Article

Preserving Information in Neural Transmission

Beckman Vision Center, University of California, San Francisco, California 94143, USA.
The Journal of Neuroscience : The Official Journal of the Society for Neuroscience (Impact Factor: 6.75). 05/2009; 29(19):6207-16. DOI: 10.1523/JNEUROSCI.3701-08.2009
Source: PubMed

ABSTRACT Along most neural pathways, the spike trains transmitted from one neuron to the next are altered. In the process, neurons can either achieve a more efficient stimulus representation, or extract some biologically important stimulus parameter, or succeed at both. We recorded the inputs from single retinal ganglion cells and the outputs from connected lateral geniculate neurons in the macaque to examine how visual signals are relayed from retina to cortex. We found that geniculate neurons re-encoded multiple temporal stimulus features to yield output spikes that carried more information about stimuli than was available in each input spike. The coding transformation of some relay neurons occurred with no decrement in information rate, despite output spike rates that averaged half the input spike rates. This preservation of transmitted information was achieved by the short-term summation of inputs that geniculate neurons require to spike. A reduced model of the retinal and geniculate visual responses, based on two stimulus features and their associated nonlinearities, could account for >85% of the total information available in the spike trains and the preserved information transmission. These results apply to neurons operating on a single time-varying input, suggesting that synaptic temporal integration can alter the temporal receptive field properties to create a more efficient representation of visual signals in the thalamus than the retina.

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    • "We found that the amount of information per spike decreased as the ISI increased. This finding, together with the previously presented results, suggests that ISI-based filtering of retinal spike trains is part of the mechanism that helps in preserving information about the important features of visual stimuli as it travels from retina to cortex, increasing the information efficiency to improve signaling the optimal stimulus features as has been suggested also by recent studies in macaque and cat [22] [23] [29] "
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    ABSTRACT: The information regarding visual stimulus is encoded in spike trains at the output of retina by retinal ganglion cells (RGCs). Among these, the directional selective cells (DSRGC) are signaling the direction of stimulus motion. DSRGCs' spike trains show accentuated periods of short interspike intervals (ISIs) framed by periods of isolated spikes. Here we use two types of visual stimulus, white noise and drifting bars, and show that short ISI spikes of DSRGCs spike trains are more often correlated to their preferred stimulus feature (that is, the direction of stimulus motion) and carry more information than longer ISI spikes. Firstly, our results show that correlation between stimulus and recorded neuronal response is best at short ISI spiking activity and decrease as ISI becomes larger. We then used grating bars stimulus and found that as ISI becomes shorter the directional selectivity is better and information rates are higher. Interestingly, for the less encountered type of DSRGC, known as ON-DSRGC, short ISI distribution and information rates revealed consistent differences when compared with the other directional selective cell type, the ON-OFF DSRGC. However, these findings suggest that ISI-based temporal filtering integrates a mechanism for visual information processing at the output of retina toward higher stages within early visual system.
    Computational Intelligence and Neuroscience 08/2012; 2012:918030. DOI:10.1155/2012/918030
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    • "In fact, our recent work has demonstrated that the inclusion of simple spike-history dependence in the GLM framework can to some degree temporally sharpen the temporal profile of the geniculate response through interactions with the correlation structure of the visual input (Desbordes et al., 2010), and the inclusion of explicitly nonlinear models of inhibitory surrounds can further capture the fine timing precision of LGN neurons (Butts et al., 2007, 2011). Further, recent modeling of geniculate neurons using techniques that capture multiple stimulus projections suggest that such strategies enhance the ability to capture the information conveyed by early visual neurons (Sincich et al., 2009; X. Wang et al., 2010), and thus may be another means by which to capture the features that go beyond the simple LN model. "
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    ABSTRACT: Thalamic neurons respond to visual scenes by generating synchronous spike trains on the timescale of 10-20 ms that are very effective at driving cortical targets. Here we demonstrate that this synchronous activity contains unexpectedly rich information about fundamental properties of visual stimuli. We report that the occurrence of synchronous firing of cat thalamic cells with highly overlapping receptive fields is strongly sensitive to the orientation and the direction of motion of the visual stimulus. We show that this stimulus selectivity is robust, remaining relatively unchanged under different contrasts and temporal frequencies (stimulus velocities). A computational analysis based on an integrate-and-fire model of the direct thalamic input to a layer 4 cortical cell reveals a strong correlation between the degree of thalamic synchrony and the nonlinear relationship between cortical membrane potential and the resultant firing rate. Together, these findings suggest a novel population code in the synchronous firing of neurons in the early visual pathway that could serve as the substrate for establishing cortical representations of the visual scene.
    The Journal of Neuroscience : The Official Journal of the Society for Neuroscience 06/2012; 32(26):9073-88. DOI:10.1523/JNEUROSCI.4968-11.2012 · 6.75 Impact Factor
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    • "Finding out how the LGN processes visual information and determining what is the nature of the LGN's intricate circuitry have garnered great interest for decades (Kaplan and Shapley 1982; Derrington and Lennie 1984; Blakemore and Vital-Durand 1986; Irvin et al. 1993; Carandini et al. 2005; Sherman 2005; Casti et al. 2008; Sincich et al. 2009, among others). Yet it remains a challenge to characterize by computational modeling what is important for visual function in the LGN input to V1. "
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    ABSTRACT: One of the reasons the visual cortex has attracted the interest of computational neuroscience is that it has well-defined inputs. The lateral geniculate nucleus (LGN) of the thalamus is the source of visual signals to the primary visual cortex (V1). Most large-scale cortical network models approximate the spike trains of LGN neurons as simple Poisson point processes. However, many studies have shown that neurons in the early visual pathway are capable of spiking with high temporal precision and their discharges are not Poisson-like. To gain an understanding of how response variability in the LGN influences the behavior of V1, we study response properties of model V1 neurons that receive purely feedforward inputs from LGN cells modeled either as noisy leaky integrate-and-fire (NLIF) neurons or as inhomogeneous Poisson processes. We first demonstrate that the NLIF model is capable of reproducing many experimentally observed statistical properties of LGN neurons. Then we show that a V1 model in which the LGN input to a V1 neuron is modeled as a group of NLIF neurons produces higher orientation selectivity than the one with Poisson LGN input. The second result implies that statistical characteristics of LGN spike trains are important for V1's function. We conclude that physiologically motivated models of V1 need to include more realistic LGN spike trains that are less noisy than inhomogeneous Poisson processes.
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