Computation at the edge of chaos: phase transitions and emergent computation
Computation at the edge of chaos: phase transitions and emergent computation
The role of constraints in Hebbian learning
Neural Computation
Computational models of neuromodulation
Neural Computation
Metalearning and neuromodulation
Neural Networks - Computational models of neuromodulation
Neuromodulation and Plasticity in an autonomous robot
Neural Networks - Computational models of neuromodulation
Evolving neural networks through augmenting topologies
Evolutionary Computation
On the computational power of circuits of spiking neurons
Journal of Computer and System Sciences
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Spike-Driven Synaptic Plasticity: Theory, Simulation, VLSI Implementation
Neural Computation
The Neuromodulatory System: A Framework for Survival and Adaptive Behavior in a Challenging World
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Reward-modulated hebbian learning of decision making
Neural Computation
Solving the distal reward problem with rare correlations
Neural Computation
Solving the distal reward problem with rare correlations
Neural Computation
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Synaptic plasticity is a major mechanism for adaptation, learning, and memory. Yet current models struggle to link local synaptic changes to the acquisition of behaviors. The aim of this paper is to demonstrate a computational relationship between local Hebbian plasticity and behavior learning by exploiting two traditionally unwanted features: neural noise and synaptic weight saturation. A modulation signal is employed to arbitrate the sign of plasticity: when the modulation is positive, the synaptic weights saturate to express exploitative behavior; when it is negative, the weights converge to average values, and neural noise reconfigures the network's functionality. This process is demonstrated through simulating neural dynamics in the autonomous emergence of fearful and aggressive navigating behaviors and in the solution to reward-based problems. The neural model learns, memorizes, and modifies different behaviors that lead to positive modulation in a variety of settings. The algorithm establishes a simple relationship between local plasticity and behavior learning by demonstrating the utility of noise and weight saturation. Moreover, it provides a new tool to simulate adaptive behavior, and contributes to bridging the gap between synaptic changes and behavior in neural computation.