The intellectual thrust of systems neuroscience at Princeton is the study of neural coding and dynamics. Neural coding refers to the way that information is represented in the electrical and biochemical signals in neurons (perception and short term memory) and the patterns of synaptic connections (long term memory). Neural dynamics refers to patterns of nerve cell electrical and chemical activity in which information is created, manipulated and stored. Neural dynamics is involved in decision making or in planning and executing sequences, such as in speaking or playing tennis. Neural dynamics also represents the cognitive manipulation of information necessary in mathematics or abstract thought. Neural coding and dynamics define the Princeton approach to systems neuroscience in a very concrete way, and this approach is distinctly different from other neuroscience institutes. Success will require the combined use of quantitative analysis, theoretical modeling, and the application of the most advanced technologies for studying the brain.

We must first understand brain micro-circuitry if we are to understand neural coding and dynamics. The brain is distinctly different from other tissues in the body because each brain cell receives precisely-defined input signals from tens of thousands of other neurons and it sends its own output signals to a different precisely-defined set of thousands of neurons. Many of the neurons with which a given neuron interacts are in close proximity, defining local microcircuits, while others are centimeters away and define a longer-range pattern of connectivity in the brain. This wiring is not at all random; it is exquisitely precise and almost unimaginably complex, vastly more complex, for example, than the human genome.

The discipline of neuroanatomy is more than a century old, but it has defined only the crudest measures of circuitry, such as bundles of nerve fibers that connect macroscopic brain regions or the properties of random neurons. What is clear from this work, however, is that there will be differences in the way neurons connect at the micro-scale in different brain areas, and even in different regions of the same area (for example, the region that detects visual motion is different from the region that recognizes faces). Micro-circuitry is, in essence, the next generation of neuroanatomy where we will create a true “wiring diagram” of specific brain areas.

Conceptually, the move from gross neuroanatomy to micro-circuitry it is a little like going from a gene to the genetic sequence of a gene, where the information in a gene is quantified. Neuroanoatomy shares another feature with the genome project: brain micro-circuitry is so complex, it can only be done using high-throughput techniques that allow us to explore neural circuitry in a semi-automatic fashion, analogous to the instrumentation and algorithms used in gene sequencing. Princeton has a great opportunity to be at the forefront of this emerging discipline by hiring new faculty and developing new facilities and instruments for high-throughput neuroanatomy.

It is useful to illustrate some of the conceptual difficulties in determining how the brain operates with an analogy to computers and electronic circuits. In your personal computer, software determines what computations are done; the underlying micro-circuitry of the computer hardware does not change when you switch from browsing the web to word processing. Is that how the brain works? Most evidence suggests that it does not. Rather, the circuits of neurons that are used, say, for recognizing speech, have been specialized through evolution to have circuitry tailored to the specific forms of computation inherent in speech recognition. These circuits are different from those used for coordinating muscles when playing tennis. In other words, circuitry in the brain seems to be special purpose hardware (rather than software) tailored to specific tasks.

Given this specialization of brain "hardware", we need to understand the relationship between specific forms of micro-circuitry and specific forms of brain function. However, even though the micro-circuitry is specialized, there are likely to be standard circuits that are used over and over in different ways, the same way that certain standard circuits used in electronics for many different purposes. Thus, the emerging hypothesis is that there are certain standard microcircuits that are organized in different ways, through different synaptic wiring diagrams, to build special-purpose neural circuits. At this time, we have no idea what these standard circuits are. It is, however, clear that we must create quantitative wiring diagrams, and understand the properties of the neurons. We can then match the wiring diagram with the particular tasks performed by that portion of the brain.

Delineating brain micro-circuitry is necessary, but not sufficient, for understanding neural coding and dynamics. What is also needed are tools and experimental procedures that manipulate and measure the properties of defined neurons within the circuit under controlled behavioral conditions. Increasingly this work requires specialized instrumentation and genetically modified biological preparations.

One new area combines the use of high-resolution optical imaging methods with genetically encoded protein optical sensors. For example, it is possible to place a fluorescent protein in specific nerve cells in a model animal, such as a mouse or fly. This protein changes color in response to changes in membrane voltage or calcium ion concentration, which occurs when a neuron is active. Using a specialized microscope, this change can be actually seen in live model animals. Similarly, a genetically encoded protein from algae that produces an electrical current in response to light can be precisely introduced into the neurons in a living model animal. The experimenter can then turn the neurons on and off with flashes of light. These imaging and sensing techniques will provide data about the activity patterns of individual neurons that define neural coding and neural dynamics. They will also allow experimenters to manipulate neural activity to test the theoretical models of how circuits work.

What binds together the study of the brain's micro-circuitry and the measurements of neural activity? The answer is theoretical and computational neuroscience. Basically, theorists and experimentalists have developed a concept about what kind of algorithm a given brain area uses for a given task, say, in recognizing a face or making a decision. This concept needs to be represented in a mathematical model that provides quantitative descriptions of the neural code—how the information is represented in the electrical and chemical activity of the neurons—and the dynamics—the way that the activity evolves with time and space in performing the algorithm. The entire model must correspond with the actual wiring diagram that has been drawn from experiments. Only then will we start to adequately describe even a small area of the brain.