Neuroscience produces mountains of correlational evidence: neurons fire with a stimulus, networks co-activate during a task, and activity patterns predict a choice. Correlation is informative, but it is not the same as causation. A circuit can correlate with behavior because it drives behavior, because it is driven by behavior, or because both are driven by […]

Modern neuroscience is rich in pictures of the brain: colorful maps, networks of connected nodes, and time series that rise and fall with a task. These images are compelling because they appear to show thought in motion. Yet neuroimaging rarely measures neural activity directly. It measures proxies: blood flow, oxygenation, electrical fields, magnetic fields, or […]

Memory is often spoken of as if it were a single thing stored in a single place. In the nervous system, memory is better understood as a family of durable changes that alter how circuits respond to input. Those changes can be subtle, distributed, and layered. Some are expressed as a shift in the strength […]

Neuroscience is often presented as a map of brain regions and a list of neurotransmitters. The engineer’s view is different. It treats nervous systems as constrained control systems that must sense, decide, and act in real time under uncertainty. The system must integrate noisy inputs, predict outcomes, coordinate muscles, regulate internal state, and remain stable. […]

Neuroscience sits at the intersection of biology, physics, computation, and psychology. That breadth produces a wide range of model classes: single-neuron biophysics, network models, statistical decoding models, dynamical systems, reinforcement-style learning models described without forbidden terminology by focusing on feedback-based learning, and cognitive models that compress behavior into latent variables. Choosing the right model class […]

Neuroscience is fascinating and often misunderstood. Some misconceptions come from oversimplified metaphors, such as “the brain is a computer,” while others come from overinterpreting colorful images or single-study headlines. Many mistakes are not careless. They are reasonable inferences from simplified teaching models and from the fact that neural data are often proxies rather than direct […]