Maria Shippi

Maria Shippi

Research Interests

I am extremely interested in the use of computational models in cognitive neuroscience.

From a biological perspective, I am currious to understand how neuronal proceses give rise to cognitive functions such as memory, attention, problem solving and decision makinge. What puzzles me the most is the incredible ability of humans and animals to build new memories for a long period of time. What are the underlying mechanisms which make a memory to be stored for long period of time and which brain areas store these long term memories. Other related questions include the explanation of why some memories last only for a short period of time while others for a life time? What are the parameters that influence the decision of which memories will be stored for a short period of time, and which memories are stored for a long period of time. Do previous expiriences and attention influence the selection of our future long term memories? Does sleep effects the selection of which memories will be stored in long term? These questions address very complicated issues and they need an interdisciplinary approached to be approached including studies in a molecular and cellular level, systems level and the behavioral level.

Coming though from a computer science background, I am extremely fascinating in using mathematical models to describe cognitive functions that occur in biology and implemented these mathematical models in the form of computer programs, which translate abstract mathematics into explicit simulations of the biological experiments. Using computational models in neuroscience, can not only lead to further predictions that can guide further experimental work but can also reveal possible answers to questions that are difficult to addressed biologically.

In my opinion, expiriencing both biological and computational studies leads to a better understanding of how these two different areas should be combined together in order to lead to biologically realistic results. Thus, through my PhD, I aim to work with both, experiments and computational models in order to discover the mechanisms for long term memory and the influence of the prior knowledge in that processes.

  Computational modelling of memory retention from synapse to behaviour
van Rossum, MCW & Shippi, M 2013, 'Computational modelling of memory retention from synapse to behaviour' Journal of Statistical Mechanics: Theory and Experiment, vol 2013, no. 03, P03007. DOI: 10.1088/1742-5468/2013/03/P03007

One of our most intriguing mental abilities is the capacity to store information and recall it from memory. Computational neuroscience has been influential in developing models and concepts of learning and memory. In this tutorial review we focus on the interplay between learning and forgetting. We discuss recent advances in the computational description of the learning and forgetting processes on synaptic, neuronal, and systems levels, as well as recent data that open up new challenges for statistical physicists.

General Information
Organisations: Institute for Adaptive and Neural Computation .
Authors: van Rossum, Mark C. W. & Shippi, Maria.
Number of pages: 13
Publication Date: 12 Mar 2013
Publication Information
Category: Article
Journal: Journal of Statistical Mechanics: Theory and Experiment
Volume: 2013
Issue number: 03
ISSN: 1742-5468
Original Language: English
DOIs: 10.1088/1742-5468/2013/03/P03007
  Soft-bound synaptic plasticity increases storage capacity
van Rossum, MCW, Shippi, M & Barrett, AB 2012, 'Soft-bound synaptic plasticity increases storage capacity' PLoS Computational Biology, vol 8, no. 12, e1002836. DOI: 10.1371/journal.pcbi.1002836
Accurate models of synaptic plasticity are essential to understand the adaptive properties of the nervous system and for realistic models of learning and memory. Experiments have shown that synaptic plasticity depends not only on pre- and post-synaptic activity patterns, but also on the strength of the connection itself. Namely, weaker synapses are more easily strengthened than already strong ones. This so called soft-bound plasticity automatically constrains the synaptic strengths. It is known that this has important consequences for the dynamics of plasticity and the synaptic weight distribution, but its impact on information storage is unknown. In this modeling study we introduce an information theoretic framework to analyse memory storage in an online learning setting. We show that soft-bound plasticity increases a variety of performance criteria by about 18% over hard-bound plasticity, and likely maximizes the storage capacity of synapses.
General Information
Organisations: Neuroinformatics DTC.
Authors: van Rossum, Mark C W, Shippi, Maria & Barrett, Adam B.
Keywords: (Information Theory, Learning, Long-Term Potentiation, Memory, Models, Theoretical, Neuronal Plasticity. )
Number of pages: 11
Publication Date: 2012
Publication Information
Category: Article
Journal: PLoS Computational Biology
Volume: 8
Issue number: 12
ISSN: 1553-734X
Original Language: English
DOIs: 10.1371/journal.pcbi.1002836