Randomness drives how our brain understands the world around us

For decades, researchers believed the brain’s navigation system relied on neatly structured neurons called place cells. These neurons, located in the hippocampus, were thought to fire in single, well-defined regions of space, forming a stable map that guides movement.

However, recent findings, published in the journal Neuron, challenge this view, revealing that place cells fire in multiple, irregularly shaped locations, especially in larger environments.

This discovery has reshaped the field of systems neuroscience, which aims to understand how networks of neurons represent information about the world. Traditional studies focused on neurons with smooth, predictable tuning curves.

But new experimental techniques—such as high-throughput neural recordings and behavioral studies in freely moving animals—have uncovered a far messier reality. Place cells do not conform to neat, bell-shaped activity patterns. Instead, they display widely varying shapes and sizes, raising fundamental questions about how the brain organizes spatial information.

A groundbreaking study led by Professor Yoram Burak at the Hebrew University of Jerusalem has introduced a simple yet powerful mathematical framework to explain the seemingly chaotic behavior of place cells.

His team found that a concept known as a Gaussian Process—a mathematical function that describes random variations—can predict the irregular patterns observed in place cell activity across different species and environments.

In this model, place fields emerge from a random Gaussian process applied across space. The process generates a smooth but random pattern, and only the regions where the function crosses a certain threshold become active place fields.

This approach accounts for the multiple firing locations and irregular field shapes observed in experimental data from rats, mice, and bats navigating different environments.

A Gaussian process is widely used in fields like physics and machine learning because it maximizes entropy while making minimal assumptions.

Applying this concept to neuroscience suggests that place cell activity is largely shaped by random, unstructured inputs rather than finely tuned circuits. This insight challenges long-standing beliefs that the brain’s spatial map is highly organized and meticulously arranged.

The researchers tested their model by reanalyzing recordings of place cells from previous experiments. Their findings confirmed that the Gaussian process model accurately predicts key features of place-cell activity, including the distribution of field sizes and their multi-peaked structure.

“Our findings suggest that randomness, rather than specific design, governs the synaptic organization of inputs to CA1 neurons in the hippocampus,” explains Nischal Mainali, a co-author of the study.

The model’s predictions were validated across different experimental conditions, reinforcing the idea that a single mathematical principle can explain neural activity in diverse environments. This universality suggests that the brain may not rely on rigidly structured maps but instead adapts to its surroundings by leveraging statistical properties of random inputs.

The implications of this work extend far beyond spatial navigation. The ability of the brain to encode complex information using random processes could inspire new approaches in artificial intelligence, robotics, and cognitive science. The findings also open new directions for research into memory formation, neural coding, and the broader principles governing brain function.

“The seemingly random firing patterns of place cells in large environments form ‘codewords’ that are uniquely assigned to different positions in space,” says Prof. Burak. “We believe the brain tunes these random codewords to create a highly efficient representation of positions.”

This research highlights the power of randomness in neural computation. By embracing unpredictability, the brain may achieve more flexibility and efficiency than previously imagined, reshaping how scientists understand cognition, learning, and decision-making.

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