Decision making and learning in the neuronal systems.

Learning in a living organism always takes place on the background of existing memories. Acquisition of new memories while preserving already existing information imposes two antagonistic requirements: for the plasticity for new learning but – at the same time – for stability to ensure persistence of the old memory. The long-term goal of this proposal is to understand how the stability vs. plasticity dilemma is resolved by neuronal systems.

 

Projects:

The role of heterosynaptic plasticity in achieving stable yet adaptable memory storage

hspLearning in a living organism always takes place on the background of existing memories. Acquisition of new memories while preserving already existing information imposes two antagonistic requirements: for the plasticity for new learning but – at the same time – for stability to ensure persistence of the old memory. The long-term goal of this proposal is to understand how the stability vs. plasticity dilemma is resolved at the level of a single neuron.

Plasticity may occur at synapses which were directly involved in the activity that caused plastic changes – homosynaptic plasticity, but also at those not active during the induction – heterosynaptic plasticity. Potential targets of heterosynaptic plasticity are much more numerous since only a fraction of the neurons’ input is active at a given time. The direction and the magnitude of these heterosynaptic changes depends on the state of the synapse: synapses with initially low release probability are usually potentiated, while synapses with initially high release probability are typically depressed or did not change. We explore the synapse-type specific predispositions for heterosynaptic plasticity that enable neocortical neurons to combine the ability for both plasticity and persistent information storage.

 

Problem solving using rewarded STDP

spirit2Rewarded spike timing dependent plasticity (STDP) has been implicated as a possible learning mechanism in a variety of brain systems. This mechanism combines unsupervised STDP that modifies synaptic strength depending on the relative timing of presynaptic input and postsynaptic spikes together with a reinforcement signal that modulates synaptic changes. The goal of this project is to combine realistic network structure, spiking neuron models and rewarded STDP to achieve efficient learning and decision making. The long-term goal of this research is apply biologically inspired neural networks to the problems of robotics and automation.

 

 

 

Additional animations accompanying paper  “Multi-layer Network Utilizing Rewarded Spike Time Dependent Plasticity to Learn a Foraging Task”, to appear in PLoS Comput Biol, 2017.

 

Untrained network

Trained network

Untrained network shows random
behaviour.
Trained network shows approaching
behaviour towards horizontal bars
and aversive to vertical bars.