Supervisor: Matias J. Ison (School of Psychology)
Co-supervisor: Stephen Coombes (School of Mathematical Sciences)

Description:

This project combines computational modelling with machine learning and state-of-the-art analyses techniques.

Our recent experimental work with single neuron recordings from epilepsy patients implanted with depth electrodes for clinical reasons showed that individual neurons in the human brain change their firing to link associations when a new memory is formed (Ison, Quiroga & Fried, Neuron 2015). Shortly after, and challenging the standard memory consolidation model, we showed that neurons in the medial temporal lobe (MTL) are involved in long-term representation of associations between items (DeFalco, Ison, Fried & Quiroga, Nature Communications, 2016). These findings, together with recent evidence from work on rodents and non-human primates, suggest that memories are encoded in sparse assemblies of neurons.

In collaboration with TU Graz (Austria), we have recently implemented a neural network model that includes short-term plasticity as well as spike-timing-dependent plasticity of synapses aiming at testing these experimental findings (Pokorny, Ison, et al., submitted 2018).
The proposed project aims at building data-driven computational models of neurons encoding new memories. It will address the fundamental question of what type of assembly can represent short and long-term information by incorporating a consolidation phase. We hope that the outcomes of this research will lead to deeper insight into how the brain implements learning and encodes memories.

Requirements:

• Candidates are expected to have either a strong quantitative background in physics, mathematics, computer science or engineering and willingness to learn about neuroscience or a background in neuroscience, biology or experimental psychology and a serious interest in programming (MATLAB and/or Python).

Reading:

• Ison, M. J., Quiroga, R. Q., & Fried, I. (2015). Rapid Encoding of New Memories by Individual Neurons in the Human Brain. Neuron 87 (1), 220-230. DOI: 10.1016/j.neuron.2015.06.016
• De Falco, E., Ison, M. J., Fried, I., & Quiroga, R. Q. (2016).
  Long-term coding of personal and universal associations underlying the memory web in the human brain. Nature Communications, 7, 13408. DOI: 10.1038/ncomms13408
• Rey, H. G., De Falco, E., Ison, M. J., C., Valentin, A., Alarcon, G., Selway, R., Richardson, M. P., & Quian Quiroga, R. (2018). Encoding of long-term associations through neural unitization in the human medial temporal lobe. Nature communications, 9(1), 4372. DOI: https://doi.org/10.1038/s41467-018-06870-2
• Komorowski, R. W., Manns, J. R., & Eichenbaum, H. (2009). Robust conjunctive item–place coding by hippocampal neurons parallels learning what happens where. Journal of Neuroscience, 29(31), 9918-9929. DOI: https://doi.org/10.1523/JNEUROSCI.1378-09.2009
• Josselyn, S. A., Köhler, S., & Frankland, P. W. (2015). Finding the engram. Nature Reviews Neuroscience, 16(9), 521.
  DOI: https://doi.org/10.1038/nrn4000

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