Control for multifunctionality: bioinspired control based on feeding in Aplysia californica

Webster‐Wood, Victoria A.; Gill, Jeffrey P.; Thomas, Peter J.; Chiel, Hillel J.

doi:10.1007/s00422-020-00851-9

Cited by 24 publications

(31 citation statements)

References 160 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In light of this, an efficient design is for neural networks to be capable of flexibly switching between various functions upon demand [1,2,3,4,5,6,7,8,9,10,11], instead of having each task be performed by a different network [1,4,12,13,14]. This flexibility has indeed been observed experimentally [3,4,15,16,17,18,19,20], but how this flexibility came to be remains a topic of investigation.…”

Section: Introductionmentioning

confidence: 99%

Augmenting Flexibility: Mutual Inhibition Between Inhibitory Neurons Expands Functional Diversity

Liu

White

2020

Preprint

View full text Add to dashboard Cite

One of the most intriguing observations of recurrent neural circuits is their flexibility. Seemingly, this flexibility extends far beyond the ability to learn, but includes the ability to use learned procedures to respond to novel situations. Here, we report that this flexibility arises from the synergistic interplay between recurrent mutual excitation and recurrent mutual inhibition. Specifically, we show that mutual inhibition is critical in expanding the functionality of the circuit, far beyond what feedback inhibition alone can accomplish. By taking advantage of dynamical systems theory and bifurcation analysis, we show mutual inhibition doubles the number of cusp bifurcations in the system in small neural circuits. As a concrete example, we build a simulation model of a class of functional motifs we call Coupled Recurrent inhibitory and Recurrent excitatory Loops (CRIRELs). These CRIRELs have the advantage of being multi-functional, performing a plethora of functions, including decisions, switches, toggles, central pattern generators, depending solely on the input type. We then use bifurcation theory to show how mutual inhibition gives rise to this broad repertoire of possible functions. Finally, we demonstrate how this trend also holds for larger networks, and how mutual inhibition greatly expands the amount of information a recurrent network can hold.

show abstract

Section: Introductionmentioning

confidence: 99%

Augmenting Flexibility: Mutual Inhibition Between Inhibitory Neurons Expands Functional Diversity

Liu

White

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Recent progress in neuromorphic sensory systems which mimic the biological receptor functions and sensorial processing [129][130][131][132] trends toward sensors and sensory systems that communicate through asynchronous digital signals analogous to neural spikes [127], improving the performance of sensors and suggesting novel sensory pro- [116,119,120], for multifunctional robotic movement command/control based on the functional neuroanatomy and synaptic plasticity of Aplysia motor and interneurons (see Figure 3).…”

Section: Sensorimotor Integrationmentioning

confidence: 99%

“…These simulate behavioral switching in response to external sensory cues. Model approaches incorporate synaptic learning and neural connectivity in a simple mechanical model of the feeding apparatus [116], with testable hypotheses in the context of robot movement control. As explained in detail in Section 3.1, the neural networks that govern feeding in Aplysia include motor neurons and cerebral-buccal target interneurons.…”

Section: Repetitive or Rhythmic Behaviormentioning

confidence: 99%

“…Learning-induced synaptic plasticity in such modular circuitry controls behavioral switching (Figure 7), as recently simulated in biologically inspired model approaches directly exploitable for multifunctional robot control. For the model equations, the reader is referred to [116][117][118][119][120]. This modeling framework can be extended to a variety of scenarios for multifunctional robot movement and rhythm control, and it has several advantages.…”

Section: Repetitive or Rhythmic Behaviormentioning

confidence: 99%

See 1 more Smart Citation

From Biological Synapses to “Intelligent” Robots

Dresp-Langley

2022

Electronics

View full text Add to dashboard Cite

This selective review explores biologically inspired learning as a model for intelligent robot control and sensing technology on the basis of specific examples. Hebbian synaptic learning is discussed as a functionally relevant model for machine learning and intelligence, as explained on the basis of examples from the highly plastic biological neural networks of invertebrates and vertebrates. Its potential for adaptive learning and control without supervision, the generation of functional complexity, and control architectures based on self-organization is brought forward. Learning without prior knowledge based on excitatory and inhibitory neural mechanisms accounts for the process through which survival-relevant or task-relevant representations are either reinforced or suppressed. The basic mechanisms of unsupervised biological learning drive synaptic plasticity and adaptation for behavioral success in living brains with different levels of complexity. The insights collected here point toward the Hebbian model as a choice solution for “intelligent” robotics and sensor systems.

show abstract

“…[26] constructed a model capable of transitioning between limitcycling and heteroclinic-cycling behaviors to represent a neuromotor central pattern generator (CPG) in Aplysia californica (see also [29]). While more detailed models for the Aplysia feeding system have since been developed [48], the simplicity of the three-component SLG (Shaw-Lyttle-Gill) model makes it an attractive target for analysis.…”

mentioning

confidence: 99%

Heteroclinic cycling and extinction in May-Leonard models with demographic stochasticity

Barendregt¹,

Thomas²

2021

Preprint

Self Cite

View full text Add to dashboard Cite

May and Leonard (SIAM J. Appl. Math 1975) introduced a three-species Lotka-Volterra type population model that exhibits heteroclinic cycling. Rather than producing a periodic limit cycle, the trajectory takes longer and longer to complete each "cycle", passing closer and closer to unstable fixed points in which one population dominates and the others approach zero. Aperiodic heteroclinic dynamics have subsequently been studied in ecological systems (side-blotched lizards; colicinogenic E. coli), in the immune system, in neural information processing models ("winnerless competition"), and in models of neural central pattern generators. Yet as May and Leonard observed "Biologically, the behavior (produced by the model) is nonsense. Once it is conceded that the variables represent animals, and therefore cannot fall below unity, it is clear that the system will, after a few cycles, converge on some single population, extinguishing the other two." Here, we explore different ways of introducing discrete stochastic dynamics based on May and Leonard's ODE model, with application to ecological population dynamics, and to a neuromotor central pattern generator system. We study examples of several quantitatively distinct asymptotic behaviors, including total extinction of all species, extinction to a single species, and persistent cyclic dominance with finite mean cycle length.

show abstract

Control for multifunctionality: bioinspired control based on feeding in Aplysia californica

Cited by 24 publications

References 160 publications

Augmenting Flexibility: Mutual Inhibition Between Inhibitory Neurons Expands Functional Diversity

Augmenting Flexibility: Mutual Inhibition Between Inhibitory Neurons Expands Functional Diversity

From Biological Synapses to “Intelligent” Robots

Heteroclinic cycling and extinction in May-Leonard models with demographic stochasticity

Contact Info

Product

Resources

About