Biology subjects

Rosenbaum, R.

Publications and source records attributed to Rosenbaum, R..

4 recordsLinked to original sources

bioRxiv2018

The correlated state in balanced neuronal networks

Understanding the magnitude and structure of inter-neuronal correlations and their relationship to synaptic connectivity structure is an important and difficult problem in computational neuroscience. Early studies show that neuronal network models with excitatory-inhibitory balance naturally create very weak spike train correlations. Later work showed that, under some connectivity structures, balanced networks can produce larger correlations between some neuron pairs, even when the average correlation is very small. All of these previous studies assume that the local neuronal network receives feedforward synaptic input from a population of uncorrelated spike trains. We show that when spike trains providing feedforward input are correlated, the downstream recurrent neuronal network produces much larger correlations. We provide an in-depth analysis of the resulting \"correlated state\" in balanced networks and show that, unlike the asynchronous state of previous work, it produces a tight excitatory-inhibitory balance consistent with in vivo cortical recordings.

neuroscience↗

bioRxiv2017

Circuit-based models of shared variability in cortical networks

Trial-to-trial variability is a reflection of the circuitry and cellular physiology that makeup a neuronal network. A pervasive yet puzzling feature of cortical circuits is that despite their complex wiring, population-wide shared spiking variability is low dimensional with all neurons fluctuating en masse. Previous model cortical networks are at loss to explain this global variability, and rather assume it is from external sources. We show that if the spatial and temporal scales of inhibitory coupling match known physiology, model spiking neurons internally generate low dimensional shared variability that captures the properties of in vivo population recordings along the visual pathway. Shifting spatial attention into the receptive field of visual neurons has been shown to reduce low dimensional shared variability within a brain area, yet increase the variability shared between areas. A top-down modulation of inhibitory neurons in our network provides a parsimonious mechanism for this attentional modulation, providing support for our theory of cortical variability. Our work provides a critical and previously missing mechanistic link between observed cortical circuit structure and realistic population-wide shared neuronal variability and its modulation.

neuroscience↗

bioRxiv2017

Imbalanced amplification: A mechanism of amplification and suppression from local imbalance of excitation and inhibition in cortical circuits

Understanding the relationship between external stimuli and the spiking activity of cortical populations is a central problem in neuroscience. Dense recurrent connectivity in local cortical circuits can lead to counterintuitive response properties, raising the question of whether there are simple arithmetical rules for relating circuits connectivity structure to their response properties. One such arithmetic is provided by the mean field theory of balanced networks, which is derived in a limit where excitatory and inhibitory synaptic currents precisely balance on average. However, balanced network theory is not applicable to some biologically relevant connectivity structures. We show that cortical circuits with such structure are susceptible to an amplification mechanism arising when excitatory-inhibitory balance is broken at the level of local subpopulations, but maintained at a global level. This amplification, which can be quantified by a linear correction to the classical mean field theory of balanced networks, explains several response properties observed in cortical recordings.

neuroscience↗

bioRxiv2017

From the statistics of connectivity to the statistics of spike times in neuronal networks

An essential step toward understanding neural circuits is linking their structure and their dynamics. In general, this relationship can be almost arbitrarily complex. Recent theoretical work has, however, begun to identify some broad principles underlying collective spiking activity in neural circuits. The first is that local features of network connectivity can be surprisingly effective in predicting global statistics of activity across a network. The second is that, for the important case of large networks with excitatory-inhibitory balance, correlated spiking persists or vanishes depending on the spatial scales of recurrent and feedforward connectivity. We close by showing how these ideas, together with plasticity rules, can help to close the loop between network structure and activity statistics.\n\nHighlightsO_LIRemarkable new data on connectivity and activity raise the promise and raise the bar for linking structure and dynamics in neural networks.\nC_LIO_LIRecent theories aim at a statistical approach, in which the enormous complexity of wiring diagrams is reduced to key features of that connectivity that drive coherent, network-wide activity.\nC_LIO_LIWe provide a unified view of three branches of this work, tied to a broadly useful \"neural response\" formula that explicitly relates connectivity to spike train statistics.\nC_LIO_LIThis isolates a surprisingly systematic role for the local structure and spatial scale of connectivity in determining spike correlations, and shows how the coevolution of structured connectivity and spiking statistics through synaptic plasticity can be predicted self-consistently.\nC_LI

neuroscience↗