Systems neuroscience represents the intermediate level of inquiry in understanding the nervous system, bridging the microscopic detail of individual cells with the complexity of observable actions and thought. This field focuses on how populations of neurons, rather than single cells or molecules, work together to process information and generate function. It explores the structure and operation of neural circuits and large-scale networks that span multiple brain regions. Researchers aim to uncover the mechanisms that translate sensory input into perception, decision-making, and movement.
Defining the Scope of Systems Neuroscience
Neuroscience is organized into a hierarchy of scales, ranging from the molecular to the cognitive. Systems neuroscience occupies the level above molecular and cellular neuroscience, which focus on the properties of proteins, genes, and individual neurons. Its subject is the collective action of these components, concentrating on groups of neurons organized into functional circuits. This focus allows the field to investigate emergent properties—functions that arise only when neurons interact in a system and cannot be predicted by studying the cells in isolation.
The collective behavior of millions of synchronized neurons produces measurable electrical oscillations associated with attention and memory, a phenomenon not observable from a single cell. This scale differentiates systems neuroscience from cognitive neuroscience, which focuses on high-level mental functions like language or self-awareness. Systems neuroscience seeks to explain the mechanical underpinnings of how the brain works, such as how the visual system processes light, rather than abstract concepts. It provides the functional organization that links the cellular hardware to the behavioral output.
The Building Blocks of Neural Systems
The units of study in this field are the neural circuit and the neural network, representing different scales of organization. A neural circuit is a small, defined population of neurons interconnected by synapses to carry out a specific, localized function. A simple example is the circuit responsible for a spinal reflex, where sensory input is rapidly converted into a motor response through a limited pathway. These circuits act as specific information processing pathways within a larger structure.
Neural networks, by contrast, are broader, interconnected collections of circuits distributed across distant brain regions. They represent the large-scale communication required for complex tasks, such as the default mode network involved in internal thought or the executive control network involved in decision-making. Information flows through these structures in various ways, including diverging circuits, where a single neuron stimulates thousands of others, and converging circuits, where inputs from many sources affect a single target.
Key Methodologies for Studying Neural Networks
Investigating complex, distributed neural networks requires sophisticated tools capable of capturing activity across vast spatial and temporal scales.
Observation and Imaging
These techniques allow researchers to record the collective activity of neural systems in real-time. Functional Magnetic Resonance Imaging (fMRI) measures changes in blood flow related to neural activity, providing high-resolution maps of active brain regions, though with limited temporal precision.
Electroencephalography (EEG) and magnetoencephalography (MEG) offer high temporal resolution, capturing synchronized electrical oscillations produced by large neuronal populations. This is essential for studying dynamic processes like attention. On a smaller scale, calcium imaging enables the visualization of individual neuron activity within a circuit by monitoring calcium influx, a marker of electrical firing.
Manipulation
Manipulation techniques establish a causal link between a specific neural circuit and a behavior. Optogenetics involves genetically modifying specific neurons to make them sensitive to light, allowing researchers to turn their activity on or off with millisecond precision. Chemogenetics offers a similar ability to control neuronal activity, but uses designer drugs to activate or inhibit genetically-targeted cell populations. These tools enable the precise functional dissection of multi-area brain networks by testing the necessity and sufficiency of a circuit’s role in a given behavior.
Analysis and Modeling
Analysis and modeling are indispensable, as the massive amounts of data generated by recording techniques cannot be interpreted without computational approaches. Computational models simulate neural dynamics and test hypotheses about how networks process information and generate outputs. Network graph theory helps map the functional relationships between different brain regions, treating them as nodes and their connections as edges to understand organizational principles. These models are vital for translating raw electrical and imaging data into a coherent understanding of the brain’s mechanisms.
Connecting Neural Systems to Complex Behavior
The goal of systems neuroscience is to explain how the coordinated action of circuits and networks gives rise to complex behaviors. Sensory processing, such as vision or audition, is one domain where this approach is effective, tracing the path of information from sensory receptors through multiple processing stages. The visual system, for example, involves a hierarchy of areas that analyze different features, such as edges, motion, and color, requiring coordinated activity across the visual cortex.
Motor control is another area of focus, examining how the brain plans and executes movement, which requires communication between the motor cortex, cerebellum, and basal ganglia. This includes understanding how the brain transitions from an intention to an action, with different networks dictating the speed and precision of the response. Higher-order cognition, including decision-making and memory retrieval, relies on the integration of information across multiple, distributed networks. The collective activity patterns within these networks are predictive of a behavioral outcome.