Biology subjects

Nock, R.

Publications and source records attributed to Nock, R..

4 recordsLinked to original sources

bioRxiv2020

Neural Network Poisson Models for Behavioural and Neural Spike Train Data

AO_SCPLOWBSTRACTC_SCPLOWOne of the most important and challenging application areas for complex machine learning methods is to predict, characterize and model rich, multi-dimensional, neural data. Recent advances in neural recording techniques have made it possible to monitor the activities of a large number of neurons across different brain regions as animals perform behavioural tasks. This poses the critical challenge of establishing links between neural activity at a microscopic scale, which might for instance represent sensory input, and at a macroscopic scale, which then generates behaviour. Predominant modeling methods apply rather disjoint techniques to these scales; by contrast, we suggest an end-to-end model which exploits recent developments of flexible, but tractable, neural network point-process models to characterize dependencies between stimuli, actions, and neural data. We apply this model to a public dataset collected using Neuropixel probes in mice performing a visually-guided behavioural task as well as a synthetic dataset produced from a hierarchical network model with reciprocally connected sensory and integration circuits intended to characterize animal behaviour in a fixed-duration motion discrimination task. We show that our model outperforms previous approaches and contributes novel insights into the relationships between neural activities and behaviour.

neuroscience↗

bioRxiv2020

Adversarial manipulation of human decision-making

Adversarial examples are carefully crafted input patterns that are surprisingly poorly classified by artificial and/or natural neural networks. Here we examine adversarial vulnerabilities in the processes responsible for learning and choice in humans. Building upon recent recurrent neural network models of choice processes, we propose a general framework for generating adversarial opponents that can shape the choices of individuals in particular decision-making tasks towards the behavioural patterns desired by the adversary. We show the efficacy of the framework through two experiments involving action selection and response inhibition. We further investigate the strategy used by the adversary in order to gain insights into the vulnerabilities of human choice. The framework may find applications across behavioural sciences in helping detect and avoid flawed choice.

neuroscience↗

bioRxiv2019

Disentangled behavioral representations

Individual characteristics in human decision-making are often quantified by fitting a parametric cognitive model to subjects behavior and then studying differences between them in the associated parameter space. However, these models often fit behavior more poorly than recurrent neural networks (RNNs), which are more flexible and make fewer assumptions about the underlying decision-making processes. Unfortunately, the parameter and latent activity spaces of RNNs are generally high-dimensional and uninterpretable, making it hard to use them to study individual differences. Here, we show how to benefit from the flexibility of RNNs while representing individual differences in a low-dimensional and interpretable space. To achieve this, we propose a novel end-to-end learning framework in which an encoder is trained to map the behavior of subjects into a low-dimensional latent space. These low-dimensional representations are used to generate the parameters of individual RNNs corresponding to the decision-making process of each subject. We introduce terms into the loss function that ensure that the latent dimensions are informative and disentangled, i.e., encouraged to have distinct effects on behavior. This allows them to align with separate facets of individual differences. We illustrate the performance of our framework on synthetic data as well as a dataset including the behavior of patients with psychiatric disorders.

bioinformatics↗

bioRxiv2017

Optimal Response Vigor and Choice Under Non-stationary Outcome Values

Within a rational framework a decision-maker selects actions based on the reward-maximisation principle which stipulates they acquire outcomes with the highest values at the lowest cost. Action selection can be divided into two dimensions: selecting an action from several alternatives, and choosing its vigor, i.e., how fast the selected action should be executed. Both of these dimensions depend on the values of the outcomes, and these values are often affected as more outcomes are consumed, and so are the actions. Despite this, previous works have addressed the computational substrates of optimal actions only in the specific condition that the values of outcomes are constant, and it is still unknown what the optimal actions are when the values of outcomes are non-stationary. Here, based on an optimal control framework, we derive a computational model for optimal actions under non-stationary outcome values. The results imply that even when the values of outcomes are changing, the optimal response rate is constant rather than decreasing. This finding shows that, in contrast to previous theories, the commonly observed changes in the actions cannot be purely attributed to the changes in the outcome values. We then prove that this observation can be explained based on the uncertainty about temporal horizons; e.g., in the case of experimental protocols, the session duration. We further show that when multiple outcomes are available, the model explains probability matching as well as maximisation choice strategies. The model provides, therefore, a quantitative analysis of optimal actions and explicit predictions for future testing.

animal behavior and cognition↗