Bistability and Spatiotemporal Irregularity in Neuronal Networks with Nonlinear Synaptic Transmission

The circuit mechanisms behind shared neural variability (noise correlation) and its dependence on neural state are poorly understood. Visual attention is well-suited to constrain cortical models of response variability because attention both increases firing rates and their stimulus sensitivity, as well as decreases noise correlations. We provide a novel analysis of population recordings in rhesus primate visual area V4 showing that a single biophysical mechanism may underlie these diverse neural correlates of attention. We explore model cortical networks where top-down mediated increases in excitability, distributed across excitatory and inhibitory targets, capture the key neuronal correlates of attention. Our models predict that top-down signals primarily affect inhibitory neurons, whereas excitatory neurons are more sensitive to stimulus specific bottom-up inputs. Accounting for trial variability in models of state dependent modulation of neuronal activity is a critical step in building a mechanistic theory of neuronal cognition.DOI: http://dx.doi.org/10.7554/eLife.23978.001

show abstract

“…Rather, if we scale

J_{i j} \sim 1 / \sqrt{N}

, as would be the case for classical balanced networks (van Vreeswijk and Sompolinsky, 1998), then for very large

N

the solution no longer depends upon the gain

L

. Finite

N

or the inclusion of synaptic nonlinearities through short term plasticity (Mongillo et al, 2012) may be necessary to satisfy condition

bold𝐂𝟐

with large synapses. Furthermore, the large synaptic weights associated with

J_{i j} \sim 1 / \sqrt{N}

do not allows us to neglect polysynaptic paths, as is needed for Equation (5).…”

Section: Resultsmentioning

confidence: 99%

Attentional modulation of neuronal variability in circuit models of cortex

Kanashiro

Ocker

Cohen

et al. 2017

eLife

101

View full text Add to dashboard Cite

show abstract

“…Such activity is typically seen in working memory, and various solutions to this problem have been proposed, which include the uses of slow reverberating synaptic current (Wang, 1999), balanced excitatory and inhibitory synaptic input (Renart et al, 2007; Roudi and Latham, 2007; Mongillo et al, 2012), short-term synaptic depression (Barbieri and Brunel, 2007; Mongillo et al, 2012), and modular network structure (Lundqvist et al, 2010). In this study, we have shown that long-tailed distributions of EPSP amplitudes generate a highly irregular persistent activity in the memory retrieval, as was the case in spontaneous activity (Teramae et al, 2012).…”

Section: Discussionmentioning

confidence: 99%

Associative memory model with long-tail-distributed Hebbian synaptic connections

Hiratani¹,

Teramae²,

Fukai³

2013

Front. Comput. Neurosci.

View full text Add to dashboard Cite

The postsynaptic potentials of pyramidal neurons have a non-Gaussian amplitude distribution with a heavy tail in both hippocampus and neocortex. Such distributions of synaptic weights were recently shown to generate spontaneous internal noise optimal for spike propagation in recurrent cortical circuits. However, whether this internal noise generation by heavy-tailed weight distributions is possible for and beneficial to other computational functions remains unknown. To clarify this point, we construct an associative memory (AM) network model of spiking neurons that stores multiple memory patterns in a connection matrix with a lognormal weight distribution. In AM networks, non-retrieved memory patterns generate a cross-talk noise that severely disturbs memory recall. We demonstrate that neurons encoding a retrieved memory pattern and those encoding non-retrieved memory patterns have different subthreshold membrane-potential distributions in our model. Consequently, the probability of responding to inputs at strong synapses increases for the encoding neurons, whereas it decreases for the non-encoding neurons. Our results imply that heavy-tailed distributions of connection weights can generate noise useful for AM recall.

show abstract

“…Balanced networks at low rates show a linear input-output relation (38), whereas bistability requires nonlinearities (22,24,27,39). Extensive theoretical work has aimed to reconcile multistability and irregular firing, mainly in the context of persistent activity circuits (40,41). We simplified the problem and, assuming a balanced state during active periods, built a rate network model to investigate the transition dynamics between the two attractors.…”

Section: Discussionmentioning

confidence: 99%

Stochastic transitions into silence cause noise correlations in cortical circuits

Mochol

Hermoso-Mendizabal

Sakata

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

The spiking activity of cortical neurons is highly variable. This variability is generally correlated among nearby neurons, an effect commonly interpreted to reflect the coactivation of neurons due to anatomically shared inputs. Recent findings, however, indicate that correlations can be dynamically modulated, suggesting that the underlying mechanisms are not well understood. Here, we investigate the hypothesis that correlations are dominated by neuronal coinactivation: the occurrence of brief silent periods during which all neurons in the local network stop firing. We recorded spiking activity from large populations of neurons in the auditory cortex of anesthetized rats across different brain states. During spontaneous activity, the reduction of correlation accompanying brain state desynchronization was largely explained by a decrease in the density of the silent periods. The presentation of a stimulus caused an initial drop of correlations followed by a rebound, a time course that was mimicked by the instantaneous silence density. We built a rate network model with fluctuation-driven transitions between a silent and an active attractor and assumed that neurons fired Poisson spike trains with a rate following the model dynamics. Variations of the network external input altered the transition rate into the silent attractor and reproduced the relation between correlation and silence density found in the data, both in spontaneous and evoked conditions. This suggests that the observed changes in correlation, occurring gradually with brain state variations or abruptly with sensory stimulation, are due to changes in the likeliness of the microcircuit to transiently cease firing.neuronal variability | noise correlations | brain state | auditory cortex | stochastic network dynamics N euronal noise correlations are defined as common fluctuations in the spiking activity of neurons under conditions of constant sensory input or motor output. Traditionally, they have been thought to arise from the dense connectivity of the cortex, such that neighboring neurons sharing a fraction of their inputs should also share a fraction of their output variability (1). Several observations are consistent with this hypothesis: pairwise correlations in the cortex decrease with cell pair distance (2) or with the difference in stimulus selectivity (3), dependencies that could follow from a variation in shared input given the anatomy of cortical circuits. Recent findings, however, challenge this simple interpretation. Recordings in the primate visual cortex have shown that attention or task context can change correlation structure (4-6) and that the magnitude of averaged correlation can be very low (7). In anesthetized rodents correlations decrease with brain state desynchronization (8, 9) or when animals switch from quiet wakefulness to active whisking during waking (10). Moreover, the commonly observed drop of spiking variability following stimulus onset (11-13) seems to occur jointly with a transient decrease in correlation (2, 14, 15). Th...

show abstract

Bistability and Spatiotemporal Irregularity in Neuronal Networks with Nonlinear Synaptic Transmission

Cited by 81 publications

References 21 publications

Attentional modulation of neuronal variability in circuit models of cortex

Attentional modulation of neuronal variability in circuit models of cortex

Associative memory model with long-tail-distributed Hebbian synaptic connections

Stochastic transitions into silence cause noise correlations in cortical circuits

Contact Info

Product

Resources

About