The gradient clusteron: A model neuron that learns to solve classification tasks via dendritic nonlinearities, structural plasticity, and gradient descent.

Synaptic clustering on neuronal dendrites has been hypothesized to play an important role in implementing pattern recognition. Neighboring synapses on a dendritic branch can interact in a synergistic, cooperative manner via nonlinear voltage-dependent mechanisms, such as NMDA receptors. Inspired by the NMDA receptor, the single-branch clusteron learning algorithm takes advantage of location-dependent multiplicative nonlinearities to solve classification tasks by randomly shuffling the locations of "under-performing" synapses on a model dendrite during learning ("structural plasticity"), eventually resulting in synapses with correlated activity being placed next to each other on the dendrite. We propose an alternative model, the gradient clusteron, or G-clusteron, which uses an analytically-derived gradient descent rule where synapses are "attracted to" or "repelled from" each other in an input- and location-dependent manner. We demonstrate the classification ability of this algorithm by testing it on the MNIST handwritten digit dataset and show that, when using a softmax activation function, the accuracy of the G-clusteron on the all-versus-all MNIST task (~85%) approaches that of logistic regression (~93%). In addition to the location update rule, we also derive a learning rule for the synaptic weights of the G-clusteron ("functional plasticity") and show that a G-clusteron that utilizes the weight update rule can achieve ~89% accuracy on the MNIST task. We also show that a G-clusteron with both the weight and location update rules can learn to solve the XOR problem from arbitrary initial conditions

Statistical Learning of Melodic Patterns Influences the Brain's Response to Wrong Notes

The theory of statistical learning has been influential in providing a framework for how humans learn to segment patterns of regularities from continuous sensory inputs, such as speech and music. This form of learning is based on statistical cues and is thought to underlie the ability to learn to segment patterns of regularities from continuous sensory inputs, such as the transition probabilities in speech and music. However, the connection between statistical learning and brain measurements is not well understood. Here we focus on ERPs in the context of tone sequences that contain statistically cohesive melodic patterns. We hypothesized that implicit learning of statistical regularities would influence what was held in auditory working memory. We predicted that a wrong note occurring within a cohesive pat tern (within-pattern deviant) would lead to a significantly larger brain signal than a wrong note occurring between cohesive patterns (between-pattern deviant), even though both deviant types were equally likely to occur with respect to the global tone sequence. We discuss this prediction within a simple Markov model framework that learns the transition probability regularities within the tone sequence. Results show that signal strength was stronger when cohesive patterns were violated and demonstrate that the transitional probability of the sequence influences the memory basis for melodic patterns. Our results thus characterize how informational units are stored in auditory memory trace for deviance detection and provide new evidence about how the brain organizes sequential sound input that is useful for perception