Nonlinear Hebbian Learning as a Unifying Principle in Receptive Field Formation

By Carlos S. N. Brito, Wulfram Gerstner. 2016

70 citations recorded by the source corpus.

The development of sensory receptive fields has been modeled in the past by a variety of models including normative models such as sparse coding or independent component analysis and bottom-up models such as spike-timing dependent plasticity or the Bienenstock-Cooper-Munro model of synaptic plasticity. Here we show that the above variety of approaches can all be unified into a single common principle, namely nonlinear Hebbian learning. When nonlinear Hebbian learning is applied to natural images, receptive field shapes were strongly constrained by the input statistics and preprocessing, but exhibited only modest variation across different choices of nonlinearities in neuron models or synaptic plasticity rules. Neither overcompleteness nor sparse network activity are necessary for the development of localized receptive fields. The analysis of alternative sensory modalities such as auditory models or V2 development lead to the same conclusions. In all examples, receptive fields can be predicted a priori by reformulating an abstract model as nonlinear Hebbian learning. Thus nonlinear Hebbian learning and natural statistics can account for many aspects of receptive field formation across models and sensory modalities.

Subfields: Cognitive Neuroscience, Electrical and Electronic Engineering, Signal Processing

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Citations in the corpus

Toward an Integration of Deep Learning and Neuroscience (2016, background): “Various versions of Hebbian plasticity (Miller and MacKay, 1994), e.g., with nonlinearities (Brito and Gerstner, 2016), can give rise to diﬀerent forms of correlation and competition between neurons, leading to the self-organized formation of ocular dominance columns, self-organizing maps and…”
Synaptic Plasticity Forms and Functions. (2020, background): “…the input stream, allowing them to mediate some types of fundamental feature selectivity and to generate self-organizing topographical representations of those features (von der Malsberg 1973, Grossberg 1976, Kohonen 1982, Oja 1982, Erwin & Miller 1998, Song & Abbott 2001, Brito & Gerstner 2016).”
Hebbian plasticity requires compensatory processes on multiple timescales (2017, methodology): “At the neuronal level, algorithmic normalization of afferent synaptic weights is a commonly used mechanism to stabilize Hebbian plasticity in network models while simultaneously allowing structure formation [9,10,13,14].”