GitLab

Our lab investigates human memory and its neural basis utilizing a combination of behavioral, computational, and neurophysiological strategies. In our computational investigations, we build mathematical and computer-simulation fashions to account for the dynamics of memory retrieval in a variety of episodic and spatial memory improvement solution duties. As a result of behavioral information offers a sparse reflection of the brain’s exercise supporting memory, we simultaneously record neurophysiological alerts as patients with arrays of implanted electrodes perform memory tasks. In these investigations we research neural exercise at a number of spatial scales, ranging from particular person neurons to spatially-distributed networks of field-potential activity supporting memory. Several of our present projects also use electrical stimulation to control memory circuits, each for understanding fundamental memory mechanisms and also for developing therapies to restore memory in patients with mind injury or neurological disease. Please click on here for extra information and errata. To assist clarify the processes underlying encoding, group and retrieval of episodic reminiscences, we have developed, prolonged and refined a category of fashions based on the idea that items in memory turn out to be related to a time-various illustration of spatio-temporal context.

The temporal context model (TCM; Howard and Kahana, 2002 and TCM-A Sederberg, Howard, and Kahana, 2008) sought to explain the time-scale invariance of recency and contiguity results in free recall, and dissociations between recall of current and remote memories. Subsequent modeling work generalized TCM past temporal context to account for the influence of semantic data on recall dynamics (CMR, Polyn, Norman, and Kahana (2009)). MATLAB scripts to run the CMR mannequin can be downloaded here. Lohnas, Polyn, and Kahana (2015) provided a serious overhaul of the earlier CMR model, going past earlier modeling of individual lists to clarify the interplay between recollections realized throughout a number of experiences . In their CMR2 model, memory accumulates throughout multiple experimental lists, and temporal context is used both to focus retrieval on a target checklist and to censor retrieved info when its match to the current context indicates that it was realized in a non-target record.

The model simultaneously accounts for a variety of intralist and interlist phenomena, together with the sample of prior-listing intrusions observed in free recall, construct-up of and launch from proactive interference, and the power to selectively target retrieval of items on particular prior lists (Jang & Huber, 2008; Shiffrin, 1970). Healey and Kahana (2015) used CMR2 to better understand why memory tends to get worse as we age. By fitting CMR2 to the efficiency of individual younger and older adults, they identified deficits in 4 important processes: sustaining attention across a examine episode, producing retrieval cues, resolving competition, and screening for inaccurate reminiscences (intrusions). Healey and Kahana additionally prolonged CMR2 to simulate a recognition memory activity using the identical mechanisms the free recall model makes use of to reject intrusions. Without fitting any extra parameters, the mannequin accounts for age differences in recognition Memory Wave accuracy. Confirming a prediction of the mannequin, free recall intrusion charges correlate positively with recognition false alarm charges.

MATLAB scripts to run the CMR2 mannequin can be downloaded right here. Cohen and Kahana (2021, Psychological Evaluation) introduced CMR3 to incorporate the vital role of arousal and emotion in human memory. They applied their model to various phenomena together with the function of emotion in organizing memories, state-dependent and temper congruent memory, the function of emotional experiences in producing persistent temper states, including depression, and a novel account of PTSD and its treatment. A review of this line of analysis appeared in Kahana (2020), Computational Fashions of Memory Search, in the Annual Assessment of Psychology. Python code that runs CMR2 and CMR3 could also be downloaded from the lab's publication web page. We examine the neurophysiology of episodic memory with electrocorticographic (ECoG) and single neuron recordings from neurosurgical patients who have had electrodes surgically implanted on the cortical floor of the mind or within the medial temporal lobes (including hippocampus) as a part of the clinical means of localizing seizure foci. One focus of this research is to find out the oscillatory correlates of profitable episodic memory formation and retrieval. Analyses of such recordings have proven that top-frequency exercise (HFA, 70-a hundred and fifty Hz) enhance while contributors are learning words that they'll successfully, as opposed to unsuccessfully, recall.

EEG voltage traces for each item and measure when and where energy at specific frequencies modifications. Successful memory formation is associated with will increase in HFA in left lateral temporal lobe, medial temporal lobe, and left prefrontal cortex. The same analyses may be performed on gadgets throughout recall to evaluate when and where memories are retrieved. Profitable memory retrieval is related to will increase in gamma band activity within the left neocortex and hippocampus in addition to increases in theta band (4 -eight Hz) activity in proper temporal lobe. The power to reinstate this contextual info throughout memory search has been considered a hallmark of episodic, or occasion-based mostly, memory. In Manning et al., 2011, we sought to find out whether contextual reinstatement may be noticed in electrical signals recorded from the human brain throughout episodic recall. We examined ECoG exercise from sixty nine neurosurgical patients as they studied and recalled lists of words in a delayed free recall paradigm (Fig. 4A), and computed similarity between the ECoG patterns recorded simply prior to every recall with those recorded after the affected person had studied every word.