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Color OrientationMonkey CC
Per
cent
Cor
rect
Color OrientationMonkey ISA
*SwitchRepetition
***** *** *** ***
*********
***
** .
Reac
tion
Tim
e (m
s)
Color to Orientation
# of Trials Since Switching Tasks
Orientation to Color
***
-1 0 1 2 3 4 5 6 7 8 9 10 -1 0 1 2 3 4 5 6 7 8 9 10
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Cued Task Switching in Non-Human Primates
No Switch CostSwitch Cost
Two monkeys were trained on a cued task switching paradigm. The task began with the presentation of a fixation spot at the center of the screen. After acquiring fixation, a cue bordering the screen indicated one of two rules was in effect – either discriminate color or orientation.
The monkeys continued to fixate for a brief, randomized, preparatory period until a test stimulus appeared at the center of the screen. Using the cued rule and the relevant feature of the test stimulus, the monkey made a left or right saccade to receive a juice reward.
70% of the trials were incongruent – meaning the test stimulus indicated different responses for the two rules. This ensured consistent application of the rule.
Each rule was repeated for at least 20 trials before a probabilistic switch to the other rule.
Monkeys show asymmetric switch cost
Monkeys were significantly slower when switching away from the orientation rule (into the color rule). In contrast, when switching into the orientation rule, no change in reaction time was observed.
This suggests that orientation was the dominant modality, similar to word-reading in the Stroop task.
The animals performed both rules well, with no significant difference in behavior between rules. Furthermore, switching rules did not impact the animals’ performance, suggesting they were able to effectively switch rules by slowing their response time.
The rule defined the stimulus-response mapping
IntroductionCognitive flexibility depends on our ability to quickly and easily switch between tasks. This, in turn, requires dynamic re-mapping of stimulus-response associations based on the current situation. We call these context-dependent, conditional stimulus-response mappings ‘rules.’
Functional imaging and lesion studies have identified the prefrontal cortex (PFC) as important to rule-based task switching in humans. Similarly, electrophysiology studies in non-human primates have found neural correlates for rules in PFC.
However, the neural infrastructure supporting cognitive flexibility is unknown. Here we present evidence that different rules are encoded by distinct dynamic, synchronous, sub-networks within prefrontal cortex and that these sub-networks organize the spiking activity of single neurons carrying task-relevant information. Such synchronous sub-networks may provide an ideal mechanism for flexibly associating task-relevant information.
Dynamic, synchronous, sub-networks in prefrontal cortex encode stimulus-response rules 599.10/DDD7
Center of Excellence for Learning in Education, Science, and Technology
Timothy J. Buschman1,2,3,5, Eric Denovellis4,5, Cinira Diogo1,3, Daniel Bullock4,5, Earl K. Miller1,3,5
1The Picower Inst. for Learning and Memory, 2The McGovern Inst. for Brain Research, 3Dept. of Brain and Cognitive Sci., Massachusetts Institute of Technology, Cambridge, MA4Cognitive & Neural Systems, Boston Univ., Boston, MA; 5Ctr. of Excellence for Learning in Education, Sci. and Technol., Boston, MA
