Invariant computations in local cortical networks with balanced excitation and inhibition

Mariño, Jorge; Schummers, James; Lyon, David C.; Schwabe, Lars; Beck, Oliver; Wiesing, Peter; Obermayer, Klaus; Sur, Mriganka

doi:10.1038/nn1391

Cited by 289 publications

(305 citation statements)

References 48 publications

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“…In all cells, the strong dynamic synaptic f luctuations provide the intrinsic ''noise'' to yield near contrast invariance. We examine the role of V1 architecture on orientation selectivity and show that, although extracellular selectivity for orientation is roughly independent of cortical location, intracellular measures are not, differing between neurons in iso-orientation regions and those near pinwheel centers, consistent with recent experiments (16). Finally, the role of synaptic f luctuations is elucidated in detail by studying the bifurcation structure of network activity in an idealized network model with statistically homogeneous coupling.…”

supporting

confidence: 73%

Orientation selectivity in visual cortex by fluctuation-controlled criticality

Tao

Cai²,

McLaughlin³

et al. 2006

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

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Within a large-scale neuronal network model of macaque primary visual cortex, we examined how intrinsic dynamic fluctuations in synaptic currents modify the effect of strong recurrent excitation on orientation selectivity. Previously, we showed that, using a strong network inhibition countered by feedforward and recurrent excitation, the cortical model reproduced many observed properties of simple and complex cells. However, that network's complex cells were poorly selective for orientation, and increasing cortical self-excitation led to network instabilities and unrealistically high firing rates. Here, we show that a sparsity of connections in the network produces large, intrinsic fluctuations in the cortico-cortical conductances that can stabilize the network and that there is a critical level of fluctuations (controllable by sparsity) that allows strong cortical gain and the emergence of orientation-selective complex cells. The resultant sparse network also shows near contrast invariance in its selectivity and, in agreement with recent experiments, has extracellular tuning properties that are similar in pinwheel center and iso-orientation regions, whereas intracellular conductances show positional dependencies. Varying the strength of synaptic fluctuations by adjusting the sparsity of network connectivity, we identified a transition between the dynamics of bistability and without bistability. In a network with strong recurrent excitation, this transition is characterized by a near hysteretic behavior and a rapid rise of network firing rates as the synaptic drive or stimulus input is increased. We discuss the connection between this transition and orientation selectivity in our model of primary visual cortex.hypercolumns ͉ numerical simulation ͉ V1 ͉ circular variance O rientation selectivity and spatial summation (1) are two fundamental attributes of visual processing performed by the mammalian primary visual cortex (V1). V1 is the first cortical area along the visual pathway where neurons are strongly selective for stimulus orientation. Moreover, measurements of orientation selectivity for individual neurons, such as the tuning curve bandwidth (half-width at half maximum), circular variance (CV), ¶ and orientation selectivity index, often show near independence of stimulus contrast. V1 neurons are also classified as either ''simple'' or ''complex'' based on their spatial summation properties. Simple cells respond to visual stimulation in an approximately linear fashion, whereas complex cells respond more nonlinearly. For example, when driven by drifting sinusoidal gratings, a simple cell follows the temporal modulation of the drifting grating as the grating moves across the receptive field of the neuron, whereas complex cells show an elevated but unmodulated response. Quantitatively, simple and complex cellular responses are often differentiated by the modulation ratio F1͞F0 (at preferred stimulus orientation, the ratio of the first Fourier component and the mean) of the cycle-averaged firing rate (2): cells...

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supporting

confidence: 73%

Orientation selectivity in visual cortex by fluctuation-controlled criticality

Tao

Cai²,

McLaughlin³

et al. 2006

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

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“…Indeed, coding efficiency depends on the shape of the neurons' tuning curves and on the structure of neuronal correlations. Importantly, despite the difference in local inputs to neurons in pinwheel centers and orientation domains, both these classes of cells have been described to have similar orientation tuning characteristics (9,34,35). In addition, optical imaging and electrophysiological studies have previously reported (36,37) that correlations between neurons depend only on the relative separation between cells and are independent on the cells' location on the cortical surface.…”

Section: Discussionmentioning

confidence: 99%

Efficient coding in heterogeneous neuronal populations

Chelaru

Dragoi

2008

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

141

148

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A ubiquitous feature of neuronal responses within a cortical area is their high degree of inhomogeneity. Even cells within the same functional column are known to have highly heterogeneous response properties when the same stimulus is presented. Whether the wide diversity of neuronal responses is an epiphenomenon or plays a role for cortical function is unknown. Here, we examined the relationship between the heterogeneity of neuronal responses and population coding. Contrary to our expectation, we found that the high variability of intrinsic response properties of individual cells changes the structure of neuronal correlations to improve the information encoded in the population activity. Thus, the heterogeneity of neuronal responses is in fact beneficial for sensory coding when stimuli are decoded from the population response.computational modeling ͉ information coding ͉ sensory cortex ͉ visual cortex ͉ neuronal populations

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“…Since connections between cortical areas are excitatory, local recurrent inhibition would be needed to accomplish this. This, then, provides a simple explanation for the information benefit of recurrent networks that balance large excitatory inputs with local recurrent inhibition, a widely observed property of cortical circuits (Marino et al, 2005).…”

Section: Cortical Expansion Redundant Codes and Balanced Excitationmentioning

confidence: 94%

Insights from a Simple Expression for Linear Fisher Information in a Recurrently Connected Population of Spiking Neurons

2011

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A simple expression for a lower bound of Fisher information is derived for a network of recurrently connected spiking neurons that have been driven to a noise-perturbed steady state. We call this lower bound linear Fisher information, as it corresponds to the Fisher information that can be recovered by a locally optimal linear estimator. Unlike recent similar calculations, the approach used here includes the effects of nonlinear gain functions and correlated input noise and yields a surprisingly simple and intuitive expression that offers substantial insight into the sources of information degradation across successive layers of a neural network. Here, this expression is used to (1) compute the optimal (i.e., informationmaximizing) firing rate of a neuron, (2) demonstrate why sharpening tuning curves by either thresholding or the action of recurrent connectivity is generally a bad idea, (3) show how a single cortical expansion is sufficient to instantiate a redundant population code that can propagate across multiple cortical layers with minimal information loss, and (4) show that optimal recurrent connectivity strongly depends on the covariance structure of the inputs to the network.

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Invariant computations in local cortical networks with balanced excitation and inhibition

Cited by 289 publications

References 48 publications

Orientation selectivity in visual cortex by fluctuation-controlled criticality

Orientation selectivity in visual cortex by fluctuation-controlled criticality

Efficient coding in heterogeneous neuronal populations

Insights from a Simple Expression for Linear Fisher Information in a Recurrently Connected Population of Spiking Neurons

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