The hallmark feature of episodic memory is the ability to link events with their temporal and situational contexts. This ability allows for memories to be truly autobiographical, and failures of episodic memory are signs of normal aging and neurodegenerative disease. The proposed research aims to illuminate the neural and cognitive mechanisms underlying human episodic (contextually-mediated) memory through both computational modeling and the analysis of electrocorticographic and single neuron recordings taken as neurosurgical patients search their memory for recently studied material. Building on prior retrieved context models of episodic memory, Aim 1 is to develop an attractor neural network (NeuroCMR) in which both remote and recent memories are stored by associating item representations with unique contextual states that gradually evolve as a function of the sequence of experienced and recalled items. Searching memory for a given item is influenced not only by the contextual information associated with that target but also by the multitude of prior memories learned in partially overlapping contexts.
Aims 2 -4 will test the predictions of NeuroCMR using neural data. Specifically, patterns of electrocorticographic and single-neuron activity will be detected using multivariate pattern analysis methods. These methods will be used to identify the neural signatures of content and context information during both encoding and retrieval, and to identify their anatomical substrates. This work will serve as an important bridge between the behavioral and neurobiological approaches to human memory, and will provide insights into the mechanisms of memory decline both in normal aging and in neurological disease.
One of the longstanding mysteries concerning human memory is how the brain is able to distinguish memories of nearly identical events that occurred at different times. Cognitive theories of memory search and retrieval propose that each memory is laid down in its own unique temporal context, and that context information can be used to guide memory search. Our proposed research will combine computational modeling approaches with analysis of direct brain recordings to test these cognitive theories of memory and to identify the neural mechanisms underlying this vital human ability.
|Lega, Bradley; Burke, John; Jacobs, Joshua et al. (2016) Slow-Theta-to-Gamma Phase-Amplitude Coupling in Human Hippocampus Supports the Formation of New Episodic Memories. Cereb Cortex 26:268-78|
|Healey, M Karl; Kahana, Michael J (2016) A four-component model of age-related memory change. Psychol Rev 123:23-69|
|Long, Nicole M; Kahana, Michael J (2016) Modulation of Task Demands Suggests That Semantic Processing Interferes With the Formation of Episodic Associations. J Exp Psychol Learn Mem Cogn :|
|Ramayya, Ashwin G; Pedisich, Isaac; Kahana, Michael J (2015) Expectation modulates neural representations of valence throughout the human brain. Neuroimage 115:214-23|
|Katkov, Mikhail; Romani, Sandro; Tsodyks, Misha (2015) Effects of long-term representations on free recall of unrelated words. Learn Mem 22:101-8|
|Long, Nicole M; Kahana, Michael J (2015) Successful memory formation is driven by contextual encoding in the core memory network. Neuroimage 119:332-7|
|Greenberg, Jeffrey A; Burke, John F; Haque, Rafi et al. (2015) Decreases in theta and increases in high frequency activity underlie associative memory encoding. Neuroimage 114:257-63|
|Recanatesi, Stefano; Katkov, Mikhail; Romani, Sandro et al. (2015) Neural Network Model of Memory Retrieval. Front Comput Neurosci 9:149|
|Haque, Rafi U; Wittig Jr, John H; Damera, Srikanth R et al. (2015) Cortical Low-Frequency Power and Progressive Phase Synchrony Precede Successful Memory Encoding. J Neurosci 35:13577-86|
|Burke, John F; Ramayya, Ashwin G; Kahana, Michael J (2015) Human intracranial high-frequency activity during memory processing: neural oscillations or stochastic volatility? Curr Opin Neurobiol 31:104-10|
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