Two-compartment neuronal spiking model expressing brain-state specific apical-amplification, -isolation and -drive regimes

arXiv (Cornell University)

Abstract

Mounting experimental evidence suggests that brain-state-specific neural mechanisms, supported by connectomic architectures, play a crucial role in integrating past and contextual knowledge with the current, incoming flow of evidence (e.g., from sensory systems). These mechanisms operate across multiple spatial and temporal scales, necessitating dedicated support at the levels of individual neurons and synapses. A notable feature within the neocortex is the structure of large, deep pyramidal neurons, which exhibit a distinctive separation between an apical dendritic compartment and a basal dendritic/perisomatic compartment. This separation is characterized by distinct patterns of incoming connections and brain-state-specific activation mechanisms, namely, apical amplification, isolation, and drive, which are associated with wakefulness, deeper NREM sleep stages, and REM sleep, respectively. The cognitive roles of apical mechanisms have been demonstrated in behaving animals. In contrast, classical models of learning in spiking networks are based on single-compartment neurons, lacking the ability to describe the integration of apical and basal/somatic information. This work aims to provide the computational community with a two-compartment spiking neuron model that incorporates features essential for supporting brain-state-specific learning. This model includes a piece-wise linear transfer function (ThetaPlanes) at the highest abstraction level, making it suitable for use in large-scale bio-inspired artificial intelligence systems. A machine learning evolutionary algorithm, guided by a set of fitness functions, selected the parameters that define neurons expressing the desired apical mechanisms.

Authors 8

1. Elena Pastorelli

   Istituto Nazionale di Fisica Nucleare, Sezione di Roma I

   Affiliation as printed

   Istituto Nazionale di Fisica Nucleare , Sezione di Roma , Roma , Italy

2. Alper Yegenoglu Aachen Department of Mathematics Institute of Geometry and Applied Mathematics

   RWTH Aachen University · Forschungszentrum Jülich · Jülich Aachen Research Alliance · Jülich Supercomputing Centre

   Affiliation as printed

   Department of Mathematics , Institute of Geometry and Applied Mathematics , RWTH Aachen University , Aachen , Germany

   Simulation and Data Lab Neuroscience , Jülich Supercomputing Centre (JSC) , Institute for Advanced Simulation , JARA , Jülich Research Center , Jülich , Germany

3. Nicole Kolodziej

   Sapienza University of Rome · Istituto Nazionale di Fisica Nucleare, Sezione di Roma I

   Affiliation as printed

   Dipartimento di Fisica , Università di Roma Sapienza , Roma , Italy

   Istituto Nazionale di Fisica Nucleare , Sezione di Roma , Roma , Italy

4. Willem A. M. Wybo
   Affiliation as printed

   Institute of Neuroscience and Medicine (INM-6) and

5. Francesco Simula

   Istituto Nazionale di Fisica Nucleare, Sezione di Roma I

   Affiliation as printed

   Istituto Nazionale di Fisica Nucleare , Sezione di Roma , Roma , Italy

6. Sandra Diaz-Pier

   Forschungszentrum Jülich · Jülich Aachen Research Alliance · Jülich Supercomputing Centre

   Affiliation as printed

   Simulation and Data Lab Neuroscience , Jülich Supercomputing Centre (JSC) , Institute for Advanced Simulation , JARA , Jülich Research Center , Jülich , Germany

7. Johan Frederik Storm

   University of Oslo · Jülich Aachen Research Alliance

   Affiliation as printed

   Department of Molecular Medicine , Institute of Basic Medical Sciences , University of Oslo , Oslo , Norway

   Institute for Advanced Simulation (IAS- ) and JARA-Institute Brain Structure-Function Relationships (INM-10) , Jülich Research Center , Jülich , Germany

8. Pier Stanislao Paolucci

   Istituto Nazionale di Fisica Nucleare, Sezione di Roma I

   Affiliation as printed

   Istituto Nazionale di Fisica Nucleare , Sezione di Roma , Roma , Italy