The Convallis Rule for Unsupervised Learning in Cortical Networks
By Pierre Yger, Kenneth D. Harris. 2013
20 citations recorded by the source corpus.
The phenomenology and cellular mechanisms of cortical synaptic plasticity are becoming known in increasing detail, but the computational principles by which cortical plasticity enables the development of sensory representations are unclear. Here we describe a framework for cortical synaptic plasticity termed the “Convallis rule”, mathematically derived from a principle of unsupervised learning via constrained optimization. Implementation of the rule caused a recurrent cortex-like network of simulated spiking neurons to develop rate representations of real-world speech stimuli, enabling classification by a downstream linear decoder. Applied to spike patterns used in in vitro plasticity experiments, the rule reproduced multiple results including and beyond STDP. However STDP alone produced poorer learning performance. The mathematical form of the rule is consistent with a dual coincidence detector mechanism that has been suggested by experiments in several synaptic classes of juvenile neocortex. Based on this confluence of normative, phenomenological, and mechanistic evidence, we suggest that the rule may approximate a fundamental computational principle of the neocortex.
Subfields: Artificial Intelligence, Cognitive Neuroscience, Electrical and Electronic Engineering
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Friedemann Zenke
- Matthieu Gilson
- Surya Ganguli
- Wulfram Gerstner
- Pierre Yger
- Rahaf Aljundi
- Yoshua Bengio
- Greg Yang
In Cognitive Neuroscience
- Friedemann Zenke
- Surya Ganguli
- Wulfram Gerstner
- Matthieu Gilson
- Pierre Yger
- Andrea Soltoggio
- Claudia Clopath
- Nicolas Frémaux
In Electrical and Electronic Engineering
- Friedemann Zenke
- Surya Ganguli
- Wulfram Gerstner
- Matthieu Gilson
- Pierre Yger
- Rahaf Aljundi
- Andrea Soltoggio
- Claudia Clopath
Citations in the corpus
- The temporal paradox of Hebbian learning and homeostatic plasticity (2017, background): “the underlying plasticity models are augmented by RCPs [31, 50, 51, 68, 82].”
- Models of Metaplasticity: A Review of Concepts (2015, methodology): “…STDP models based on the curve, in order to incorporate the observed effects for triplets of spikes, spike bursts, clamping the postsynaptic membrane potential and so on (Pfister and Gerstner, 2006; Clopath et al., 2010; El Boustani et al., 2012; Graupner and Brunel, 2012; Yger and Harris, 2013).”