NMDA-driven dendritic modulation enables multitask representation learning in hierarchical sensory processing pathways

Proceedings of the National Academy of Sciences, vol. 120, pp. e2300558120

Abstract

While sensory representations in the brain depend on context, it remains unclear how such modulations are implemented at the biophysical level, and how processing layers further in the hierarchy can extract useful features for each possible contextual state. Here, we demonstrate that dendritic N-Methyl-D-Aspartate spikes can, within physiological constraints, implement contextual modulation of feedforward processing. Such neuron-specific modulations exploit prior knowledge, encoded in stable feedforward weights, to achieve transfer learning across contexts. In a network of biophysically realistic neuron models with context-independent feedforward weights, we show that modulatory inputs to dendritic branches can solve linearly nonseparable learning problems with a Hebbian, error-modulated learning rule. We also demonstrate that local prediction of whether representations originate either from different inputs, or from different contextual modulations of the same input, results in representation learning of hierarchical feedforward weights across processing layers that accommodate a multitude of contexts.

Authors 7

1. Willem A. M. Wybo

   Forschungszentrum Jülich · Jülich Aachen Research Alliance

   Affiliation as printed

   Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure–Function Relationships (INM-10), Jülich Research Center, DE-52428 Jülich, Germany

2. Matthias C. Tsai

   University of Bern

   Affiliation as printed

   Department of Physiology, University of Bern, CH-3012 Bern, Switzerland

3. Viet Anh Tran Aachen Faculty 1 Department of Computer Science 3

   RWTH Aachen University · Forschungszentrum Jülich · Jülich Aachen Research Alliance

   Affiliation as printed

   Department of Computer Science - 3, Faculty 1, RWTH Aachen University, DE-52074 Aachen, Germany

   Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure–Function Relationships (INM-10), Jülich Research Center, DE-52428 Jülich, Germany

4. Bernd Illing

   École Polytechnique Fédérale de Lausanne

   Affiliation as printed

   Laboratory of Computational Neuroscience, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland

5. Jakob Jordan

   University of Bern

   Affiliation as printed

   Department of Physiology, University of Bern, CH-3012 Bern, Switzerland

6. Abigail Morrison Aachen Faculty 1 Department of Computer Science 3

   RWTH Aachen University · Forschungszentrum Jülich · Jülich Aachen Research Alliance

   Affiliation as printed

   Department of Computer Science - 3, Faculty 1, RWTH Aachen University, DE-52074 Aachen, Germany

   Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure–Function Relationships (INM-10), Jülich Research Center, DE-52428 Jülich, Germany

7. Walter Senn

   University of Bern

   Affiliation as printed

   Department of Physiology, University of Bern, CH-3012 Bern, Switzerland