Analyzing Short-Term Noise Dependencies of Spike-Counts in Macaque Prefrontal Cortex Using Copulas and the Flashlight Transformation

Simultaneous spike-counts of neural populations are typically modeled by a Gaussian distribution. On short time scales, however, this distribution is too restrictive to describe and analyze multivariate distributions of discrete spike-counts. We present an alternative that is based on copulas and can account for arbitrary marginal distributions, including Poisson and negative binomial distributions as well as second and higher-order interactions. We describe maximum likelihood-based procedures for fitting copula-based models to spike-count data, and we derive a so-called flashlight transformation which makes it possible to move the tail dependence of an arbitrary copula into an arbitrary orthant of the multivariate probability distribution. Mixtures of copulas that combine different dependence structures and thereby model different driving processes simultaneously are also introduced. First, we apply copula-based models to populations of integrate-and-fire neurons receiving partially correlated input and show that the best fitting copulas provide information about the functional connectivity of coupled neurons which can be extracted using the flashlight transformation. We then apply the new method to data which were recorded from macaque prefrontal cortex using a multi-tetrode array. We find that copula-based distributions with negative binomial marginals provide an appropriate stochastic model for the multivariate spike-count distributions rather than the multivariate Poisson latent variables distribution and the often used multivariate normal distribution. The dependence structure of these distributions provides evidence for common inhibitory input to all recorded stimulus encoding neurons. Finally, we show that copula-based models can be successfully used to evaluate neural codes, e. g., to characterize stimulus-dependent spike-count distributions with information measures. This demonstrates that copula-based models are not only a versatile class of models for multivariate distributions of spike-counts, but that those models can be exploited to understand functional dependencies

Bump attractor dynamics in prefrontal cortex explains behavioral precision in spatial working memory

Prefrontal persistent activity during the delay of spatial working memory tasks is thought to maintain spatial location in memory. A 'bump attractor' computational model can account for this physiology and its relationship to behavior. However, direct experimental evidence linking parameters of prefrontal firing to the memory report in individual trials is lacking, and, to date, no demonstration exists that bump attractor dynamics underlies spatial working memory. We analyzed monkey data and found model-derived predictive relationships between the variability of prefrontal activity in the delay and the fine details of recalled spatial location, as evident in trial-to-trial imprecise oculomotor responses. Our results support a diffusing bump representation for spatial working memory instantiated in persistent prefrontal activity. These findings reinforce persistent activity as a basis for spatial working memory, provide evidence for a continuous prefrontal representation of memorized space and offer experimental support for bump attractor dynamics mediating cognitive tasks in the cortex

Fundamental activity constraints lead to specific interpretations of the connectome

The continuous integration of experimental data into coherent models of the brain is an increasing challenge of modern neuroscience. Such models provide a bridge between structure and activity, and identify the mechanisms giving rise to experimental observations. Nevertheless, structurally realistic network models of spiking neurons are necessarily underconstrained even if experimental data on brain connectivity are incorporated to the best of our knowledge. Guided by physiological observations, any model must therefore explore the parameter ranges within the uncertainty of the data. Based on simulation results alone, however, the mechanisms underlying stable and physiologically realistic activity often remain obscure. We here employ a mean-field reduction of the dynamics, which allows us to include activity constraints into the process of model construction. We shape the phase space of a multi-scale network model of the vision-related areas of macaque cortex by systematically refining its connectivity. Fundamental constraints on the activity, i.e., prohibiting quiescence and requiring global stability, prove sufficient to obtain realistic layer- and area-specific activity. Only small adaptations of the structure are required, showing that the network operates close to an instability. The procedure identifies components of the network critical to its collective dynamics and creates hypotheses for structural data and future experiments. The method can be applied to networks involving any neuron model with a known gain function.Comment: J. Schuecker and M. Schmidt contributed equally to this wor

Intrinsic neuronal dynamics predict distinct functional roles during working memory

Working memory (WM) is characterized by the ability to maintain stable representations over time; however, neural activity associated with WM maintenance can be highly dynamic. We explore whether complex population coding dynamics during WM relate to the intrinsic temporal properties of single neurons in lateral prefrontal cortex (lPFC), the frontal eye fields (FEF), and lateral intraparietal cortex (LIP) of two monkeys (Macaca mulatta). We find that cells with short timescales carry memory information relatively early during memory encoding in lPFC; whereas long-timescale cells play a greater role later during processing, dominating coding in the delay period. We also observe a link between functional connectivity at rest and the intrinsic timescale in FEF and LIP. Our results indicate that individual differences in the temporal processing capacity predict complex neuronal dynamics during WM, ranging from rapid dynamic encoding of stimuli to slower, but stable, maintenance of mnemonic information.Biotechnology and Biological Sciences Research Council (Great Britain) (BB/M010732/1)United States. Office of Naval Research (N00014-14-1-0681)National Institute of Mental Health (U.S.) (R00MH092715)National Institute of Mental Health (U.S.) (R37MH087027)Massachusetts Institute of Technology. Picower Innovation FundUnited States. Office of Naval Research. Multidisciplinary University Research Initiative (grant N00014-16-1-2832)National Institute for Health Research (Great Britain). Wellcome Trust (203139/Z/16/Z

Neural network mechanisms of working memory interference

[eng] Our ability to memorize is at the core of our cognitive abilities. How could we effectively make decisions without considering memories of previous experiences? Broadly, our memories can be divided in two categories: long-term and short-term memories. Sometimes, short-term memory is also called working memory and throughout this thesis I will use both terms interchangeably. As the names suggest, long-term memory is the memory you use when you remember concepts for a long time, such as your name or age, while short-term memory is the system you engage while choosing between different wines at the liquor store. As your attention jumps from one bottle to another, you need to hold in memory characteristics of previous ones to pick your favourite. By the time you pick your favourite bottle, you might remember the prices or grape types of the other bottles, but you are likely to forget all of those details an hour later at home, opening the wine in front of your guests. The overall goal of this thesis is to study the neural mechanisms that underlie working memory interference, as reflected in quantitative, systematic behavioral biases. Ultimately, the goal of each chapter, even when focused exclusively on behavioral experiments, is to nail down plausible neural mechanisms that can produce specific behavioral and neurophysiological findings. To this end, we use the bump-attractor model as our working hypothesis, with which we often contrast the synaptic working memory model. The work performed during this thesis is described here in 3 main chapters, encapsulation 5 broad goals: In Chapter 4.1, we aim at testing behavioral predictions of a bump-attractor (1) network when used to store multiple items. Moreover, we connected two of such networks aiming to model feature-binding through selectivity synchronization (2). In Chapter 4.2, we aim to clarify the mechanisms of working memory interference from previous memories (3), the so-called serial biases. These biases provide an excellent opportunity to contrast activity-based and activity-silent mechanisms because both mechanisms have been proposed to be the underlying cause of those biases. In Chapter 4.3, armed with the same techniques used to seek evidence for activity-silent mechanisms, we test a prediction of the bump-attractor model with short-term plasticity (4). Finally, in light of the results from aim 4 and simple computer simulations, we reinterpret previous studies claiming evidence for activity-silent mechanisms (5)