Neuronal Representation of Memories

BIG NEWS!!! Heading direction with respect to a reference point modulates place-cell activity, it's been published in Nature Communications

Our Goal:

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. With our different research projects we are trying to bridge the gap between the extensive knowledge that the neuroscience community has on the spatial information encoded in the activity of neurons in the medial temporal lobe (place-cells, grid-cells, head-direction-cells, etc), and the role in memory formation and recall of this brain region.

Our research is based on three methodologies:

1. Unsupervised 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).

for more details see our current publication

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 (w/ D. Tomàs)