Neuronal Representation of Memories
BIG NEWS!!! Our novel result about how noise correlations limits spatial accuracy in hippocampal code has been published in Nature Communications
We focus our research on the exploration of how information is encoded long-term in the brain. We are specially interested in the formation and recall of associative spatial memories. Our different research projects try to bridge the gap between the extensive knowledge about how information is encoded in the activity of neurons in the medial temporal lobe (place-cells, grid-cells, head-direction-cells, etc), with their possible role in memory encoding.
Modern experimental results are giving support to the need of recording large population of neuronal electrophysiological activity with animal behavior, as the current most accurate way of investigating how information is encoded in the brain. For this reason we are utilizing the most advanced methodologies to maximize the access to this type of neuronal information.
Our research is based on three main methodologies:
1. Unsupervised high-throughput behavioral tasks
2. Electrophysiology and calcium imaging recordings of large neuronal population in freely moving mice
3. Machine learning techniques to correlate neuronal population activity with animal behavior
1. High-throughput behavioral tasks
a. Fully computer controlled freely moving behavioral task to measure spatial memory recall, during neural activity recordings
b. Parametric & hight number of trials test to measure learning and recall of spatial memories
c. Ability to measure memory accuracy over many trials per session (~20), for many daily sessions per animal (~200).
2. Neuronal recordings in freely moving mice
We perform tetrode based electrophysiological recordings and miniaturized 1-photon calcium-imaging recordings in freely moving mice.
See more details in our electrophysiology and calcium imaging published experiments
Freely moving calcium imaging recordings of hippocampal CA1 pyramidal cells during an association task (w/ T. Rogerson)
Freely moving calcium imaging recordings of entorhinal cortex during a foraging task
3. Neuronal population data analysis
a. We utilized neuronal decoders to measure the amount of information encoded in the activity of the cells, to track learning over time at the population level (see reference).
b. We use information theory in high dimension neuron population vector spaces, to test which sensory input features better explain neuronal responses to understand how information is encoded (see reference).
Decoding animal position using population activity from hippocampal CA1 neurons