Biological learning curves outperform existing ones in artificial intelligence algorithms.

Recently, deep learning algorithms have outperformed human experts in various tasks across several domains; however, their characteristics are distant from current knowledge of neuroscience. The simulation results of biological learning algorithms presented herein outperform state-of-the-art optimal learning curves in supervised learning of feedforward networks. The biological learning algorithms comprise asynchronous input signals with decaying input summation, weights adaptation, and multiple outputs for an input signal.

Stationary log-normal distribution of weights stems from spontaneous ordering in adaptive node networks.

Experimental evidence recently indicated that neural networks can learn in a different manner than was previously assumed, using adaptive nodes instead of adaptive links. Consequently, links to a node undergo the same adaptation, resulting in cooperative nonlinear dynamics with oscillating effective link weights. Here we show that the biological reality of stationary log-normal distribution of effective link weights in neural networks is a result of such adaptive nodes, although each effective link weight varies significantly in time.

Dendritic Learning as a Paradigm Shift in Brain Learning.

Experimental and theoretical results reveal a new underlying mechanism for fast brain learning process, dendritic learning, as opposed to the misdirected research in neuroscience over decades, which is based solely on slow synaptic plasticity. The presented paradigm indicates that learning occurs in closer proximity to the neuron, the computational unit, dendritic strengths are self-oscillating, and weak synapses, which comprise the majority of our brain and previously were assumed to be insignificant, play a key role in plasticity. The new learning sites of the brain call for a reevaluation of current treatments for disordered brain functionality and for a better understanding of proper chemical drugs and biological mechanisms to maintain, control and enhance learning.

Adaptive nodes enrich nonlinear cooperative learning beyond traditional adaptation by links.

Physical models typically assume time-independent interactions, whereas neural networks and machine learning incorporate interactions that function as adjustable parameters. Here we demonstrate a new type of abundant cooperative nonlinear dynamics where learning is attributed solely to the nodes, instead of the network links which their number is significantly larger. The nodal, neuronal, fast adaptation follows its relative anisotropic (dendritic) input timings, as indicated experimentally, similarly to the slow learning mechanism currently attributed to the links, synapses.

Oscillations in networks of networks stem from adaptive nodes with memory.

We present an analytical framework that allows the quantitative study of statistical dynamic properties of networks with adaptive nodes that have memory and is used to examine the emergence of oscillations in networks with response failures. The frequency of the oscillations was quantitatively found to increase with the excitability of the nodes and with the average degree of the network and to decrease with delays between nodes. For networks of networks, diverse cluster oscillation modes were found as a function of the topology.