Biology subjects

Bowman, H.

Publications and source records attributed to Bowman, H..

21 records · Page 2Linked to original sources

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↗