Parallel and Recurrent Cascade Models as a Unifying Force for Understanding Subcellular Computation

Harkin, Emerson F; Shen, Peter R.; Goel, Anish; Richards, Blake A.; Naud, Richard

doi:10.1016/j.neuroscience.2021.07.026

Cited by 8 publications

(7 citation statements)

References 84 publications

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“…The resulting f-I curves are modulated by the strength of dendritic inputs in two fundamental ways. Firstly, the f-I curves may shift by an amount that scales with dendritic input strength, consistent with dendrites acting additively such as in hierarchical neural network [13, 24, 26]. Secondly, the gain of the f-I curve may increase slightly with dendritic input, consistent with a role of dendritic spikes in gain modulation [21, 43].…”

Section: Resultsmentioning

confidence: 94%

“…The role of these dendritic spikes is thought to be in modulating the gain of the soma-based input-output function [21]. Other efforts have established an additive modulation by dendritic inputs that have been transformed nonlinearly as in an artificial neural network [22][23][24][25][26] (with possibly nonmonotonic activation functions [27,28]). However, both the additive and gain modulations are thought to be weak in the presence of background fluctuations [21,24].…”

Section: Introductionmentioning

confidence: 99%

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Dendritic excitability controls overdispersion

Friedenberger

Naud

2022

Preprint

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A neuron's input-output function is commonly understood in terms of the fluctuation-driven and mean-driven regimes. Here we investigate how active dendrites falling in either regime shape the input-output function and find that dendritic input primarily controls interspike interval dispersion, a prediction that is validated with dual patch clamp recordings. This phenomenon can be understood by considering that neurons display not two, but three fundamental operating regimes, depending on whether dendritic spikes or the somatic input reach threshold.

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Section: Resultsmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Dendritic excitability controls overdispersion

Friedenberger

Naud

2022

Preprint

Self Cite

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“…Here we build on a theme of some of our previous work by adding biological details to a simple model in order to improve its interpretability and performance (10, 72, 73). Here, the success of our model hinges on combining the normative idea of a value signal with spike-frequency adaptation from a bottom-up model of the DRN (10).…”

Section: Discussionmentioning

confidence: 99%

“…Here we build on a theme of some of our previous work by adding biological details to a simple model in order to improve its interpretability and performance (10,72,73).…”

Section: Top-down Meets Bottom-upmentioning

confidence: 99%

Serotonin predictively encodes value

Harkin,

Grossman,

Cohen

et al. 2023

Preprint

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Thein vivoresponses of dorsal raphe nucleus (DRN) serotonin neurons to emotionally-salient stimuli are a puzzle. Existing theories centred on reward, surprise, or uncertainty individually account for some aspects of serotonergic activity but not others. Here we find a unifying perspective in a biologically-constrained predictive code for cumulative future reward, a quantity called state value in reinforcement learning. Through simulations of trace conditioning experiments common in the serotonin literature, we show that our theory, called value prediction, intuitively explains phasic activation by both rewards and punishments, preference for surprising rewards but absence of a corresponding preference for punishments, and contextual modulation of tonic firing---observations that currently form the basis of many and varied serotonergic theories. Next, we re-analyzed data from a recent experiment and found serotonin neurons with activity patterns that are a surprisingly close match: our theory predicts the marginal effect of reward history on population activity with a precision < 0.1 Hz/neuron. Finally, we directly compared against quantitative formulations of existing ideas and found that our theory best explains both within-trial activity dynamics and trial-to-trial modulations, offering performance usually several times better than the closest alternative. Overall, our results show that previous models are not wrong, but incomplete, and that reward, surprise, salience, and uncertainty are simply different faces of a predictively-encoded value signal. By unifying previous theories, our work represents an important step towards understanding the potentially heterogeneous computational roles of serotonin in learning, behaviour, and beyond.

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“…All these efforts take part in solving the crucial neuro-science quest to figure out the input-output function of the neuron. Examples of recent effort in this direction can be found at Ujfalussy et al ( 2018 ), Beniaguev et al ( 2021 ), Harkin et al ( 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Parameter Estimation of Two Spiking Neuron Models With Meta-Heuristic Optimization Algorithms

AbdelAty

Fouda

Eltawil

2022

Front. Neuroinform.

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The automatic fitting of spiking neuron models to experimental data is a challenging problem. The integrate and fire model and Hodgkin–Huxley (HH) models represent the two complexity extremes of spiking neural models. Between these two extremes lies two and three differential-equation-based models. In this work, we investigate the problem of parameter estimation of two simple neuron models with a sharp reset in order to fit the spike timing of electro-physiological recordings based on two problem formulations. Five optimization algorithms are investigated; three of them have not been used to tackle this problem before. The new algorithms show improved fitting when compared with the old ones in both problems under investigation. The improvement in fitness function is between 5 and 8%, which is achieved by using the new algorithms while also being more consistent between independent trials. Furthermore, a new problem formulation is investigated that uses a lower number of search space variables when compared to the ones reported in related literature.

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Parallel and Recurrent Cascade Models as a Unifying Force for Understanding Subcellular Computation

Cited by 8 publications

References 84 publications

Dendritic excitability controls overdispersion

Dendritic excitability controls overdispersion

Serotonin predictively encodes value

Parameter Estimation of Two Spiking Neuron Models With Meta-Heuristic Optimization Algorithms

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