Ever since the advent of the neuron doctrine more than a century ago, information processing in the brain is widely believed to mainly follow the forward pre to post-synaptic neurons direction. In this paper, we emit the backpropagation-based recollection hypothesis as follows: weak and fast fading retrograde signals following the post to pre-synaptic backward pathways with highest pre-synaptic weights , mediate explicit cue-based memory recall. This includes also the tasks of imagination, future episodic thinking, language understanding and associating names to various stimuli. These signals originate in highly invariant neurons, which uniquely respond to some specific stimuli , e.g. image of a cat , and travel backwards to reactivate the same populations of neurons that usually uniquely activate in the actual presence of such specific stimuli , thus creating offline an experience that is similar. After stating our hypothesis  , we review abundant evidence on the existence of such backpropagating signals as well as other relevant literature that supports our claims. We then leverage simulations based on existing spiking neural network models with STDP learning to show the computational feasibility of using such a mechanism to map the image of an object to its name with the same high accuracy as a state of the art machine learning classifier. Although not yet a theory, we believe this hypothesis presents a paradigm shift that is worth further investigating: it opens the way, among others, to new interpretations of language acquisition and understanding, the interplay between memories encoding and retrieval, as well as reconciling the apparently opposed views between sparse coding and distributed representations.
Ever since the advent of the neuron doctrine more than a century ago, information processing in the brain is widely believed to mainly follow the forward pre to post-synaptic neurons direction. In this paper, we emit the backpropagation-based recollection hypothesis as follows: weak and fast fading Action Potentials following the (highest weight) post to pre-synaptic backward pathways , mediate explicit cue-based memory recall. This includes also the tasks of imagination, future episodic thinking, language understanding and associating names to various stimuli. These signals originate in highly invariant neurons, which uniquely respond to some specific stimuli ( e.g. image of a cat ). They then travel backwards to reactivate the same populations of neurons that uniquely respond to this specific stimuli during perception, thus recreating "offline" an experience that is similar. After stating our hypothesis in details , we review abundant evidence on the existence of such backpropagating signals as well as other relevant literature that supports our claims. We then leverage simulations based on existing spiking neural network models with STDP learning to show the computational feasibility of using such a mechanism to map the image of an object to its name with the same high accuracy as a state of the art machine learning classifier. Although not yet a theory, we believe this hypothesis presents a paradigm shift that is worth further investigating: it opens the way, among others, to new interpretations of language acquisition and understanding, the interplay between memories encoding and retrieval, as well as reconciling the apparently opposed views between sparse coding and distributed representations.
R
retrograde signals following the
Action Potentials following the (highest weight)
D
with highest pre-synaptic weights
R
,
(
R
, and
). They then
R
usually uniquely activate in the actual presence of such specific stimuli , thus creating offline
uniquely respond to this specific stimuli during perception, thus recreating "offline"
A
in details