Evolving Plasticity for Autonomous Learning under Changing Environmental Conditions

A fundamental aspect of learning in biological neural networks is the plasticity property which allows them to modify their configurations during their lifetime. Hebbian learning is a biologically plausible mechanism for modeling the plasticity property in artificial neural networks (ANNs), based on the local interactions of neurons. However, the emergence of a coherent global learning behavior from local Hebbian plasticity rules is not very well understood. The goal of this work is to discover interpretable local Hebbian learning rules that can provide autonomous global learning. To achieve this, we use a discrete representation to encode the learning rules in a finite search space. These rules are then used to perform synaptic changes, based on the local interactions of the neurons. We employ genetic algorithms to optimize these rules to allow learning on two separate tasks (a foraging and a prey-predator scenario) in online lifetime learning settings. The resulting evolved rules converged into a set of well-defined interpretable types, that are thoroughly discussed. Notably, the performance of these rules, while adapting the ANNs during the learning tasks, is comparable to that of offline learning methods such as hill climbing.Comment: Evolutionary Computation Journa

Neuromorphic, Digital and Quantum Computation with Memory Circuit Elements

Memory effects are ubiquitous in nature and the class of memory circuit elements - which includes memristors, memcapacitors and meminductors - shows great potential to understand and simulate the associated fundamental physical processes. Here, we show that such elements can also be used in electronic schemes mimicking biologically-inspired computer architectures, performing digital logic and arithmetic operations, and can expand the capabilities of certain quantum computation schemes. In particular, we will discuss few examples where the concept of memory elements is relevant to the realization of associative memory in neuronal circuits, spike-timing-dependent plasticity of synapses, digital and field-programmable quantum computing

Regulation of circuit organization and function through inhibitory synaptic plasticity

Diverse inhibitory neurons in the mammalian brain shape circuit connectivity and dynamics through mechanisms of synaptic plasticity. Inhibitory plasticity can establish excitation/inhibition (E/I) balance, control neuronal firing, and affect local calcium concentration, hence regulating neuronal activity at the network, single neuron, and dendritic level. Computational models can synthesize multiple experimental results and provide insight into how inhibitory plasticity controls circuit dynamics and sculpts connectivity by identifying phenomenological learning rules amenable to mathematical analysis. We highlight recent studies on the role of inhibitory plasticity in modulating excitatory plasticity, forming structured networks underlying memory formation and recall, and implementing adaptive phenomena and novelty detection. We conclude with experimental and modeling progress on the role of interneuron-specific plasticity in circuit computation and context-dependent learning

Evaluating a Novel Photochemical Tool for Labeling and Tracking Live, Endogenous Calcium-Permeable AMPARs

The purpose of this research is to advance development of a photochemical tool designed to probe the role of ionotropic glutamate receptor signaling in neurodegenerative processes, and to delve more deeply into the biological processes underlying the role of these receptors in signaling and memory formation. This ligand-targeted nanoprobe was designed and developed in our lab to label endogenous calcium-permeable AMPARs (CP-AMPARs) in live cells with minimal disruption to native receptor activity. Nanoprobe is designed to use naphthyl acetyl spermine (NASPM) as a photocleavable ligand to target and covalently label native CP-AMPARs with a non-perturbing, fluorescent marker that then allows observation of these receptors using standard epifluorescence microscopy. My contribution to this work, outlined in the aims below, is the characterization of nanoprobe using electrophysiology and fluorescent imaging to evaluate its effectiveness as an endogenous CP-AMPAR label on live neurons. Aim 1: To use whole cell patch clamp electrophysiology to test the labeling of CP-AMPARs with nanoprobe by recording changes in glutamate-evoked current through heterologously expressed GluA1-L497Y homomultimers during, pre- and post- nanoprobe labeling. Aim 2: To use fluorescent imaging to evaluate nanoprobe labeling of glutamate receptors endogenously expressed in hippocampal neurons by co-labeling nanoprobe-treated neurons with traditional antibodies to AMPAR and synaptic targets. Aim 3: To use nanoprobe to detect endogenously expressed CP-AMPARs on live neurons during the course of neuron development. Live neuronal cultures will be imaged before and after labeling with nanoprobe in young dissociated cultures (DIV 1-2) and in maturing cultures (DIV 14-17). Conclusions: Whole cell patch clamp electrophysiology results provide evidence that nanoprobe will label CP-AMPARs in a minimally-perturbing fashion that allows the receptors to resume normal activity after photolytic-release of ligand as designed. Fixed cell imaging of CP-AMPAR nanoprobe labeling was largely ineffective, and live cell imaging was not conclusive, but provided supporting evidence that nanoprobe targets and labels NASPM-sensitive endogenous glutamate receptors on live dissociated hippocampal neuron