Statistical analysis and optimality of neural systems

Młynarski, Wiktor; Hledík, Michal; Sokolowski, Thomas R.; Tkačik, Gašper

doi:10.1101/848374

Cited by 6 publications

(6 citation statements)

References 67 publications

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“…Later, Teti et al (69) employed sparse coding with lateral inhibition in simulations of neuronal activation in visual cortex. More recently, Młynarski et al (41) presented a probabilistic framework combining normative priors with statistical inference and demonstrated the usefulness of this approach for the analysis of diverse neuroscientific datasets. However, their work was rather conceptual, with the datasets they used being either simulated or low-dimensional.…”

Section: Discussionmentioning

confidence: 99%

“…Notably, the intersection between EC and SI has long remained largely unexplored but lately shifted more into focus. In particular, Mlynarski and colleagues recently proposed a theoretical framework incorporating normative theories for statistical inference on simulated or pre-fit neural data (41). Their framework enables conducting rigorous statistical hypothesis tests of coding principles, but has not yet been applied to predicting neural responses to arbitrary stimuli with minimal assumptions.…”

Section: Introductionmentioning

confidence: 99%

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Efficient coding of natural scenes improves neural system identification

Qiu

Klindt²,

Szatko

et al. 2022

Preprint

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Neural system identification aims at learning the response function of neurons to arbitrary stimuli using experimentally recorded data, but typically does not leverage coding principles such as efficient coding of natural environments. Visual systems, however, have evolved to efficiently process input from the natural environment. Here, we present a normative network regularization for system identification models by incorporating, as a regularizer, the efficient coding hypothesis, which states that neural response properties of sensory representations are strongly shaped by the need to preserve most of the stimulus information with limited resources. Using this approach, we explored if a system identification model can be improved by sharing its convolutional filters with those of an autoencoder which aims to efficiently encode natural stimuli. To this end, we built a hybrid model to predict the responses of retinal neurons to noise stimuli. This approach did not only yield a higher performance than the stand-alone system identification model, it also produced more biologically-plausible filters. We found these results to be consistent for retinal responses to different stimuli and across model architectures. Moreover, our normatively regularized model performed particularly well in predicting responses of direction-of-motion sensitive retinal neurons. In summary, our results support the hypothesis that efficiently encoding environmental inputs can improve system identification models of early visual processing.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Efficient coding of natural scenes improves neural system identification

Qiu

Klindt²,

Szatko

et al. 2022

Preprint

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“…As such, we could obtain misleading results if the recorded network is only approximately optimal. To deal with this, recent work by one of the present authors [45] proposed a framework in which the neural network is assumed to satisfy a known optimality criterion approximately. In this work, the optimality criterion is formulated as a Bayesian prior, which serves to nudge the network towards desirable solutions.…”

Section: Discussionmentioning

confidence: 99%

Training and inferring neural network function with multi-agent reinforcement learning

Chalk

Tkačik

Marre

2019

Preprint

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A central goal in systems neuroscience is to understand the functions performed by neural circuits. Previous top-down models addressed this question by comparing the behaviour of an ideal model circuit, optimised to perform a given function, with neural recordings. However, this requires guessing in advance what function is being performed, which may not be possible for many neural systems. To address this, we propose a new framework for optimising a recurrent network using multi-agent reinforcement learning (RL). In this framework, a reward function quantifies how desirable each state of the network is for performing a given function. Each neuron is treated as an 'agent', which optimises its responses so as to drive the network towards rewarded states. Three applications follow from this. First, one can use multi-agent RL algorithms to optimise a recurrent neural network to perform diverse functions (e.g. efficient sensory coding or motor control). Second, one could use inverse RL to infer the function of a recorded neural network from data. Third, the theory predicts how neural networks should adapt their dynamics to maintain the same function when the external environment or network structure changes. This could lead to theoretical predictions about how neural network dynamics adapt to deal with cell death and/or varying sensory stimulus statistics.February 6, 2020 1/24 environment [4], or memory storage [5]. Nevertheless, it has remained difficult to make quantitative contact between top-down model predictions and data, in particular, to rigorously test which (if any) of the proposed functions is actually being carried out by a real neural circuit. The first problem is that a pure top-down approach requires us to hypothesise the function performed by a given neural circuit, which is often not possible. Second, even if our hypothesis is correct, there may be multiple ways for a neural circuit to perform the same function, so that the predictions of the top-down model may not match the data.Here we propose a new framework for considering optimal coding by a recurrent neural network, that aims to overcome these problems. First, we show how optimal coding by a recurrent neural network can be re-cast as a multi-agent reinforcement learning (RL) problem [14-18] (Fig 1). In this framework, a reward function quantifies how desirable each state of the network is for performing a given computation. Each neuron is then treated as a separate 'agent', which optimises its responses (i.e. when to fire a spike) so as to drive the network towards rewarded states, given a constraint on the information each neuron encodes about its inputs. This framework is very generaldifferent choices of reward function result in the network performing diverse functions, from efficient coding to decision making and optimal control -and thus has the potential to unify many previous theories of neural coding.Next, we show how our proposed framework could be used to tackle the inverse problem, of inferring the reward function from the observed n...

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“…Combined, our results develop a framework and provide support for the role of efficient neuronal coding in behavior. The efficient coding hypothesis has emerged as one of the leading principles in computational neuroscience that has shaped our understanding of neuronal coding, architecture and evolution 1,22,[76][77][78] . Prior research found that human behavior follows principles of efficiency 19,24 .…”

Section: The Missing Link Between Efficient Coding and Behaviormentioning

confidence: 99%

“…The efficient coding hypothesis has been formally implemented through normative models of brain function 3,4,[20][21][22][23] . These models assess whether and how neuronal adaptation shapes sensory information and simulate how such adaptation might constrain behavior.…”

Section: Introductionmentioning

confidence: 99%

Dynamics of cortical contrast adaptation predict perception of signals in noise

Angeloni

Młynarski

Piasini

et al. 2021

Preprint

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The efficient coding hypothesis postulates that neurons shape their response properties to match their dynamic range to the statistics of incoming signals. However, whether and how the dynamics of efficient neuronal adaptation inform behavior has not been directly shown. Here, we trained mice to detect a target presented in background noise shortly after a change in the background contrast. The observed changes in cortical gain and detection behavior followed the predictions of a normative model of efficient cortical sound processing; specifically, target detection and sensitivity to target volume improved in low contrast backgrounds relative to high contrast backgrounds. Additionally, the time course of target detectability adapted asymmetrically depending on contrast, decreasing rapidly after a transition to high contrast, and increasing more slowly after a transition to low contrast. Auditory cortex was required for detection of targets in background noise and cortical neuronal responses exhibited the patterns of target detectability observed during behavior and in the normative model. Furthermore, variability in cortical gain predicted behavioral performance beyond the effect of stimulus-driven gain control. Combined, our results demonstrate that efficient neural codes in auditory cortex directly influence perceptual behavior.

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Statistical analysis and optimality of neural systems

Cited by 6 publications

References 67 publications

Efficient coding of natural scenes improves neural system identification

Efficient coding of natural scenes improves neural system identification

Training and inferring neural network function with multi-agent reinforcement learning

Dynamics of cortical contrast adaptation predict perception of signals in noise

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