Research Briefing: Dynamic population coding for flexible cognition

Dynamic population coding in prefrontal cortex

Our environment is in constant flux. At any given moment there could be a shift in scenario that demands an equally rapid shift in how we interpret the world around us. For example, the meaning of a simple traffic light critically depends on whether you are driving to work or travelling on foot. Our brains must constantly adapt to accommodate an enormous range of such possible scenarios - in this study, we applied new analysis tools to explore how patterns of brain activity change for different task contexts, allowing for flexible cognitive processing (in Stokes et al., 2013, Neuron; see also Comment by Miller and Fusi in the same issue).

Prefontal Cortex

Adapted from Fig 1

We focused our investigation on an area in the frontal lobe known as lateral prefrontal cortex. This brain area has long been implicated in flexible cognitive processing. Damage to prefrontal cortex is classically associated with reduced cognitive flexibility (Luria, 1966) as part of a more general dysexecutive syndrome. In studies using functional magnetic resonance imaging (fMRI), lateral frontal cortex is also usually more active when participants perform tasks that demand cognitive flexibility (Wager et al., 2004). It it widely believed that prefrontal cortex is especially important for representing information about our environment and task goals in mind for guiding flexible behaviour (Baddeley, 2003; Miller, 2000).

Dynamic coding population coding

Dynamic trajectory through state-space

In this study, we observe a highly dynamic process underlying flexible cognitive processing using a statistical approach that allows us to decode the patterns of population-level activity in prefrontal cortex at high temporal resolution. During a task that requires a different stimulus-response mapping according to trial-by-trial instruction cues (see Fig 1), we found that the pattern of activity rapidly changes during processing of the instructive cue stimulus. After this complex cascade through activity state-space (for more info, see Stokes, 2011), overall activity levels return to baseline for the remainder of a delay period spanning the instruction cue and a possible target stimulus.

Adapted from Fig 5

However, the effect of the cue response lingers on. Subsequent stimuli elicit a population response that critically depends on the previous cue identity. In other words, the dynamic population response triggered by the cue stimulus shifts the response profile of the network of prefrontal cells. This shift in tuning profile allows us to decode the current task-rule (i.e., cue indentify) based on a simple driving stimulus (i.e., neutral stimuli, see Fig 5).

Adapted from Fig 6

More importantly, the shift in the network response profile could also underlie task-dependent target processing (i.e., choice stimuli, see Fig 6). The population response to potential target stimuli rapidly evolved from a stimulus-specific coding scheme, to a more abstract code that distinguishes only between different target and non-target items. This dynamic tuning property is ideal for flexible cognition (Duncan, 2001).

Putative mechanism: flexible connectivity

The flow of brain activity critically depends on the pattern of connections between neurons. Contrary to intuition, these connections are always changing. The pattern of connections that make up the very essence of personal experience is constantly adjusting and adapting to the myriad changes experienced throughout life.

Synaptic Plasticity [wiki commons]

Extensive research focuses on long-term structural changes in connectivity through synaptic plasticity, however the rapid changes we experience from moment-to-moment requires a more flexible kind of memory that can represent the transient features of a given scenario. This kind of flexible "online" memory is typically referred to as ‘working memory’.

It has long been assumed that working memory is maintained by keeping a specific thought in mind, like a static snapshot of a visual image or an abstract goal such as ‘turn left at the next set of lights’. However, more recent evidence suggests that working memory can also be stored by laying down specific, but temporary neural pathways (e.g., Mongillo, Barak & Tsodyks, 2008). Neural pathways are formed by synaptic connections. In a comprehensive review of the literature on short-term synaptic plasticity, Zucker (1989) writes: “Chemical synapses are not static. Postsynaptic potentials wax and wane, depending on the recent history of presynaptic activity”. Short-term plasticity could provide a key mechanisms for flexible connectivity that is necessary for rapid, but temporary changes in network behaviour.

This new idea allows for a more dynamic theory of brain function, which is more consistent with the everyday experience of continuous thought processes that seem to evolve through time, rather than persist as a static representation. We suggest that short-term plasticity could help explain our data:

Adapted from Fig 7

The initial instruction cue stimulus establishes a specific (but temporary) connectivity state during the most active phase of the response. This would explain why the pattern constantly changes - if the synapse are constantly changing, then even identical input to the system will result in constantly shifting output patterns (Buonomano and Maass, 2009). This temporary shift in the response sensitivity of the prefrontal network allows the identity of previous input to be decoded by the patterned response to subsequent input, consistent with the silent memory hypothesis. Finally, dynamic changes in connectivity could also be used to rapidly shift the tuning profile of the prefrontal network to accommodate changes in what specific stimuli mean for behaviour (see Fig. 7).

Broader implications

Brain activity is inherently non-stationary - the continuity/stability of cognitive states are unlikely to depend on static activity states, but rather rapid changes in temporary connectivity patterns. This research also raises the intriguing possibility that cognitive capacity limits are not so much constrained by the sheer amount of information that we can keep in mind, but rather how we can put that information to use. Further research in our lab will explore these exciting possibilities.


Reference:

Stokes, Kusunoki, Sigala, Nili, Gaffan and Duncan (2013). Dynamic Coding for Cognitive Control in Prefrontal Cortex. Neuron, 78, 364-375 [here]

Also see coverage: Miller Lab (MIT), Neuron Preview


Other literature cited:

Baddeley, A. (2003). Working memory: looking back and looking forward. Nat. Rev. Neurosci. 4, 829–839. [here]

Buonomano, D.V., and Maass, W. (2009). State-dependent computations: spatiotemporal processing in cortical networks. Nat. Rev. Neurosci. 10, 113–125. [here]

Luria, A.R. (1966). Higher Cortical Functions in Man (New York: Basic Books).

Miller, E.K. (2000). The prefrontal cortex and cognitive control. Nat. Rev. Neurosci. 1, 59–65. [here]

Mongillo, G., Barak, O., and Tsodyks, M. (2008). Synaptic theory of working memory. Science 319, 1543–1546. [here]

Wager, T.D., Jonides, J., and Reading, S. (2004). Neuroimaging studies of shifting attention: a meta-analysis. Neuroimage 22, 1679–1693. [here]

Zucker (1989) Short-term synaptic plasticity. Ann. Rev. Neurosci, 12: 13-31 [here]

Research Grant to Explore Fluid Intelligence

Thank you to the British Academy for awarding John Duncan and myself research funds to test a key hypothesis in the cognitive neuroscience of human performance: is prefrontal cortex necessary for fluid intelligence?

We will use non-invasive brain stimulation (transcranial magnetic stimulation: TMS) to temporarily ‘deactivate’ the prefrontal cortex, and then measure the consequences for performance on standard tests of fluid intelligence. It is a relatively simple experimental design, but if done correctly, the results should provide important and novel insights into the brain mechanisms underlying one of the most important human faculties: flexible reasoning and problem solving.

My co-investigator, John Duncan, gained his reputation in the cognitive neuroscience of intelligence with his seminal brain imaging study published in Science. This research demonstrated that when people perform tasks that tax fluid intelligence, neural activity increases in the prefrontal cortex relative to control tasks that require less fluid intelligence.

This result suggests that the prefrontal cortex is involved in fluid intelligence - but of course, as every undergraduate in psychology/cognitive neuroscience should be able to tell you, brain imaging alone cannot tell us whether the activated brain area is in fact necessary for performing the task.

So, to verify the causal role of the prefrontal cortex, Duncan and colleagues next examined stroke patients (published in PNAS). The logic here is simple: does damage to the prefrontal cortex reduce fluid intelligence? But the methodology is not so simple. Of particular importance, how can you tell  whether a patient has low IQ because of the brain damage, or  whether they were always a low IQ individual?

Duncan's team tackled this problem by estimated pre-damage fluid intelligence from scores on other tests that measure so-called crystallised intelligence (e.g., vocabulary and general knowledge). Critically, crystallised intelligence reflects the life long achievements that depend on fluid intelligence during acquisition, and therefore can be used to approximate pre-damage fluid intelligence. If the prefrontal cortex is especially important for fluid intelligence, then damage should result in a disparity between fluid and crystallised intelligence. Indeed, this is what they found. 

As developed in his popular science book, "How Intelligence Happens", Duncan suggests that the prefrontal cortex is essential for flexible structured cognitive processing, a key ingredient to fluid intelligence. If this theory is correct, then temporary deactivation of the prefrontal cortex should impair fluid intelligence. If not, then we need to rethink this working hypothesis. 

What will these results tell us? Are we just heading back to 19th Century phrenology – associating discrete brain areas with complex high order human traits that are more like sociocultural inventions than principled neurocognitive constructs? Do we then plan to localise creativity here, insight there, and perhaps a little bit of moral judgment over here? 

Of course, I don't think this is modern day phrenology. Rather, I would argue that this research could provide key insights into the fundamental cognitive neuroscience of this important brain area. From a theoretical perspective, we can attempt to decompose the underlying processes for fluid intelligence, and relate these to the neurophysiological principles of prefrontal function. Intelligence is not mystical or intractable. It is a specific cognitive process that we can measure, and must have a neurological basis that is an important target for cognitive neuroscience.

However, we must also recognise that we have to be careful how these results could be interpreted. Intelligence is a particularly sensitive area. The very concept of fluid intelligence often takes on more than it should - a reflection of the fundamental worth or even moral character of the individual.

Obviously there is some danger in reducing one of the most important cognitive mechanisms to a single number (e.g., intelligence quotient: IQ), which we can then compare between individuals and against groups. It is a dangerous business that can be exploited for any number of nefarious agendas. For example, we can try to confirm our own racial or sexist prejudices, conjuring up a biological, and therefore 'scientific' excuse for beliefs that are motivated by simple bigotry (recall the recent Watson controversy?). Conversely, on the other side, the same logic could be used to pursue an equality agenda. This could also be a dangerous path to follow - what if we are not all equal in ability? I see no a priori reason that there should not be group differences in any measure, including IQ. It is simply an empirical issue, and therefore a risky business to stake our sense of equality on equal ability. 

IQ is certainly a loaded concept. Recently, I was speaking with a mathematician and historian about an advert they saw for a brain imaging study comparing IQ between academics from the sciences and humanities. The historian was intrigued, and eager to participate, whereas the mathematician was much more reluctant. I guess the risk of a lower-than-hoped-for score is quite disconcerting when your very livelihood depends on an almost mythical concept of pure intelligence, or better still - genius.

An anecdote comes to mind of a researcher who was to be the first subject in an fMRI study of IQ conducted by his colleagues. Being scanned can sometimes make people nervous the first time, but this was a seasoned neuroscientist, no stranger to the confined and uncomfortable space of an MRI. Rather, what made this no ordinary scanning experience was the fact that his respected colleagues were watching from the control room, monitoring his responses to the IQ task. Enough to make any academic uncomfortable!

This kind of awkwardness raises an important practical issue for us. Like many cognitive neuroscientists, I often rely on friends and colleagues to participate in my experiments, especially students, academic visitors, post-doctoral researchers. Obviously, one could easily imagine some tension arising in a lab that has tested everyone’s IQ. This could be particularly worrying for the more senior amongst us, as fluid intelligence is negatively correlated with age. We would not want to upset the natural order of the academic hierarchy!

Anyway, I will keep you posted how we get on with the project.