Daoyun Ji, Ph.D.
Department of Molecular and Cellular Biology
Ph.D.: Baylor College of Medicine
Postdoctoral training: Massachusetts Institute of Technology
Cellular and Systems Neurobiology of Memory Formation, Storage, and Utilization
“Memory is deceptive because it is colored by today’s events.” – Albert Einstein
“Seeing is believing.” – English proverb
These quotes reflect an interesting sensory-memory mutual interaction between what we learn (memory) and what we see (sensory perception). Memories may be modified by current sensory input, and perceptions are biased by memories. The long-term goal of our research is to understand how sensory information is integrated into memory traces and how memories in turn shape other brain functions, such as perception.
All these behaviors are largely mediated by neurons, the primary cell types in the brain. These cells are connected to form complex circuits and communicate using action potentials (spikes). It is these circuits, like electronic circuits in a computer, that process sensory input, encode and store memories, and generate responses. To understand the sensory-memory interactions, we study the dialogue between cells and circuits in the visual cortex, an area critical for visual perception, and those in the hippocampus, a memory center, in rats and mice.
First, we examine how cortical and hippocampal cells respond to memory tasks and how the responses evolve during different stages of memory formation. Since sleep has been proposed to be involved in memory consolidation, which turns short-term memories in the hippocampus into long-term memories in the cortex, an important aspect of the study is to examine functional significance of sleep. This study primarily uses tetrode recordings to monitor spike patterns of a large number of cells in multiple brain areas. The technique makes it possible to read the “mind” of an animal during a task (learning) and during sleep (like in dreaming). Recently we have found that memory traces are coordinately replayed during sleep in the hippocampus and visual cortex (Figure below). Using the same technique, we also uncovered the dynamics of hippocampal activity patterns while rats learn novel spatial trajectories.
Second, to understand the mechanisms underlying these interactions, we study the feed forward and feed back circuits between the visual cortex and hippocampus in brain slices. An area of focus is temporal area A (TeA), which interfaces between the visual and memory circuits in rats. The recently developed channelrhodopsin system offers a great opportunity to study long-range connections across circuits. Furthermore, abnormal sensory-memory interactions may be involved in a number of neurological and psychiatric disorders. For example, hallucinations and dementia often occur together in patients with Alzheimer’s, Parkinson’s, or posttraumatic stress disorder. We utilize transgenic or pharmacological rodent models to study cortical-hippocampal interactions in these diseased states. Finally, computational approaches are applied to decode functional structures from spike patterns of normal or abnormal brain activities.
Memory traces are replayed during sleep. (A, B) Activity patterns in the visual cortex (A) and hippocampus (B) while a rat was running a maze (RUN) and while the animal was in sleep immediately afterward (SLEEP). A particular experience, such as running a trajectory on a maze, is encoded by unique spike patterns (top) of populations of neurons in both areas. Each row represents a neuron and each tick represents a spike at a particular time of the running. Later during slow-wave sleep, similar activity patterns are re-expressed (bottom) in both the visual cortex and hippocampus, suggesting the memory for this particular experience is reactivated (like in a dream). Monitoring brain activity as such provides a direct observation of memory traces, and may reveal a mechanism for information re-processing during sleep that contributes to memory consolidation and storage. Interestingly, during slow-wave sleep, the replay events are 4 to 5 times faster. (Note the different time scales.)
- Ji D. and Wilson MA. (2008). Firing Rate Dynamics in the Hippocampus Induced by Trajectory Learning. Journal of Neuroscience, 28:4679-4689.
- Ji D. and Wilson MA. (2007). Coordinated Memory Replay in the Visual Cortex and Hippocampus. Nature Neuroscience, 10:100-107.
- Broide RS, Salas R, Ji D, Paylor R, Patrick JW, Dani JA and De Biasi M. (2002) Increased Sensitivity to Nicotine-Induced Seizures in Mice Expressing the L250T Alpha7 Nicotinic Acetylcholine Receptor Mutation. Molecular Pharmacology 61:695-705.
- Dani JA, Ji D. and Zhou FM. (2001). Synaptic Plasticity and Nicotine Addiction. Neuron 31:349-352.
- Ji D, Lape R and Dani JA. (2001). Timing and Location of Nicotinic Activity Enhances or Depresses Hippocampal Synaptic Plasticity. Neuron 31:131-141.
- Ji D and Dani JA. (2000). Inhibition and Disinhibition of Pyramidal Neurons by Activation of Nicotinic Receptors on Hippocampal Interneurons. Journal of Neurophysiology 83:2682-2690.
- Ji D, Hu B and Chen T. (1996). Dynamics of a Neural Network Model with Finite Connectivity and Cycle Stored Patterns. Physica A 229:147-165.
- Ji D, Hu B and Chen T (1996). Dynamics of Overlap in Pair-Correlated Hopfield model. Communication in Theoretical Physics 25: 293-298.
- Ji D, Hu B and Chen T (1995). Storage and Retrieval of General Pattern Sequences. Communication in Theoretical Physics 23: 495-500.
- Ji D, Hu B and Chen T (1995). Parallel Dynamics of Hopfield Network with Self-Coupling Terms. Communication in Theoretical Physics 23: 363-370.