The Neuron as a Direct Data-Driven Controller
CoRR(2024)
摘要
In the quest to model neuronal function amidst gaps in physiological data, a
promising strategy is to develop a normative theory that interprets neuronal
physiology as optimizing a computational objective. This study extends the
current normative models, which primarily optimize prediction, by
conceptualizing neurons as optimal feedback controllers. We posit that neurons,
especially those beyond early sensory areas, act as controllers, steering their
environment towards a specific desired state through their output. This
environment comprises both synaptically interlinked neurons and external motor
sensory feedback loops, enabling neurons to evaluate the effectiveness of their
control via synaptic feedback. Utilizing the novel Direct Data-Driven Control
(DD-DC) framework, we model neurons as biologically feasible controllers which
implicitly identify loop dynamics, infer latent states and optimize control.
Our DD-DC neuron model explains various neurophysiological phenomena: the shift
from potentiation to depression in Spike-Timing-Dependent Plasticity (STDP)
with its asymmetry, the duration and adaptive nature of feedforward and
feedback neuronal filters, the imprecision in spike generation under constant
stimulation, and the characteristic operational variability and noise in the
brain. Our model presents a significant departure from the traditional,
feedforward, instant-response McCulloch-Pitts-Rosenblatt neuron, offering a
novel and biologically-informed fundamental unit for constructing neural
networks.
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