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Institute of Cognitive Neuroscience

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NEUROMEM

NEUROMEM

A Neurocomputational Model of Episodic Memory

ERC Advanced Grant (ERC-2015-AdG). Grant agreement ID: 694779


Objective 

Our memories define us, and their disruption in psychiatric and neurological conditions can be devastating. However, how we are able, e.g. to remember our wedding day and re-imagine the scene that was around us, remains one of the great mysteries of the human mind. NEUROMEM is an integrated experimental and computational attempt at a fundamental breakthrough in this problem. Building on recent insights into how environmental location and orientation is encoded by neurons in the mammalian brain, I aim to develop a mechanistic understanding of how events we experience are stored, recalled and imagined, i.e. a neurocomputational model of how specific memories result from patterns of activity in neuronal populations.
NEUROMEM will provide mechanistic answers to 3 long-standing questions: 1) What is the link between memory and space, and role of spatial context in re-imagining episodes? 2) How are the multiple diverse elements of complex life-like events recollected together? 3) How can remembered events be read-out as visuospatial imagery? Work will comprise psychological and functional neuroimaging experiments using sophisticated designs including use of virtual reality, and corresponding simulations of how such behaviour can be driven by neuronal activity. The computational modelling will directly contact neurophysiological data such as the firing of place and grid cells in the hippocampal formation, and provide quantitative behavioural predictions, while neuroimaging provides a read out of population activity during this processing in the human brain.
NEUROMEM will generate new hypotheses and explanations at the cognitive level, of interest to all scholars of the complexity of the human mind, and allow neurophysiological interpretation of behavioural data - providing a vital link between cognitive theory and neuroimaging and neurological data. Its implications extend beyond memory, including the mechanism for imagining views that have not been experienced.

Associated Publications

  1. Yan Y, Burgess N, Bicanski A (2021) A model of head direction and landmark coding in complex environments. PLoS Computational Biology, 17(9): e1009434. https://doi.org/10.1371/journal.pcbi.1009434.

  2. Geerts JP, Chersi F, Stachenfeld KL, Burgess N (2020) A general model of hippocampal and dorsal striatal learning and decision making. PNAS 117: 31427-37. Click here for pdf

  3. Bicanski A, Burgess N (2020) Neuronal vector coding in spatial cognition. Nature Reviews Neuroscience 21: 453–70. Click here for pdf

  4. Ikhsan SNBM, Bisby JA, Bush D, Steins DS, Burgess N (2020) Inference within episodic memory reflects pattern completion. Quart. J. Exp. Psychology. 73: 2047–70. Click here for pdf

  5. Zotow E, Bisby JA, Burgess N (2020) Behavioral evidence for pattern separation in human episodic memory. Learning & Memory 27: 301-309. Click here for pdf.

  6. Bush D, Burgess N (2020) Advantages and detection of phase coding in the absence of rhythmicity. Hippocampus 30: 745–62. Click here for pdf  

  7. Adams* RA, Bush* D, Zheng* F, Meyer SS, Kaplan R, Orfanos S, Marques TR, Howes OD, Burgess N (2020) Impaired theta phase coupling underlies frontotemporal dysconnectivity in schizophrenia. Brain 143: 1261–1277. Click here for pdf

  8. Bush D, Burgess N (2020) Advantages and detection of phase coding in the absence of rhythmicity. Hippocampus 30: 745-62: Click here for pdf 

  9. Bicanski A, Burgess N (2019) A computational model of visual recognition memory via grid cells. Curr Biol 29: 979-90. Click here for pdf.

  10. Edvardsen V, Bicanski A, Burgess N (2019) Navigating with grid and place cells in cluttered environments. Hippocampus 1–13. https://doi.org/10.1002/hipo.23147

  11. Horlyck LD, Bisby JA, King JA, Burgess N. (2019). Wakeful rest compared to vigilance reduces intrusive but not deliberate memory for traumatic videos  Scientific Reports 9:13403. Click here for pdf.
  12. Howett D, Castegnaro A, Krzywicka K, Hagman J, Marchment D, Henson RNA, Rio M, King JA, Burgess N, Chan D. (2019) Differentiation of mild cognitive impairment using an entorhinal cortex-based test of virtual reality navigation. Brain 142:1751–66  Click here for pdf
  13. Bedder RL, Bush D, Banakou D, Peck T, Slater M, Burgess N (2019) A mechanistic account of bodily resonance and implicit bias. Cognition 184. Click here for pdf.

  14. Laptev D, Burgess N (2019) Neural dynamics indicate parallel integration of environmental and self-motion information by place and grid cells. Frontiers in Neural Circuits 13: 59. Click here for pdf. 

  15. Bicanski A, Burgess N (2018) A neural-level model of spatial memory and imagery. eLife 2018;7:e33752. Click here for pdf

  16. Suarez-Jimenez B, Bisby JA, Horner AJ, King JA, Pine DS, Burgess N (2018) Linked networks for learning and expressing location-specific threat. P.N.A.S. doi: 10.1073/pnas.1714691115. Click here for pdf.

  17. Bisby JA, Horner AJ, Bush D, Burgess N (2017) Negative emotional content disrupts the coherence of episodic memories. J Exp Psychol: Gen, 147 243-256. doi: 10.1037/xge0000356. Click here for pdf

  18. Bisby JA, Burgess N (2017) Differential effects of negative emotion on memory for items and associations, and their relationship to intrusive imagery. Curr Opin Behav Sci, 17, 124-132. doi: 10.1016/j.cobeha.2017.07.012 Click here for pdf

  19. Bush D, Bisby JA, Bird CM, Gollwitzer S, Rodionov R, Diehl B, McEvoy AW, Walker MC, Burgess N (2017) Human hippocampal theta power indicates movement onset and distance travelled. P.N.A.S. 114: 12297. Click here for pdf.