Neuromodulation and Neural Circuit Performativity: Adequacy Conditions for Their Computational Modelling
An understanding of the functional repertoire of neural circuits and their plasticity requires knowledge of neural connectivity diagrams and their dynamical evolution. However, one must additionally take into account the fast and reversible functional effects induced by neuromodulatory mechanisms which do not alter neural circuit diagrams. Neuromodulators contribute crucially to determine the performativity of a neural circuit, that is, its ability to change behavior, and especially behavioral changes occurring under temporal constraints that are incompatible with the longer time scales of Hebbian learning and other forms of neural learning. This paper focuses on two properties of neuromodulatory action that have been relatively neglected so far. These properties are the functional soundness of neuromodulated circuits and the robustness of neuromodulatory action. Both properties are analyzed here as sources of functional specifications for the computational modeling of neural circuit performativity. In particular, taking dynamical systems that are based on CTRNNs (Continuous Time Recurrent Neural Networks) as an exemplary class of computational models, it is argued that robustness is suitably modeled there by means of a hysteresis process, and functional soundness by means of a multiplicity of stable fixed points.
KeywordsComputational neurosciences Performativity of neural circuits Neuromodulation Computational models of neuromodulation Robustness of neuromodulatory action
Research work on which this article is based was partially supported by grant 2015TM24JS in the framework of the PRIN 2015 program of MIUR (Italian Ministry of University, Research and Education).
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