A core goal of the lab is to make machines learn more like human infants, and here is a summary of some of our recent work in this area.

Self-supervised Babies
Developmental psychologists have shown that infants are learning many things in their first year. However, linguistic understanding is primitive until the end of the year, and so their learning must be "self-supervised", in that they can learn without being explicitly taught. At present, machines are mostly taught using hand-curated datasets, which are painstakingly labelled by humans. Self-supervised learning algorithms can potentially reduce the dependence on these datasets, and so are of great interest to the machine learning community.
In an arXiv preprint​, Lorijn Zaadnoordijk from the lab, and our collaborator Tarek Besold have reviewed the developmental psychology literature to identify potential "next big thing(s)" for this area of machine learning.

Learning Semantics
Humans have a deep understanding of the world. When we recognise an object, we know what other things it is similar to and we can classify it as part of some superordinate category. This type of knowledge is called semantic knowledge. Cliona O'Doherty has been testing the idea that by observing the co-occurrences of objects in the world, infants could not just learn how to recognise things, but also learn about semantics. She has done this by setting up a computational model using a deep neural network. 
Cliona O'Doherty will present SemanticCMC - improved semantic self-supervised learning with naturalistic temporal co-occurrences at the workshop Self-supervised learning: theory and practice at Neural Information Processing Systems (NeurIPS) 2020.

How Can Random Networks Explain the Brain So Well?
A part of the brain called the inferotemporal (IT) cortex is critical for humans and other monkeys to visually recognise objects. Currently, deep neural networks are the best models of brain responses in the IT cortex of adults. It has been argued that this is because the visual features that deep neural networks learn for object recognition are the same as those IT uses. But, Anna Truzzi has been investigating a conundrum, which is that actually untrained (or random) deep neural networks also do a surprisingly good job of modelling IT activity.
Anna presented the paper "Convolutional Neural Networks as a Model of Visual Activity in The Brain: Greater Contribution of Architecture Than Learned Weights" at the workshop Bridging AI and Cognitive Science​ at the International Conference on Learning Representations (ICLR) 2020. She will also be presenting at the NeurIPS2020 workshop Shared Visual Representations in Humans and Machine Intelligence, with the title "Understanding CNNs as a model of the inferior temporal cortex: using mediation analysis to unpack the contribution of perceptual and semantic features in random and trained networks". This work is also directly relevant to neuroscientists, and was presented at the neuromatch 1.0 conference with the title "Are deep neural networks effective models of visual activity in the brain because of their architecture or training?".

References

• O'Doherty, C. and Cusack, R. (2020) "SemanticCMC - improved semantic self-supervised learning with naturalistic temporal co-occurrences" Workshop: Self-supervised learning: theory and practice, NeurIPS.
• Truzzi, A. and Cusack, R. (2020) "Convolutional Neural Networks as a Model of Visual Activity in The Brain: Greater Contribution of Architecture Than Learned Weights" Workshop: Bridging AI and Cognitive Science​, International Conference on Learning Representations.
• Truzzi, A. and Cusack, R. (2020) "Are deep neural networks effective models of visual activity in the brain because of their architecture or training?" Neuromatch conference 1.0
• Truzzi, A. and Cusack, R. (2020) "Understanding CNNs as a model of the inferior temporal cortex: using mediation analysis to unpack the contribution of perceptual and semantic features in random and trained networks" Workshop: Shared Visual Representations in Humans and Machine Intelligence, NeurIPS.
• Zaadnoordijk, L, Besold, T.R. and Cusack, R. (2020) "The Next Big Thing(s) in Unsupervised Machine Learning: Five Lessons from Infant Learning" arxiv:2009.08497.

The lab's penultimate student from Western University, Patrick Gatutsi, receiving his MSc in Neuroscience. 

Congratulations to Patrick Gatutsi, who passed is Master's defense with minor changes. He worked to develop a wearable device to measure fetal movements in response to sound, and conducted extensive evaluation in Rwanda.

It was great to see past and present PhD students from the lab in three different countries in Singapore for the Human Brain Mapping conference, along with a potential member of the next generation of neuroimagers. Thank you to everyone that came to Laura Cabral's and Chiara Caldinelli's posters. And, to Chiara for her work on the committee of the Student & postdoc special interest group.

Thanks to the organisers of the International Congress in Infant Studies in Philadelphia. Laura Cabral and I had a great time at the meeting.

Many members of the lab have published work that I'm very excited about this year. Some of the publications in 2018 include:

• Cusack, R., Linke, A.C., Zubiaurre, L. & Wild, C. (in press). Why Does Language Not Emerge Until the Second Year? Hearing Research.
• Mitchell, D.J., Cam-CAN, Cusack, R. (in press). Visual Short-Term Memory Through the Lifespan: Context and Metacognition. Psychology and Aging,preprint
• Blumenthal, A., Stojanoski, B., Martin, C., Cusack, R. & Köhler, S. (in press). Animacy and real world size shape object representations in the human medial temporal lobes. Human Brain Mapping, preprint
• Linke, A.C., Wild, C., Zubiaurre-Elorza, L., Herzmann, C., Duffy, H., Han, V.K., Lee, D.S.C., Cusack, R. (in press). Disruption to functional networks in neonates with perinatal brain injury predicts motor skills at 8 months. Neuroimage: Clinical, preprint
• Zubiaurre-Elorza, L., Linke, A.C., Herzmann, C., Wild. C.J., Duffy, H., Lee, D.S.C., Han, V.K., Cusack, R. (in press). Auditory structural connectivity during the first postnatal year. Developmental Psychobiology
• Cusack, R., McCuaig, O., Linke, A.C. (in press). Methodological Challenges in the Comparison of Infant fMRI Across Age Groups. Special issue of Developmental Cognitive Neuroscience. pdf

Take a look at the publications page for a full list of our work.

I was honoured to be featured in the Provost's Annual Review of 2016-2017 along with the other professors appointed at Trinity this year. The review also gives an overview of Trinity in 2017, including the entrance to the League of European Research Universities, about student experience and public engagement.

It has been a busy summer for the lab. In addition to getting set up at Trinity College Dublin, we've been to some fantastic conferences and met some fascinating people.

• Society for Neuroscience, Washington, DC. Look out for Laura Cabral's poster from the lab.
• Cognitive Development Society, Portland, OR. Thanks to Elika Bergelson, Michael C Frank, Rebecca Lundwall for the invitation to a workshop on Collaborative Replication in Psychology
• Brain Monitoring and Neuroprotection in the Newborn, Killarney, Ireland. It was get the chance to present to attendees from Brazil to New Zealand, congregating on the lake in beautiful Kerry. 
  
• The Auditory Cortex, Banff, Canada. Thanks to the organizing committee for the opportunity to give an invited presentation.
  
• Cognitive Computational Neuroscience, New York, NY. This was a fantastic inaugural meeting. I look forward to going back!
  
• Human Brain Mapping, Vancouver, Canada. Gets its own post, below.

Rhodri also enjoyed very much presenting and meeting people at Cambridge (MRC CBU) and Cardiff Universities (CUBRIC) in the UK.

We're excited to have been selected as organizers of a symposium at the Human Brain Mapping meeting this June. "Collect Your Thoughts: Individual Differences in the Networks Underlying Intelligence," examines how information is brought together to allow the complex, flexible cognition that is fundamental to human intelligence.  

I'm excited to be working with a fantastic group of presenters representing four countries and fittingly for International Women's Day, equally representing the genders:
Rhodri Cusack (Trinity College Dublin, Ireland) "The Fronto-Parietal Network is Maturely Connected and Influences Developing Behaviour from the First Months."
Rogier Kievit (University of Cambridge, UK) "Watershed Models of Intelligence Through the Lifespan"
Danielle Bassett (University of Pennsylvania, USA) “Charting dynamic interactions between large-scale brain networks in health and disease”
Lorina Naci (Western University, Canada) “The Neural Machinery of Conscious Cognition: Converging Evidence from Anesthesia-Induced Unconsciousness, Severe Brain Injury and Intellectual Prowess.”