Biology subjects

Bowman, H.

Publications and source records attributed to Bowman, H..

6 recordsLinked to original sources

bioRxiv2018

Understanding visual attention with RAGNAROC: A Reflexive Attention Gradient through Neural AttRactOr Competition

A quintessential challenge for any perceptual system is the need to focus on task-relevant information without being blindsided by unexpected, yet important information. The human visual system incorporates several solutions to this challenge, one of which is a reflexive covert attention system that is rapidly responsive to both the physical salience and the task-relevance of new information. This paper presents a model that simulates behavioral and neural correlates of reflexive attention as the product of brief neural attractor states that are formed across the visual hierarchy when attention is engaged. Such attractors emerge from an attentional gradient distributed over a population of topographically organized neurons and serve to focus processing at one or more locations in the visual field, while inhibiting the processing of lower priority information. The model moves towards a resolution of key debates about the nature of reflexive attention, such as whether it is parallel or serial, and whether suppression effects are distributed in a spatial surround, or selectively at the location of distractors. Most importantly, the model develops a framework for understanding the neural mechanisms of visual attention as a spatiotopic decision process within a hierarchy and links them to observable correlates such as accuracy, reaction time, and the N2pc and PD components of the EEG. This last contribution is the most crucial for repairing the disconnect that exists between our understanding of behavioral and neural correlates of attention.

neuroscience↗

bioRxiv2018

Speed of time-compressed forward replay flexibly changes in human episodic memory

Remembering information from continuous past episodes is a complex task. On the one hand, we must be able to recall events in a highly accurate way that often includes exact timing; on the other hand, we can ignore irrelevant details and skip to events of interest. We here track continuous episodes, consisting of different sub-events, as they are recalled from memory. In behavioral and MEG data, we show that memory replay is temporally compressed and proceeds in a forward direction. Neural replay is characterized by the reinstatement of temporal patterns from encoding. These fragments of activity reappear on a compressed timescale. Herein, the replay of sub-events takes longer than the transition from one sub-event to another. This identifies episodic memory replay as a dynamic process in which participants replay fragments of fine-grained temporal patterns and are able to skip flexibly across sub-events.

neuroscience↗

bioRxiv2018

Not so proportional after all?

BackgroundThe proportional recovery rule asserts that most stroke survivors recover a fixed proportion of lost function. Reports that the rule can be used to predict recovery, extraordinarily accurately, are rapidly accumulating. Here, we show that the rule may not be as powerful as it seems.\n\nMethodsWe provide a formal analysis of the relationship between baseline scores (X), outcomes (Y) and recovery (Y-X), to highlight the shortcomings of the proportional recovery rule, and illustrate those problems with simulations in which synthetic recovery data are derived from different types of recovery processes.\n\nFindingsWhen the correlation between baseline scores and recovery is stronger than that between baselines scores and outcomes, the former can create an inflated impression of how predictable outcomes really are given baseline scores. This often happens when outcomes are less variable than baseline scores, as is common in empirical studies of recovery after stroke. Moreover, we cannot use the results of these correlations to distinguish proportional recovery from recovery which is either not consistently proportional, or not proportional at all.\n\nInterpretationAnalyses relating baseline scores to subsequent change are a minefield: our formal analysis applies as consistently outside the area of stroke as it does within it. One implication of our analysis is that the proportional recovery rule is not as predictive of real recovery after stroke as recent empirical studies suggest. Another is that different analytical methods will be required to ascertain whether recovery is even proportional at all.

neuroscience↗

bioRxiv2017

Replay of Stimulus Specific Temporal Patterns during Associative Memory Formation

Forming a memory often entails the association of recent experience with present events. This recent experience is usually an information rich and dynamic representation of the world around us. We here show that associating a static cue with a previously shown dynamic stimulus, yields a detectable, dynamic representation of this stimulus in working memory. We further implicate this representation in the decrease of low-frequency power ([~]4-30 Hz) in the ongoing electroencephalogram (EEG), which is a well-known correlate of successful memory formation. The maintenance of content specific patterns in desynchronizing brain oscillations was observed in two sensory domains, i.e. in a visual and in an auditory condition. Together with previous results, these data suggest a mechanism that generalizes across domains and processes, in which the decrease in oscillatory power allows for the dynamic representation of information in ongoing brain oscillations.

neuroscience↗

bioRxiv2017

The Sync/deSync model: How a synchronized hippocampus and a de-synchronized neocortex code memories

Neural oscillations are important for memory formation in the brain. The de-synchronisation of Alpha (10Hz) oscillations in the neo-cortex has been shown to predict successful memory encoding and retrieval. However, when engaging in learning, it has been found that the hippocampus synchronises in Theta (4Hz) oscillations, and that learning is dependent on the phase of Theta. This inconsistency as to whether synchronisation is good for memory formation leads to confusion over which oscillations we should expect to see and where during learning paradigm experiments. This paper seeks to respond to this inconsistency by presenting a neural network model of how a well-functioning learning system could exhibit both of these phenomena, i.e. desynchronization of alpha and synchronisation of theta during successful memory encoding.\n\nWe present a spiking neural network (the Sync/deSync model) of the neo-cortical and hippocampal system. The simulated hippocampus learns through an adapted spike-time dependent plasticity rule, in which weight change is modulated by the phase of an extrinsically generated theta oscillation. Additionally, a global passive weight decay is incorporated, which is also modulated by theta phase. In this way, the Sync/deSync model exhibits theta phase-dependent long-term potentiation and long-term depression. We simulated a learning paradigm experiment and compared the oscillatory dynamics of our model with those observed in single-cell and scalp-EEG studies of the medial temporal lobe. Our Sync/deSync model suggests that both the de-synchronisation of neo-cortical Alpha and the synchronisation of hippocampal Theta are necessary for successful memory encoding and retrieval.\n\nSignificance statementA fundamental question is the role of rhythmic activation of neurons, i.e. how their firing oscillates between high and low rates. A particularly important question is how oscillatory dynamics between the neo-cortex and hippocampus contribute to memory formation. We present a spiking neural-network model of such memory formation, with the central ideas that 1) in neo-cortex, neurons need to break-out of an alpha oscillation in order to represent a stimulus (i.e. alpha desynchronises), while 2) in hippocampus, the firing of neurons at theta facilitates formation of memories (i.e. theta synchronises). Accordingly, successful memory formation is marked by reduced neo-cortical alpha and increased hippocampal theta. This pattern has been observed experimentally and gives our model its name - the Synch/deSynch model.

neuroscience↗

bioRxiv2017

Data-driven re-referencing of intracranial EEG based on independent component analysis (ICA)

Intracranial recordings from patients implanted with depth electrodes are a valuable source of information in cognitive neuroscience. They allow for the unique opportunity to record brain activity with a high spatial and temporal resolution. To extract the local signal of interest in stereotactic EEG (S-EEG) data, a common pre-processing choice is to re-reference the data with a bipolar montage.\n\nWith bipolar reference, each channel is subtracted from its neighbour in order to reduce commonalities between channels and isolate activity that is spatially confined. We here challenge the assumption that bipolar reference can effectively perform this task. We argue that in order to extract local activity, the distribution of the signal source of interest, as well as the distribution of interfering distant signals and noise sources need to be considered. Those can have a variable spatial extent and are modulated by electrode spacing, location and anatomical characteristics. Those factors are not accounted for by a fixed referencing scheme and bipolar reference can therefore not only decrease the signal to noise ratio (SNR) of the data, but also lead to mislocalization of activity and consequently to misinterpretation of results.\n\nWe promote the perspective of regarding referencing as a spatial filtering operation with fixed coefficients. As an alternative, we propose to use Independent Component Analysis (ICA), to derive filter coefficients that reflect the statistical dependencies of the data at hand. We argue that ICA performs the same task that bipolar referencing pursues, namely undoing the linear superposition of activity and can therefore be used to identify activity that is local. We first describe and demonstrate this procedure on human S-EEG recordings. In a simulation with real data, we then quantitatively show that ICA outperforms the bipolar referencing operation in sensitivity and importantly in specificity when it comes to revealing local time series from the superposition of neighbouring channels.

neuroscience↗