The key to understanding human thought may lie in deciphering how your brain's 86 billion neurons encode reality.
Imagine you are drinking a morning coffee. The rich aroma, the warm mug in your hand, the bitter taste—each of these distinct experiences is a pattern of activity in your brain. Cognitive neuroscience seeks to crack the code of these patterns, a puzzle known as the problem of "representation." It is the study of how your brain creates and holds a model of everything you perceive, remember, and decide. This article explores the fascinating quest to understand the physical basis of our mental world.
At its core, a neural representation is the physical embodiment of information within the brain. When you see a face, hear your name, or plan a movement, specific groups of neurons become active in a specific way. This activity—the pattern of electrical signals and their chemical consequences—is the brain's representation of that information.
These representations are not static pictures. They are dynamic processes that allow the brain to work with information. For instance, the representation of a visual stimulus might first appear in classical visual areas before spreading to regions involved in making a choice 3 .
Neuroscientists have discovered that some representations, like those for impending action or reward, can be found in neurons across nearly the entire brain, while others, like a specific sensory input, are confined to narrower regions 3 .
Understanding these codes is fundamental to understanding ourselves. It helps explain how we interact with our environment, how memories are stored, and even how disorders of the mind can arise from disruptions in these neural patterns.
Information is encoded across networks of neurons rather than in single cells, creating robust and flexible representations.
Neural representations evolve over time as information flows through different brain regions during cognitive tasks.
Deciphering the brain's activity requires a suite of sophisticated tools. Researchers no longer rely on a single method but combine multiple techniques to get a clearer picture, gathering evidence for association, necessity, and sufficiency 8 .
| Tool | What It Measures | Key Insight |
|---|---|---|
| fMRI (functional Magnetic Resonance Imaging) | Blood oxygenation changes (BOLD signal) linked to neural activity 2 . | Shows which brain areas are associated with a task, but with an indirect and slow signal 2 6 . |
| Electrophysiology (e.g., Neuropixels) | Electrical activity from individual or groups of neurons 3 6 . | Provides millisecond precision of neural firing, directly reading the brain's code 6 . |
| Lesion Studies | Behavioral changes following specific brain damage 8 . | Tests if a brain area is necessary for a function (e.g., if damage to MT impairs motion perception) 8 . |
| Non-invasive Brain Stimulation (e.g., TMS) | Behavioral changes from temporarily disrupting or enhancing brain activity 8 . | Tests the necessity and sufficiency of an area for a function in a controlled way 8 . |
Each tool has its own strengths. fMRI offers a broad, whole-brain view, while electrophysiology provides the fine-grained detail of neural communication. By converging evidence from these different methods, scientists can move from simply noting that a brain area is active during a task to claiming that it is critically involved in creating a specific representation.
A recent monumental study exemplifies the power of modern neuroscience. A large international team, the International Brain Laboratory (IBL), set out to create a brain-wide map of neural activity during a complex decision-making task, published in Nature 3 .
The experiment was conducted on 139 mice trained on a standardized decision-making task 3 . The mouse had to turn a wheel to center a visual stimulus appearing on either the left or right side of a screen. The task incorporated sensory, cognitive, and motor components, including changing expectations about where the stimulus was likely to appear.
Mice
Neuropixels Probes
Neurons Recorded
The key to this study was its scale. The researchers used 699 Neuropixels probes—a high-density electrophysiology technology—to record from a staggering 621,733 neurons across 279 brain areas in a single hemisphere 3 . This approach allowed them to move beyond studying isolated brain regions and to observe how representations emerge and evolve across the entire brain simultaneously during a single, well-defined behavior.
The findings shattered simple notions of one-brain-region, one-function. The team discovered that representations of task variables were remarkably widespread 3 .
Representations of the visual stimulus appeared transiently in classical visual areas after onset, then spread to other regions 3 .
"Neural responses correlated with impending motor action almost everywhere in the brain." Ramp-like activity leading up to the choice appeared in a collection of midbrain and hindbrain regions 3 .
"Responses to reward delivery and consumption were also widespread," highlighting how core variables like reward are broadcast across the brain 3 .
To quantify the prevalence of these representations, the study analyzed how well task variables could be predicted from neural activity in different brain regions.
| Brain Division | Visual Stimulus Representation | Action-Related Representation | Reward Representation |
|---|---|---|---|
| Cortical Areas | High (Early, transient) | Moderate to High | Moderate |
| Midbrain & Hindbrain | Moderate (Later, ramping) | High | High |
| Cerebellum | Low | High | Moderate |
The study further analyzed the timing of these representations, showing how information flows through the brain.
Peaks immediately after Stimulus Onset
Ramps up until Movement Initiation
Peaks at Movement Initiation
Immediately after Feedback
This brain-wide map is now a public resource, allowing any researcher to probe deeper into the neural basis of decision-making, promising years of new discoveries 3 .
The IBL study also highlights the ongoing evolution of our tools. While it relied on Neuropixels for direct neural recording, other technologies like fMRI are also advancing. For example, new fMRI techniques based on fractional diffusion representation are being developed to improve spatial specificity by measuring microstructural changes in brain tissue during activation, potentially getting us closer to the actual site of neural computation .
The central challenge remains: correlation is not causation. Finding that a brain area represents a variable does not prove it is necessary for the behavior 8 . The field is therefore moving toward a multi-method approach, combining large-scale recording like the IBL's with causal interventions like targeted stimulation or lesions to truly prove a brain area's role in creating a representation.
New techniques like high-resolution fMRI and calcium imaging allow researchers to observe neural activity with unprecedented detail in living brains.
Optogenetics and chemogenetics enable precise control of specific neuron types, allowing causal testing of neural representations.
Ultimately, the quest to understand neural representation is the quest to understand how the physical matter of the brain gives rise to the rich tapestry of mental life. Each new map and each new decoding technique brings us a step closer to answering one of humanity's oldest questions: how do we think, feel, and know?