The Memory Dynamics lab aims at unraveling the mechanisms of memory in the brain, with a strong emphasis on the memory consolidation process, that is, the reinforcement and reprocessing of memory that takes place during sleep, and other periods of inactivity.
To reach this goal, we take a multidisciplinary approach, based on the diverse expertise of the two co-PI, Francesco Battaglia (neurophysiology, computational neuroscience, neuroengineering) and Lisa Genzel (clinician, behavioral neuroscience, sleep physiology), and of the other core members (currently 8 PhD students, 1 postdoc).
We offer Master and Bachelor students a vast spectrum of internships, tailored to the student interests, background and desired internship duration. Typically, students participate in a larger research project, but have also the possibility of carrying out personalized projects. In the last years our lab hosted 30-40 students per year, from 23 different nationalities. Students are divided in teams according to their focus (“Coders”, doing data analysis, machine learning, computer vision, “Rats” and “Mice” running behavioral and neurophysiological experiments, “Brains” focusing on histological and molecular techniques). We have successfully (co)directed students from technical universities such as TU/e and TU Delft, who also got a first introduction to neuroscience.
Here are some examples of potential internship subjects for students with an engineering/quantitative background:
Computational/data analysis. With our experimental techniques, we can monitor the activity of up to hundreds of neurons at the same time, during active behavior. That allows us to understand how information is processed in brain circuits. The data we obtain is extremely complex and high dimensional. We develop mathematical and statistical techniques to make sense of this activity and understand how it supports cognitive processes
- Finding patterns in neural activity. Sometimes, finding the patterns of neural activity that contribute to cognitive processes is like finding a “needle in a haystack”, as the patterns are hidden in a sea of noise. You will expand and apply a clustering techniques that we recently developedto extract patterns of activity related to memory from rodent brain activity (https://journals.plos.org/ploscompbiol/article/comments?id=10.1371/journal.pcbi.1006283 the first author of this paper was one of our Master students)
- Extract brain activity from optical signals. One of the most advanced methods for monitoring brain activity uses genetically inserted fluorescent indicators to reveal the activity of neurons by imaging the tissue with a miniature microscope The simultaneous activity of hundreds of neurons can such be discriminated from the resulting “movies”. You will develop and apply machine learning techniques to identify single neurons from these videos, and relate them back to the animal’s behavior.
Computer vision/Automated analysis of behavior
In our lab we make wide use of sophisticated behavioral experiments and accumulated a very large repertoire of videos with rodents performing various tasks (~3000 hours). At this scale automated analysis using machine learning and “big data” approaches is of the essence in order to extract subtle behavioral features that can inform about underlying cognitive and neural processes, as well as correlating with simultaneously recorded high resolution measurements of neural activity
- Social interactions in mice. Many of the cognitive abilities of an animal are expressed in the social domain. To investigate how the brain generate diverse social behaviors, we recorded brain activity while two mice interacted with each other for the first time. You will use computer vision methods (see e.g. http://www.mousemotorlab.org/deeplabcut) to analyze videos and produce “ethograms” of the animal behavior.
- Behavioral abnormalities in a mouse model of intellectual disabilities. Disorders of the nervous system may be studied in genetically modified mice that have gene mutations related to the disease. We have tested mouse models of Kleefstra syndrome, a form of intellectual disability related also to Autistic Spectrum Disorder in behavioral tasks. You will analyze the video to determine which features of behavior differentiate the diseased mice from normal ones
- 3D Characterization of mouse behavior.A more detailed analysis of behavior may be obtained from “quasi-3D” images obtained with depth sensing cameras such as the Microsoft Kinect. You will set up Kinect imaging and behavioral analysis in our lab, using methods described in http://datta.hms.harvard.edu/research/behavioral-analysis/and will combine the results with the outcome of brain activity monitoring.
Electronics/Neuroengineering/experimental software development
In the lab, we have multiple advanced techniques to monitor brain activity, including multi-electrode recordings, C-MOS silicon probes, and optical imaging. We are continuously developing new approaches to monitor and/or modify brain activity.
- Closed-loop brain stimulation. Brain stimulation with electrical or optical (optogenetic) methods can have stunning effects on motor, sensory and cognitive functions. The effect can be strongly enhanced if the stimulation is precisely timed on the emergence of precise neural patterns. You will develop hardware (mostly FPGA programming) for the online analysis of neural data and pattern detection to drive stimulators.
- Simultaneous optical and electrical monitoring of brain activity. Micro-endoscopes (http://miniscope.org) optically monitor the activity of brain structures with the help of genetically encoded calcium indicators. The microscope uses a cylindrical GRIN (Gradient Index) lens to reach the structure. You will develop a method to combine the lens with electrophysiological probes for simultaneous optical and electrophysiological recording.
Other subjects are also available. You can find more information on our website http://www.memorydynamics.org. If you have questions you can e-mail the PIs at email@example.com(Francesco Battaglia) or firstname.lastname@example.org(Lisa Genzel)