Perspectives on adaptive dynamical systems

Adaptivity is a dynamical feature that is omnipresent in nature, socio-economics, and technology. For example, adaptive couplings appear in various real-world systems, such as the power grid, social, and neural networks, and they form the backbone of closed-loop control strategies and machine learning algorithms. In this article, we provide an interdisciplinary perspective on adaptive systems. We reflect on the notion and terminology of adaptivity in different disciplines and discuss which role adaptivity plays for various fields. We highlight common open challenges and give perspectives on future research directions, looking to inspire interdisciplinary approaches

Perspectives on adaptive dynamical systems

Adaptivity is a dynamical feature that is omnipresent in nature, socio-economics, and technology. For example, adaptive couplings appear in various real-world systems like the power grid, social, and neural networks, and they form the backbone of closed-loop control strategies and machine learning algorithms. In this article, we provide an interdisciplinary perspective on adaptive systems. We reflect on the notion and terminology of adaptivity in different disciplines and discuss which role adaptivity plays for various fields. We highlight common open challenges, and give perspectives on future research directions, looking to inspire interdisciplinary approaches.Comment: 46 pages, 9 figure

Eleven strategies for making reproducible research and open science training the norm at research institutions

Across disciplines, researchers increasingly recognize that open science and reproducible research practices may accelerate scientific progress by allowing others to reuse research outputs and by promoting rigorous research that is more likely to yield trustworthy results. While initiatives, training programs, and funder policies encourage researchers to adopt reproducible research and open science practices, these practices are uncommon inmanyfields. Researchers need training to integrate these practicesinto their daily work. We organized a virtual brainstorming event, in collaboration with the German Reproducibility Network, to discuss strategies for making reproducible research and open science training the norm at research institutions. Here, weoutline eleven strategies, concentrated in three areas:(1)offering training, (2)adapting research assessment criteria and program requirements, and (3) building communities. We provide a brief overview of each strategy, offer tips for implementation,and provide links to resources. Our goal is toencourage members of the research community to think creatively about the many ways they can contribute and collaborate to build communities,and make reproducible research and open sciencetraining the norm. Researchers may act in their roles as scientists, supervisors, mentors, instructors, and members of curriculum, hiring or evaluation committees. Institutionalleadership and research administration andsupport staff can accelerate progress by implementing change across their institution

Eleven strategies for making reproducible research and open science training the norm at research institutions

Across disciplines, researchers increasingly recognize that open science and reproducible research practices may accelerate scientific progress by allowing others to reuse research outputs and by promoting rigorous research that is more likely to yield trustworthy results. While initiatives, training programs, and funder policies encourage researchers to adopt reproducible research and open science practices, these practices are uncommon inmanyfields. Researchers need training to integrate these practicesinto their daily work. We organized a virtual brainstorming event, in collaboration with the German Reproducibility Network, to discuss strategies for making reproducible research and open science training the norm at research institutions. Here, weoutline eleven strategies, concentrated in three areas:(1)offering training, (2)adapting research assessment criteria and program requirements, and (3) building communities. We provide a brief overview of each strategy, offer tips for implementation,and provide links to resources. Our goal is toencourage members of the research community to think creatively about the many ways they can contribute and collaborate to build communities,and make reproducible research and open sciencetraining the norm. Researchers may act in their roles as scientists, supervisors, mentors, instructors, and members of curriculum, hiring or evaluation committees. Institutionalleadership and research administration andsupport staff can accelerate progress by implementing change across their institution

Stability and learning in excitatory synapses by nonlinear inhibitory plasticity.

Synaptic changes are hypothesized to underlie learning and memory formation in the brain. But Hebbian synaptic plasticity of excitatory synapses on its own is unstable, leading to either unlimited growth of synaptic strengths or silencing of neuronal activity without additional homeostatic mechanisms. To control excitatory synaptic strengths, we propose a novel form of synaptic plasticity at inhibitory synapses. Using computational modeling, we suggest two key features of inhibitory plasticity, dominance of inhibition over excitation and a nonlinear dependence on the firing rate of postsynaptic excitatory neurons whereby inhibitory synaptic strengths change with the same sign (potentiate or depress) as excitatory synaptic strengths. We demonstrate that the stable synaptic strengths realized by this novel inhibitory plasticity model affects excitatory/inhibitory weight ratios in agreement with experimental results. Applying a disinhibitory signal can gate plasticity and lead to the generation of receptive fields and strong bidirectional connectivity in a recurrent network. Hence, a novel form of nonlinear inhibitory plasticity can simultaneously stabilize excitatory synaptic strengths and enable learning upon disinhibition

The nonlinear inhibitory plasticity rule maintains an excitatory-to-inhibitory weight ratio.

A. The steady state E/I weight ratio as a function of the presynaptic excitatory rate ρE. Inset: RE/I approaches the steady state NIνI/(NEρE) (dashed line) for large I-to-E weights. B-F Based on a random initial weight configuration drawn from a uniform distribution in the range of [0, 3], excitatory and inhibitory plasticity was induced for 100 ms. Extreme initial E/I ratios () were excluded from the analysis. B. Phase portrait of the dynamics of E-to-E (wEE) and I-to-E (wEI) weights. Gray arrows indicate the direction of weight evolution over time, colored points represent three different weight initialization, , colored lines represents the weight evolution for each case and the cross marks the weights after plasticity induction. The firing rates dynamics are similar as in Fig 2. C. E/I ratio before and after plasticity induction. Crosses indicate examples in B. Gray dashed line indicates the identity line and gray line indicates . D. E-to-E weight change ΔwEE versus I-to-E weight change ΔwEI after plasticity induction in percent of initial synaptic weights. Dashed gray line indicates initial I-to-E weight strength and crosses indicate examples in B. E. E-to-E weight change ΔwEE as a function of E/I ratio RE/I before plasticity in percent of initial weights. Dashed gray line indicates initial E-to-E weight strength and crosses indicate examples in B. F. Same as E but for I-to-E weight change ΔwEI.</p

A novel nonlinear inhibitory plasticity rule can counteract runaway dynamics of excitatory-to-excitatory weights.

A. Plasticity curves of E-to-E (, blue) and I-to-E (, red) weights as a function of the postsynaptic rate νE. The excitatory and inhibitory LTD/LTP thresholds are identical (). B. Phase portrait of the dynamics of E-to-E (wEE) and I-to-E (wEI) weights. Gray arrows indicate the direction of weight evolution over time, points represent three different initial conditions of the weights, , and green lines represent the weight evolution for each initial condition. The two colored points represent initial weights in C. Black line indicates the line attractor and the gray line separates the space at which the postsynaptic firing rate is zero (no dynamics) or larger than zero (Methods, Eq 18). C. E-to-E (wEE, blue) and I-to-E (wEI, red) weight dynamics and postsynaptic rate dynamics (νE, gray) as a function of time for two initial conditions in B, (solid lines) and (dashed lines). D. The slope and intersection of the line attractor with the abscissa (black line) depend on the number and firing rates of excitatory and inhibitory neurons and the LTD/LTP threshold.</p

Dynamic matching of the excitatory and inhibitory postsynaptic LTD/LTP thresholds and networks response to input perturbations.

A. Postsynaptic LTD/LTP thresholds and shift dynamically depending on the recent postsynaptic rate νE. For lower postsynaptic rate than the excitatory postsynaptic LTD/LTP threshold (), decreases, and for , increases. For higher postsynaptic rate than the inhibitory postsynaptic LTD/LTP threshold (), decreases, and for , increases (see Methods). B. Evolution of excitatory (, blue) or inhibitory (, red) postsynaptic LTD/LTP thresholds for initial conditions , . C. Excitatory (wEE, blue) and inhibitory (wEI, red) weight dynamics and postsynaptic rate dynamics (νE, gray) for the initial condition , . D. Effect of increasing (solid lines, ) or decreasing (dashed lines, ) excitatory input rates from a baseline of on excitatory (blue) and inhibitory (red) firing rates. E. Same as D but for the and weights. F. Same as D but for the wEE and wEI weights. G. Plasticity curve of E-to-E weights for input 1 or 2 () as a function of the presynaptic excitatory rate ρE for different input-specific perturbations . H. Evolution of excitatory (, blue) or inhibitory (, red) postsynaptic LTD/LTP thresholds for the case in G. I. Excitatory (wEE, blue) and inhibitory (wEI, red) weight dynamics for the case in G. Compare A-C to Fig 3, D-F to Fig 4 and G-I to Fig 7B. (EPS)</p

Nonlinear inhibitory plasticity can regulate the network response to perturbations.

A. Schematic of perturbing the excitatory presynaptic rate in the inhibitory feedforward motif. We use the nonlinear inhibitory plasticity rule with identical excitatory and inhibitory LTD/LTP thresholds from Fig 2A. B. Effect of increasing (solid lines, ) or decreasing (dashed lines, ) excitatory input rates from a baseline of on excitatory (blue) and inhibitory (red) firing rates. C. Same as B but for the wEE and wEI weights. D. The line attractor for the baseline input and two input perturbations . E. E-to-E weight change as a function of the presynaptic excitatory rate ρE for the baseline input and for two input perturbations . F. I-to-E weight change as a function of the inhibitory rate νI for the baseline input and for two input perturbations .</p

Performance of the nonlinear inhibitory plasticity rule under varying postsynaptic firing rate with dynamic excitatory and inhibitory LTD/LTP threshold matching.

A. Adding noise to the postsynaptic firing rate. Top: E-to-E (wEE, blue) and I-to-E (wEI, red) as a function of time. Middle: Excitatory (, blue) and inhibitory (, red) postsynaptic LTD/LTP threshold as a function of time. Bottom: Postsynaptic rate dynamics (νE, gray) as a function of time. B. Same as A but after adding a sinusiodal input to the postsynaptic firing rate. (EPS)</p