The postdoctoral fellow will work on developing constraint programming methods to aid decision support in life science applications. The project will involve exploring a range of possible directions, including but not limited to: Devising specialised models and methods for solving problem substructures in the context of laboratory experiment design. Investigating the hybridisation of constraint programming and other artificial intelligence methods for reasoning and prediction, such as active learning, applied to drug discovery for brain tumours.

Thinking about the next position for your research career? I am hiring postdocs in my machine learning research group both in Helsinki, Finland and Manchester, UK. We develop new machine learning methods and study machine learning principles. Keywords include: probabilistic modelling, Bayesian inference, simulation-based inference, multi-agent RL and collaborative AI, sequential decision making and experimental design, active learning, human-in-the-loop learning and user modelling, privacy-preserving learning, Bayesian deep learning, generative models. We also solve problems of other fields with the methods – and use those problems as test benches when developing the methods. We have excellent collaborators in drug design, synthetic biology and biodesign, personalized medicine, cognitive science and human-computer interaction.

Thinking about the next position for your research career? I am hiring postdocs in my machine learning research group both in Helsinki, Finland and Manchester, UK. We develop new machine learning methods and study machine learning principles. Keywords include: probabilistic modelling, Bayesian inference, simulation-based inference, multi-agent RL and collaborative AI, sequential decision making and experimental design, active learning, human-in-the-loop learning and user modelling, privacy-preserving learning, Bayesian deep learning, generative models. We also solve problems of other fields with the methods – and use those problems as test benches when developing the methods. We have excellent collaborators in drug design, synthetic biology and biodesign, personalized medicine, cognitive science and human-computer interaction.

Visual working memory (VWM) is limited in capacity, but memorizing meaningful objects may refine this limitation. However, meaningless and meaningful stimuli usually differ perceptually and an object’s association with meaning is typically already established before the actual experiment. We applied a strict control over these potential confounds by asking observers (N=45) to actively learn associations of (initially) meaningless objects. To this end, a change detection task presented Chinese characters, which were meaningless to our observers. Subsequently, half of the characters were consistently paired with pictures of animals. Then, the initial change detection task was repeated. The results revealed enhanced VWM performance after learning, in particular for meaning-associated characters (though not quite reaching the accuracy level attained by N=20 native Chinese observers). These results thus provide direct experimental evidence that the short-term retention of objects benefits from active learning of an object’s association with meaning in long-term memory.

Mammalian babies are “sensory traps” for parents. Various sensory cues from the newborn are tremendously efficient in triggering parental responses in caregivers. We recently showed that core aspects of maternal behavior such as pup retrieval in response to infant vocalizations rely on active learning of auditory cues from pups facilitated by the neurohormone oxytocin (OT). Release of OT from the hypothalamus might thus help induce recognition of different infant cues but it is unknown what sensory stimuli can activate OT neurons. I performed unprecedented in vivo whole-cell and cell-attached recordings from optically-identified OT neurons in awake dams. I found that OT neurons, but not other hypothalamic cells, increased their firing rate after playback of pup distress vocalizations. Using anatomical tracing approaches and channelrhodopsin-assisted circuit mapping, I identified the projections and brain areas (including inferior colliculus, auditory cortex, and posterior intralaminar thalamus) relaying auditory information about social sounds to OT neurons. In hypothalamic brain slices, when optogenetically stimulating thalamic afferences to mimic high-frequency thalamic discharge, observed in vivo during pup calls playback, I found that thalamic activity led to long-term depression of synaptic inhibition in OT neurons. This was mediated by postsynaptic NMDARs-induced internalization of GABAARs. Therefore, persistent activation of OT neurons following pup calls in vivo is likely mediated by disinhibition. This gain modulation of OT neurons by infant cries, may be important for sustaining motivation. Using a genetically-encoded OT sensor, I demonstrated that pup calls were efficient in triggering OT release in downstream motivational areas. When thalamus projections to hypothalamus were inhibited with chemogenetics, dams exhibited longer latencies to retrieve crying pups, suggesting that the thalamus-hypothalamus noncanonical auditory pathway may be a specific circuit for the detection of social sounds, important for disinhibiting OT neurons, gating OT release in downstream brain areas, and speeding up maternal behavior.