Abstract
In this paper, we pursue recent observations that, through selective dendritic filtering, single neurons respond to specific sequences of presynaptic inputs. We try to provide a principled and mechanistic account of this selectivity by applying a recent free-energy principle to a dendrite that is immersed in its neuropil or environment. We assume that neurons self-organize to minimize a variational free-energy bound on the self-information or surprise of presynaptic inputs that are sampled. We model this as a selective pruning of dendritic spines that are expressed on a dendritic branch. This pruning occurs when postsynaptic gain falls below a threshold. Crucially, postsynaptic gain is itself optimized with respect to free energy. Pruning suppresses free energy as the dendrite selects presynaptic signals that conform to its expectations, specified by a generative model implicit in its intracellular kinetics. Not only does this provide a principled account of how neurons organize and selectively sample the myriad of potential presynaptic inputs they are exposed to, but it also connects the optimization of elemental neuronal (dendritic) processing to generic (surprise or evidence-based) schemes in statistics and machine learning, such as Bayesian model selection and automatic relevance determination.
Highlights
The topic of this special issue, cortico-cortical communication, is usually studied empirically by modeling neurophysiologic data at the appropriate spatial and temporal scale (Friston, 2009)
The second section presents simulations, in which we demonstrate the reorganization of connections under free-energy minimization and record the changes in free energy over the different connectivity configurations that emerge
The mechanism implied by the model is simple; intracellular states of a dendrite are viewed as predicting their presynaptic inputs
Summary
The topic of this special issue, cortico-cortical communication, is usually studied empirically by modeling neurophysiologic data at the appropriate spatial and temporal scale (Friston, 2009). Neural mass models of neuronal sources have been used to account for magneto- and electroencephalography (M/EEG) data (Kiebel et al, 2009a) These sort of modeling techniques have been likened to a “mathematical microscope” which effectively increase the spatiotemporal resolution of empirical measurements by using neurobiologically plausible constraints on how data were generated (Friston and Dolan, 2010). Understanding the computational principles of this essential building block may generate novel insights and constraints on the computations that emerge in the brain at larger scales. This may help us form hypotheses about what neuronal systems encode, communicate, and decode
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