What Is Brain Function and How Is It Studied Across Neural Levels?

Brain function refers to the activity and processes that underlie perception, movement, memory, and sleep. At the Computational Neurobiology Laboratory (CNL), the stated long-range goal is to build bridges between brain levels—from the biophysical properties of synapses to the function of neural systems—using combined experimental and computational approaches. This explainer covers what those levels are, the methods used to connect them, and the functions they produce.

The levels of brain function

The CNL framing treats brain function as a chain of scales rather than a single phenomenon. Each level has its own questions and its own tools:

• Synapses and dendrites — how individual neurons receive and integrate synaptic signals.
• Neural circuits — how groups of neurons generate behavior.
• Systems and behavior — how perception, movement, memory, and sleep emerge, and how learning adaptively modifies the circuits beneath them.

The central issues the lab addresses map directly onto this chain: how dendrites integrate synaptic signals in neurons, how neural circuits generate behavior, and how learning and sleep adaptively modify these circuits.

How the levels are connected

Bridging levels means using each scale to constrain the others. Experimental measurements at the synaptic level inform circuit models, and circuit-level behavior feeds back into questions about cellular mechanisms. The CNL describes this as combining experimental and computational approaches rather than treating them as separate tracks.

Computational modeling and simulation

• Monte Carlo methods (MCell) are used to explore synapses. In practice, this means simulating molecular and synaptic events stochastically—useful when the number of molecules or channels involved is small enough that average-value equations miss important variability.
• Independent components analysis (ICA) is used to analyze brain activity. ICA separates a mixed signal into statistically independent sources, which is how it isolates distinct activity patterns from recordings that blend many sources together.

A specific circuit focus

Fast-spiking parvalbumin-positive interneurons are the focus of both computational and experimental studies of attention in the visual cortex and of dysfunction in schizophrenia. This is an example of how one cell type can serve as a bridge: it is studied at the biophysical level and at the level of cortical function and dysfunction at the same time.

Examples of brain functions studied

The lab's keyword set names the functions in play: motor control, visual perception, neural coding, memory systems, and sleep. These are not isolated topics—they are the behaviors and processes that the circuit-level and synaptic-level work is meant to explain. Attention in the visual cortex, for instance, is a function studied through the activity of specific interneuron populations.

How learning and sleep change brain function

Learning and sleep are treated as adaptive modifiers of neural circuits, not as separate phenomena. The stated goal includes understanding how learning and sleep adaptively modify circuits—meaning brain function is not fixed hardware but a system that rewires itself. Recent work from the lab includes a study on presynaptic vesicles supplying membrane for axonal bouton enlargement during long-term potentiation, a cellular mechanism directly relevant to how learning changes synaptic structure.

Where to go next

If you want to follow this work, the CNL site lists publications, people, and research pages, plus events such as the Joint Symposium on Neural Computation and the NeurIPS conference. The "What's Hot" section points to resources including a MOOC on learning how to learn, a book on the deep learning revolution, and material on cortical travelling waves. These are entry points into the same question: how activity at one level of the brain produces function at another.