Force Learning
FORCE learning
Dr. Fleur Zeldenrust
We are looking for a postdoctoral researcher to study the effects of neuromodulators in biologically realistic networks and learning tasks in the Vidi project 'Top-down neuromodulation and bottom-up network computation, a computational study'. You will use cellular and behavioural data gathered by our department over the previous five years on dopamine, acetylcholine and serotonin in mouse barrel cortex, to bridge the gap between single cell, network and behavioural effects. The aim of this project is to explain the effects of neuromodulation on task performance in biologically realistic spiking recurrent neural networks (SRNNs). You will use biologically realistic learning frameworks, such as force learning, to study how network structure influences task performance. You will use existing open source data to train a SRNN on a pole detection task (for rodents using their whiskers) and incorporate realistic network properties of the (barrel) cortex based on our lab's measurements. Next, you will incorporate the cellular effects of dopamine, acetylcholine and serotonin that we have measured into the network, and investigate their effects on task performance. In particular, you will research the effects of biologically realistic network properties (balance between excitation and inhibition and the resulting chaotic activity, non-linear neuronal input-output relations, patterns in connectivity, Dale's law) and incorporate known neuron and network effects. You will build on the single cell data, network models and analysis methods available in our group, and your results will be incorporated into our group's further research to develop and validate efficient coding models of (somatosensory) perception. We are therefore looking for a team player who can collaborate well with the other group members, and is willing to both learn from them and share their knowledge.
The centrality of population-level factors to network computation is demonstrated by a versatile approach for training spiking networks
Neural activity is often described in terms of population-level factors extracted from the responses of many neurons. Factors provide a lower-dimensional description with the aim of shedding light on network computations. Yet, mechanistically, computations are performed not by continuously valued factors but by interactions among neurons that spike discretely and variably. Models provide a means of bridging these levels of description. We developed a general method for training model networks of spiking neurons by leveraging factors extracted from either data or firing-rate-based networks. In addition to providing a useful model-building framework, this formalism illustrates how reliable and continuously valued factors can arise from seemingly stochastic spiking. Our framework establishes procedures for embedding this property in network models with different levels of realism. The relationship between spikes and factors in such networks provides a foundation for interpreting (and subtly redefining) commonly used quantities such as firing rates.