 |
Neuroscience Graduate Program at UCSF
Learning and Spatial Coding in the Hippocampal-Cortical Circuit
The ability to use experience to guide behavior (to learn) is one of the central functions of the brain. We are interested in understanding the neural correlates of learning and memory. In particular, our laboratory focuses on the circuitry of the hippocampus and adjacent regions. Our goal is to examine the relationships among neural firing patterns, behavior, and anatomy to understand how the brain uses and stores information. Ultimately we should be able to generate accurate computational models of learning to both test hypotheses concerning hippocampal-cortical interactions and to generate new predictions that can be tested experimentally.
Anatomical organization
The hippocampal formation has a unique anatomical organization in that the connectivity between adjacent hippocampal regions is almost exclusively unidirectional. The majority of neocortical input to the hippocampus comes in through the superficial layers of the entorhinal cortex and connections proceed through the dentate gyrus, to CA3 and on to CA1 (the hippocampus proper), and then to the subiculum. Nearly all neocortically bound outputs from the hippocampus originate in CA1 and the subiculum and target cells in the deep layers of the entorhinal cortex, which projects both to numerous neocortical regions as well as to back to the superficial layers of the entorhinal cortex. Our research uses that organization to compare patterns of activity across regions and to use the similarities and differences among the patterns to identify the transformations that occur in the hippocampal circuit.
An animal model for hippocampal function
Numerous researchers have shown that a human without a hippocampus is unable to form new memories of facts or events. In rodents these same structures play an essential role in animal's abilities to learn about and remember complex associations, including tasks where the animal must learn and remember information about a set of spatial cues in order to navigate through an environment. Both event / fact memory in humans and spatial memory in rodents require learning complex relationships, and that parallel strongly suggests that qualitatively similar processing occurs in the human and the rat hippocampus.
Previous studies have shown that neurons throughout the hippocampal formation show place specific firing patterns, where a given neuron is active only in a subregion of the animal's environment. While neurons throughout these areas show place specificity, the properties of the place code differs from regions to region. We have shown, for example, that neurons in the CA1 subregion tend to be active in small subregions of the environment, while downstream in the deep layers of the entorhinal cortex, neurons tend to be active across long, contiguous regions, suggesting that these cells may be involved in representing extended trajectories. In addition, deep entorhinal neurons appear to code for task related information, in that individual neurons are frequently in multiple, behaviorally related locations.
Learning in the hippocampus and cortex
Most previous focused on describing patterns of activity during well learned tasks, and we therefore know little about neural processing during learning. One prominent hypothesis states that learning takes place first in the hippocampus and over time information is transferred to neocortical regions in a process known as consolidation. We are currently examining the development of spatial and task related patterns of activity in CA1 and the entorhinal cortex to determine whether representations form at different rates in these structures.
That experiment, and related work currently underway, focuses on understanding the relationship between learning and changes in neural firing patterns in the hippocampal formation and related cortical regions. Over the longer term we plan to examine learning related changes activity across a wide variety of regions in an effort to understand the interactions within the hippocampus and between the hippocampus and the cortex during learning and memory retrieval.
Our approach
Our investigations rely on the combination of spatial tasks, large scale multielectrode recordings from awake, behaving animals, advanced analytical techniques, and computational modeling. We utilize custom large scale (128 channel) real-time Linux recording systems that allow us to record both cellular activity and local field potentials (also known as EEG) from up to 32 independently moveable four wire electrodes (tetrodes). These tetrodes are generally targeted at two or more structures, allowing us to simultaneously monitor patterns of activity across multiple brain regions. We record activity while animals perform tasks that require them to learn about and remember the spatial relationships among various locations and the behavioral significance of those locations.
We have also begun collaborations with two other groups in an effort to develop and apply genetic tools for manipulating neural circuits in vivo. We are working with Dr. Steven Finkbeiner and his colleagues here at UCSF and with Dr. Karl Deisseroth and his colleagues at Stanford University to combine viral transfection techniques with multielectrode recordings. This combination should make it possible to effect specific changes in neural plasticity or activity and to understand how those changes affect the network as a whole.
Once data are collected, we analyze them using both standard techniques as well as advanced algorithms developed in collaboration with Dr. Emery Brown of Massachusetts General Hospital and the Harvard / M.I.T. Health Sciences and Technology Program. Learning is thought to involve complex changes in the relationships among external stimuli, neural activity, and behavior. Studying the neural correlates learning is difficult, however, as the standard analyses require averaging across multiple trials or multiple neurons to produce accurate results. Unfortunately, averaging makes it impossible to identify the fast changes in neural representations that are thought to occur during learning.
We have therefore developed adaptive estimation algorithms that allow us to describe the changing relationships between neural firing rates and a set of other variables, including, but not limited to, the animal's position in space and the temporal structure of the neuron's spike train. These algorithms do not require binning over time or space. Instead, they combine information about previous activity with new information to produce accurate instantaneous estimates of the underlying neural representation. We are also working with Dr. Brown and colleagues to develop new algorithms and models that will allow us to represent the dynamics of populations of simultaneously recorded neurons and the relationship between those dynamics and behavior.
Finally, once sufficient data have been collected it becomes possible to generate computational models of the system. Our current focus is on the data collection and analysis, as there are many fundamental issues that have not been addressed experimentally, but over the longer term we are interested in creating accurate and well constrained models.
Sen Cheng
Sloan-Swartz post-doctoral fellow
Ph.D, Michigan State University
Data analytic and theoretical models of hippocampal dynamics
Yuri Dabaghian
Sloan-Swartz post-doctoral fellow
Ph.D, University of Rhode Island
Hippocampal – parietal interactions
Steven Kim
Graduate student
A.B. Harvard University
CA1 – subiculum interactions
Mattias Karlsson
Graduate student
B.S., UC Davis, 2002
CA3 – CA1 interactions
Caleb Kemere
Sloan-Swartz post-doctoral fellow
Ph.D, Stanford University
Manipulating neural ensemble dynamics in the hippocampus
Ana Nathe
Visiting Graduate Student (Boston University)
B.A. Wellesley College
CA1 plasticity and experience
Ken Park
Graduate Student
B.S. University of Florida
CA1 – entorhinal interactions
Annabelle Singer
Graduate Student
B.A. Wesleyan University
Effects of dentate pattern separation on CA3
Frank LM, Eden, UT, Wilson, MA, Brown, EN. (2002) Contrasting patterns of receptive field plasticity in the hippocampus and the entorhinal cortex: an adaptive filtering approach. Journal of Neuroscience. May 1, 22(9): 3817-30.
Wirth S, Yanike M. Frank LM, Smith AC, Brown EN, Suzuki WA. (2003) Single neurons in the monkey hippocampus signal learning of new associations. Science. 300(5625):1578-81.
Nguyen D, Frank LM, Brown EN. (2003) An application of reversible-jump MCMC to spike classification of multiunit extracellular recordings. NETWORK: Computation in Neural Systems. 14(1):61-82.
Brown EN, Barbieri R, Eden UT, Frank LM. (2003) Likelihood methods for neural data analysis. In: Feng J, ed. Computational Neuroscience: A Comprehensive Approach, London: CRC Press.
Frank LM, Brown EN. (2003) Persistent activity and memory in the entorhinal cortex. Trends in Neuroscience. Aug;26(8):400-1.
Smith AC, Frank LM, Wirth S, Yanike M, Hu D, Kubota Y, Graybiel AM, Suzuki W, Brown EN. (2004) Dynamic analysis of learning in behavioral experiments. Journal of Neuroscience, Jan 14;24(2):447-61.
Barbieri R, Frank LM, Nguyen DP, Quirk MC, Solo V, Wilson MA, Brown EN. (2004) Dynamic analyses of information encoding by neural ensembles. Neural Computation, Feb;16(2):277-307.
Eden UT, Frank LM, Barbieri R, Solo V, Brown EN. (2004) Dynamic analyses of neural encoding by point process adaptive filtering, Neural Computation, May;16(5):971-98.
Frank LM, Stanley GB, Brown EN. (2004) Hippocampal plasticity across multiple days of exposure to novel environments. Journal of Neuroscience, Sep 1;24(35):7681-9.
Frank LM, Brown EN, Stanley GB. (2006) Hippocampal and Cortical Place Cell Plasticity: Implications for Episodic Memory. Hippocampus. 16(9):775-84.
Cheng S, Frank LM (2008) New experiences enhance coordinated neural activity in the hippocampus. Neuron. Jan 24;57(2):303-13.
Karlsson M, Frank LM. (2008) A network mechanism for the formation of sparse, informative representations in the hippocampus. Journal of Neuroscience. Dec 24;28(52):14271-81.
Kim SM, Frank LM (2009) Hippocampal lesions impair rapid learning of a continuous spatial alternation task. PLoS ONE 4:e5494.
Karlsson MP, Frank LM. (2009) Awake replay of remote experiences in the hippocampus. Nature Neuroscience. Jul;12(7):913-8.
Loren Frank, Ph.D.
Phone
415-502-6317
Physical Address
513 Parnassus
S-859
Mailing Address
UCSF
513 Parnassus
Box 0444
San Francisco, CA 94143-0444
For Internal Campus Mail
Box 0444
Other Websites