Molecular Biology Faculty
Carlos D. Brody
and the princeton neuroscience institute
Moffett Lab, 323
Lab (609) 258-5559
Rebecca Khaitman heller
Quantitative approaches to Systems Neuroscience
What are we interested in? Here's an example: you're browsing DVDs in a video store. You pick one up—you like it, perhaps you might buy it, but you're not sure yet. You put it back down, and stroll down the aisle, pick a second DVD up. You compare them; perhaps today you decide to buy the first DVD. What happened in your brain as you went through all this? What are the neural mechanisms that allow you to remember, for a few seconds, how much you liked the first DVD; to compare the two DVDs; to make a decision; to apply the rules of behavior appropriate for the context you're in (here, a video store)? In other words, what are the neural mechanisms underlying your cognitive abilities?
What methods do we use in our research? We use a combination of computational, behavioral, and electrophysiological techniques. We train rats to perform tasks that require cognitive components that we're interested in studying. For example, we train them to remember a stimulus for a few seconds, and to then make a behavior based on their memory of the stimulus. We can then study neural responses during this behavior, and observe the neural correlates of short-term memory. To help us understand the mechanisms behind our findings, we build computational models of networks of spiking neurons, with which we explore the circuit architectures and mechanistic hypotheses that could explain the experimental results. The models both give us greater insight into potential mechanisms, and help us decide what are the best next experiments to test and distinguish between hypotheses.
Who are we? Personnel in the lab range from purely computational to purely experimental. We try to minimize the barriers in going from one end of this spectrum to the other: all researchers in the lab are encouraged to flow easily and freely within the computational/experimental spectrum, according to their own interests and needs at any given point in time, and to talk frequently with people at other points of the spectrum.
The specific tasks we are studying. Currently we are focused on three behavioral tasks performed by rats, all studied from the above combined computational/experimental approach.
The first is a task in which rats are presented with a first stimulus; then there is a pause; then they are presented with a second stimulus; and the rats must compare the two stimuli and make a binary decision based on the comparison. (This is our rodent analogue of deciding between the two DVDs in the music store.) We use this task to study short-term memory and decision-making. This task is adapted from a primate task used by the Romo lab (see Romo et al., 1999, below).
In the second task, we study how it is that cognitive state can flexibly determine appropriate rules of behavior. Rats experience two types of trials, "Pro" and "Anti." In Pro trials, they must orient towards a sound to obtain a water reward. In Anti trials, they must orient away from a sound to obtain a water reward. Which type of trial they will be in is indicated to them before the trial starts; this sets the cognitive stage. Then, when the sound comes, the rats must use their knowledge of whether it is a Pro or Anti trial to select which of two opposite sensorimotor rules is the appropriate one to follow.
The third and final task that we are working on is one where rats hear a sound, and must decide whether the sound was long or short. We are using this task to study the neural basis of time perception. Unlike other perceptual modalities, there is no specific sensory organ for "time." This is in strong contrast to modalities like vision, for example: for vision, we can follow anatomical pathways from the eyes to know where in the brain visual information is processed and perceived. But where in the brain is information about time processed and perceived? And how is time represented? These are some of the questions we are addressing with our time discrimination task.
Erlich JC, Brody CD. (2013) Neuroscience: What to do and how. Nature. 503: 45-7. Pubmed
Scott BB, Brody CD, Tank DW. (2013) Cellular resolution functional imaging in behaving rats using voluntary head restraint. Neuron. 80: 371-84. Pubmed
Brunton BW, Botvinick MM, Brody CD. (2013) Rats and humans can optimally accumulate evidence for decision-making. Science. 340: 95-8. Pubmed
Kopec CD, Bowers AC, Pai S, Brody CD. (2010) Semi-automated atlas-based analysis of brain histological sections. J Neurosci Methods. 196: 12-19. PubMed
Kopec CD, Brody CD. (2010) Human performance on the temporal bisection task. Brain Cogn. 74: 262-272. PubMed
Jun JK, Miller P, Hernández A, Zainos A, Lemus L, Brody CD, Romo R. (2010) Heterogenous population coding of a short-term memory and decision task. J Neurosci. 30: 916-929. PubMed
Markowitz DA, Collman F, Brody CD, Hopfield JJ, Tank DW. (2008) Rate-specific synchrony: using noisy oscillations to detect equally active neurons. Proc Natl Acad Sci USA. 105: 8422-8427. PubMed
Machens CK, Romo R, Brody CD. (2005) Flexible control of mutual inhibition: a neural model of two-interval discrimination. Science 307: 1121–1124. PubMed
Hopfield JJ, Brody CD. (2004) Learning rules and network repair in spike-timing-based computation networks. Proc Natl Acad Sci USA 101: 337–342. PubMed
Brody CD, Hopfield JJ. (2003) Simple networks for spike-timing-based computation, with application to olfactory processing. Neuron 37: 843–852. PubMed
Hopfield JJ, Brody CD. (2001). What is a moment? Transient synchrony as a collective mechanism for spatiotemporal integration. Proc Natl Acad Sci USA 98: 1282–1287. PubMed
Romo R, Brody CD, Hernandez A, Lemus L. (1999) Neuronal correlates of parametric working memory in the prefrontal cortex. Nature 399: 470–473. PubMed