Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex

By Elie Bienenstock, Leon N. Cooper, Paul W. Munro. 1982

The development of stimulus selectivity in the primary sensory cortex of higher vertebrates is considered in a general mathematical framework. A synaptic evolution scheme of a new kind is proposed in which incoming patterns rather than converging afferents compete. The change in the efficacy of a given synapse depends not only on instantaneous pre- and postsynaptic activities but also on a slowly varying time-averaged value of the postsynaptic activity. Assuming an appropriate nonlinear form for this dependence, development of selectivity is obtained under quite general conditions on the sensory environment. One does not require nonlinearity of the neuron's integrative power nor does one need to assume any particular form for intracortical circuitry. This is first illustrated in simple cases, e.g., when the environment consists of only two different stimuli presented alternately in a random manner. The following formal statement then holds: the state of the system converges with probability 1 to points of maximum selectivity in the state space. We next consider the problem of early development of orientation selectivity and binocular interaction in primary visual cortex. Giving the environment an appropriate form, we obtain orientation tuning curves and ocular dominance comparable to what is observed in normally reared adult cats or monkeys. Simulations with binocular input and various types of normal or altered environments show good agreement with the relevant experimental data. Experiments are suggested that could test our theory further.

Subfields: Computer Vision and Pattern Recognition, Neuroscience

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Citations in the corpus

Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (2017, background): “Variations have been proposed to include, e.g. terms to achieve stability (Oja, 1982; Bienenstock et al., 1982) and various constraints (Miller and Mackay, 1994), or more advanced update dynamics such as dual weights for fast and slow decay (Levy and Bairaktaris, 1995; Soltoggio, 2015).”
The temporal paradox of Hebbian learning and homeostatic plasticity (2017, background): “(a) Unstable oscillations in the BCM model for a simple feed-forward circuit [6, 11].”
A model for the development of simple cell receptive fields and the ordered arrangement of orientation columns through activity-dependent competition between ON- and OFF-center inputs (1994, methodology): “I have studied these correlation-based mechanisms within the context of a simple class of models first used by von der Malsburg (1973), and subsequently used by many other theorists (e.g., Bienenstock et al., 1982; Linsker, 1986a-c; Miller et al., 1989; Tanaka, 1990, 1992; Miller, 1992a).”
Homeostatic Activity-Dependent Tuning of Recurrent Networks for Robust Propagation of Activity (2015, methodology): “The homeostatic model was inspired by the Bienenstock-Cooper-Munro theory of synaptic plasticity (Bienenstock et al., 1982).”
Emergent Dynamical Properties of the BCM Learning Rule (2017, methodology): “[5], φ(v) = v(v − θ) is used, E[v] is a running temporal average of v and the learning rule is stable for p > 1.”
Modeling the dynamic interaction of Hebbian and homeostatic plasticity. (2014, methodology): “of Hebbian and Homeostatic Plasticity”
Continuous latent variable models for dimensionality reduction and sequential data reconstruction (2001, methodology): “Another possibility is to use the automatic relevance determination (ARD) framework (MacKay, 1995b), as Bishop (1999) has proposed for the probabilistic PCA and PCA mixture models described in sections 2.”
Musical training as a framework for brain plasticity: behavior, function, and structure. (2012, background): “…long-term potentiation (e.g., Huang et al., 1992), it has also been applied to explain features of experience-dependent plasticity in visual cortex (Bienenstock et al., 1982), and it can also explain enhanced short-term plastic effects due to modulation of the involved networks by previous sensory…”
Stable Hebbian Learning from Spike Timing-Dependent Plasticity (2000, background): “Alternatively, the postsynaptic activity can be used to adjust the threshold for potentiation (Bienenstock et al., 1982; Kirkwood et al., 1996), to regulate neuronal excitability (Desai et al., 1999), or to scale all of a neuron’s synaptic weights (Turrigiano et al., 1998).”
Neural Networks: Tricks of the Trade (2012, methodology): “Neural Computation 4, 141–166 (1992) [4] Becker, S.”
Learning Invariance from Natural Images Inspired by Observations in the Primary Visual Cortex (2012, unknown): “Our new rule expands these ideas by incorporating aspects of BCM learning (Bienenstock, Cooper, & Munro, 1982; Shouval, Castellani, Blais, Yeung, & Cooper, 2002; Yeung, Shouval, Blais, & Cooper, 2004; Castellani, Quinlan, Bersani, Cooper, & Shouval, 2005), particularly by using the level of…”
Nonlinear neural networks: Principles, mechanisms, and architectures (1988, background): “Early cerebellar learning models were proposed by Albus (1971), Brindley (1964), Grossberg (1964, 1969d, 1972b), and Marr (1969). Later models and experimental support were provided by Fujita (1982a, 1982b), Ito (1974, 1982, 1984), McCormick and Thompson (1984), Optican and Robinson (1980), Ron and ”