Periodic Reporting for period 3 - LeaRNN (Principles of Learning in a Recurrent Neural Network)
Período documentado: 2022-09-01 hasta 2024-02-29
We have also set up two patch-clamp recording rigs and generated all the necessary transgenic fly stocks for activating individual MBONs (using LexA to drive CSChrimson expression) while recording from postsynaptic neurons (using GAL4 to drive with GFP expression) or to record from DANs while activating their presynaptic neurons. We are currently testing the functional connections in the learning circuit and characterising in detail the learning-induced changes in the functional connectivity in the learning circuit.
Aim 2 progress: We have identified the features encoded by some of the key feedback neurons in the learning circuit. We have discovered that they integrate input from neurons that encode positive and negative learnt values as well as positive and negative innate values. They compare odour drive to positive and negative value neurons and bidirectionally encode integrated predicted values of stimuli. These neurons promote actions based on the predictions they encode and they also feedback to DANs to regulate future learning. We have published these findings in Eschbach et al. eLife 2021, "Circuits for integrating learnt and innate valences in the insect brain."
We have also set up a multi-view light sheet microscope whole-brain imaging of neural activity before, during and after learning to systematically discover the efatures encoded by all the neurons in the learning circuit.
Aim 3 progress: In collaboration with Prof. Ashok Litvin-Kumar, we have developed a connectivity-constrained model of the learning circuit constrained by the structural synaptic connectivity map, published in Eschbach et al. 2020, Nature neuroscience: "Recurrent architecture for adaptive regulation of learn in the insect brain." We are currently gathering functional data to constrain the model further with functional connectivity data. We have also developed a novel automated high-throughput learning rig for exploring new learning tasks to be able to rapidly test the models' predictions about the roles of specific feedback motifs in learning.