Biology subjects

Friedman, D.

Publications and source records attributed to Friedman, D..

4 recordsLinked to original sources

bioRxiv2018

More is Better: Using Machine Learning Techniques and Multiple EEG Metrics to Increase Preference Prediction Above and Beyond Traditional Measurements

A basic aim of marketing research is to predict consumers preferences and the success of marketing campaigns in the general population. However, traditional behavioral measurements have various limitations, calling for novel measurements to improve predictive power. In this study, we use neural signals measured with electroencephalography (EEG) in order to overcome these limitations. We record the EEG signals of subjects, as they watched commercials of six food products. We introduce a novel approach in which instead of using one type of EEG measure, we combine several measures, and use state-of-the-art machine learning algorithms to predict subjects individual future preferences over the products and the commercials population success, as measured by their YouTube metrics. As a benchmark, we acquired measurements of the commercials effectiveness using a standard questionnaire commonly used in marketing research. We reached 68.5% accuracy in predicting between the most and least preferred items and a lower than chance RMSE score for predicting the rank order preferences of all six products. We also predicted the commercials population success better than chance. Most importantly, we demonstrate for the first time, that for all of our predictions, the EEG measurements increased the prediction power of the questionnaires. Our analyses methods and results show great promise for utilizing EEG measures by managers, marketing practitioners, and researchers, as a valuable tool for predicting subjects preferences and marketing campaigns success.

neuroscience↗

bioRxiv2018

Hippocampal signature of associative memory measured by chronic ambulatory intracranial EEG

Some patients with medically refractory focal epilepsy are chronically implanted with a brain-responsive neurostimulation device (the RNS(R) System), permitting neurophysiological measurements at millisecond resolution. This clinical device can be adapted to measure hippocampal dynamics time-locked to cognitive tasks. We illustrate the technique with a proof of concept in three patients previously implanted with the RNS System as they engage in an associative memory task, measured months apart. Hippocampal activity measured in successful encoding in RNS System patients mirrors that in surgical patients during intracranial electroencephalography (iEEG), suggesting that chronic iEEG allows sensitive measurements of hippocampal physiology over prolonged timescales.

neuroscience↗

bioRxiv2017

Replay of large-scale spatio-temporal patterns from waking during subsequent slow-wave sleep in human cortex

Animal studies support the hypothesis that in slow-wave sleep, replay of waking neocortical activity under hippocampal guidance leads to memory consolidation. However, no intracranial electrophysiological evidence for replay exists in humans. We identified consistent sequences of population firing peaks across widespread cortical regions during complete waking periods. The occurrence of these Motifs were compared between sleeps preceding the waking period (Sleep-Pre) when the Motifs were identified, and those following (Sleep-Post). In all subjects, the majority of waking Motifs (most of which were novel) had more matches in Sleep-Post than in Sleep-Pre. In rodents, hippocampal replay occurs during local sharp-wave ripples, and the associated neocortical replay tends to occur during local sleep spindles and down-to-up transitions. These waves may facilitate consolidation by sequencing cell-firing and encouraging plasticity. Similarly, we found that Motifs were coupled to neocortical spindles, down-to-up transitions, theta bursts, and hippocampal sharp-wave ripples. While Motifs occurring during cognitive task performance were more likely to have more matches in subsequent sleep, our studies provide no direct demonstration that the replay of Motifs contributes to consolidation. Nonetheless, these results confirm a core prediction of the dominant neurobiological theory of human memory consolidation.

neuroscience↗

bioRxiv2017

Mean-Field Equations For Neuronal Networks With Arbitrary Degree Distributions

The emergent dynamics in networks of recurrently coupled spiking neurons depends on the interplay between single-cell dynamics and network topology. Most theoretical studies on network dynamics have assumed simple topologies, such as connections which are made randomly and independently with a fixed probability (Erdos-Renyi network) (ER), or all-to-all connected networks. However, recent findings from slice experiments suggest that the actual patterns of connectivity between cortical neurons are more structured than in the ER random network. Here we explore how introducing additional higher-order statistical structure into the connectivity can affect the dynamics in neuronal networks. Specifically, we consider networks in which the number of pre-synaptic and post-synaptic contacts for each neuron, the degrees, are drawn from a joint degree distribution. We derive mean-field equations for a single population of homogeneous neurons and for a network of excitatory and inhibitory neurons, where the neurons can have arbitrary degree distributions. Through analysis of the mean-field equations and simulation of networks of integrate-and-fire neurons, we show that such networks have potentially much richer dynamics than an equivalent ER network. Finally, we relate the degree distributions to so-called cortical motifs.

neuroscience↗