Efficient dendritic learning as an alternative to synaptic plasticity hypothesis

Abstract

Synaptic plasticity is a long-lasting core hypothesis of brain learning that suggests local adaptation between two connecting neurons and forms the foundation of machine learning. The main complexity of synaptic plasticity is that synapses and dendrites connect neurons in series and existing experiments cannot pinpoint the significant imprinted adaptation location. We showed efficient backpropagation and Hebbian learning on dendritic trees, inspired by experimental-based evidence, for sub-dendritic adaptation and its nonlinear amplification. It has proven to achieve success rates approaching unity for handwritten digits recognition, indicating realization of deep learning even by a single dendrite or neuron. Additionally, dendritic amplification practically generates an exponential number of input crosses, higher-order interactions, with the number of inputs, which enhance success rates. However, direct implementation of a large number of the cross weights and their exhaustive manipulation independently is beyond existing and anticipated computational power. Hence, a new type of nonlinear adaptive dendritic hardware for imitating dendritic learning and estimating the computational capability of the brain must be built.

Chemogenetic Silencing of Differentiating Cortical Neurons Impairs Dendritic and Axonal Growth

“…A recent report shows that large L5 pyramidal neurons of primary and secondary cortical areas differ in apical length, and the shorter the dendrite the more reduced is the cell's dendritic excitability and propensity of burst firing (Galloni et al, 2020). A precise regulation of the dendritic architecture is a prerequisite of neural computations, especially with regard to the newly proposed model on deep learning in even single dendritic branches (Hodassman et al, 2022).…”

Changes in dendritic complexity and spine morphology following BCG immunization in APP/PS1 mice

“…This experiment of dendritic adaptation is expected to be an alternative of the hypothesis of synaptic plasticity. 56 Dendritic adaptation is an important new methodology in the study of brain learning; However, the methodology of dendritic adaptation still need to be refined and further verified.…”

Hypnotherapy and IBS: Implicit, long-term stress memory in the ENS?

“…However, there are studies that do not suggest such a high degree of neurogenesis [ 20 , 56 ] in the ENS, as reported by Kulkarni et al [ 55 ]; and we should consider that the origin of differentiating cells is currently under debate. In addition, the latest studies question the 70-year-old paradigm, the Hebbian hypothesis of neuroscience plasticity, according to which the brain learns by modifying the strength of synapses [ 57 , 58 ]. Hodassman et al [ 57 ] proposed that the neuron is not a binary unit that can fire or not and that a single neuron can realize learning algorithms that previously required an artificially complex network of thousands of interconnected neurons and synapses.…”

“…In addition, the latest studies question the 70-year-old paradigm, the Hebbian hypothesis of neuroscience plasticity, according to which the brain learns by modifying the strength of synapses [ 57 , 58 ]. Hodassman et al [ 57 ] proposed that the neuron is not a binary unit that can fire or not and that a single neuron can realize learning algorithms that previously required an artificially complex network of thousands of interconnected neurons and synapses.…”