Formal and attribute-specific information in primary visual cortex

Laboratory of Biophysics, The Rockefeller University, New York, New York 10021, USA.
Journal of Neurophysiology (Impact Factor: 2.89). 02/2001; 85(1):305-18.
Source: PubMed


We estimate the rates at which neurons in the primary visual cortex (V1) of anesthetized macaque monkeys transmit stimulus-related information in response to three types of visual stimulus. The stimuli-randomly modulated checkerboard patterns, stationary sinusoidal gratings, and drifting sinusoidal gratings-have very different spatiotemporal structures. We obtain the overall rate of information transmission, which we call formal information, by a direct method. We find the highest information rates in the responses of simple cells to drifting gratings (median: 10.3 bits/s, 0.92 bits/spike); responses to randomly modulated stimuli and stationary gratings transmit information at significantly lower rates. In general, simple cells transmit information at higher rates, and over a larger range, than do complex cells. Thus in the responses of V1 neurons, stimuli that are rapidly modulated do not necessarily evoke higher information rates, as might be the case with motion-sensitive neurons in area MT. By an extension of the direct method, we parse the formal information into attribute-specific components, which provide estimates of the information transmitted about contrast and spatiotemporal pattern. We find that contrast-specific information rates vary across neurons-about 0.3 to 2.1 bits/s or 0.05 to 0.22 bits/spike-but depend little on stimulus type. Spatiotemporal pattern-specific information rates, however, depend strongly on the type of stimulus and neuron (simple or complex). The remaining information rate, typically between 10 and 32% of the formal information rate for each neuron, cannot be unambiguously assigned to either contrast or spatiotemporal pattern. This indicates that some information concerning these two stimulus attributes is confounded in the responses of single neurons in V1. A model that considers a simple cell to consist of a linear spatiotemporal filter followed by a static rectifier predicts higher information rates than are found in real neurons and completely fails to replicate the performance of real cells in generating the confounded information.

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Available from: Jonathan D Victor, Dec 24, 2013
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    • "Many of these cells show encoding limits lower than 10 ms. Information rates measured in central neurons of different animals ranging from insects up to monkeys are much smaller than the ones typically found for sensory receptors (see for overview: Borst and Theunissen, 1999; fly H1 and monkey MT, (Strong et al., 1998); retinal ganglion cells (Koch et al., 2004; Passaglia and Troy, 2004); LGN neurons, (Sincich et al., 2009); V1 simple cells, (Reich et al., 2001). From these published data it seems reasonable to assume that peripheral receptors in general are optimized to convey maximal sensory information at fast rates. "
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    ABSTRACT: Sensory receptors determine the type and the quantity of information available for perception. Here, we quantified and characterized the information transferred by primary afferents in the rat whisker system using neural system identification. Quantification of ‘how much’ information is conveyed by primary afferents, using the direct method, a classical information theoretic tool, revealed that primary afferents transfer huge amounts of information (up to 529 bits/s). Information theoretic analysis of instantaneous spike-triggered kinematic stimulus features was used to gain functional insight on ‘what’ is coded by primary afferents. Amongst the kinematic variables tested - position, velocity, and acceleration - primary afferent spikes encoded velocity best. The other two variables contribute to information transfer, but only if combined with velocity. We further revealed three additional characteristics that play a role in information transfer by primary afferents. Firstly, primary afferent spikes show preference for well separated multiple stimuli (i.e. well separated sets of combinations of the three instantaneous kinematic variables). Secondly, spikes are sensitive to short strips of the stimulus trajectory (up to 10 ms pre-spike time), and thirdly, they show spike patterns (precise doublet and triplet spiking). In order to deal with these complexities, we used a flexible probabilistic neuron model fitting mixtures of Gaussians to the spike triggered stimulus distributions, which quantitatively captured the contribution of the mentioned features and allowed us to achieve a full functional analysis of the total information rate indicated by the direct method. We found that instantaneous position, velocity, and acceleration explained about 50% of the total information rate. Adding a 10 ms pre-spike interval of stimulus trajectory achieved 80-90%. The final 10-20% were found to be due to non-linear coding by spike bursts.
    Frontiers in Neural Circuits 12/2013; DOI:10.3389/fncir.2013.00190 · 3.60 Impact Factor
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    • "The cell-by-cell differences in the latency–contrast relationship mean that signals will become increasingly asynchronous as the stimulus contrast is reduced, particularly in the higher visual areas such as STSa (van Rossum et al., 2008). While it has been shown that information unavailable from spike count is carried by response latency (Reich et al., 2001a,b), it is not immediately obvious how this information could be used by receiving neurons. The problem for the nervous system in determining response latency (time from stimulus onset to response onset) is undeniable. "
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    ABSTRACT: Neurones in visual cortex show increasing response latency with decreasing stimulus contrast. Neurophysiological recordings from neurones in inferior temporal cortex (IT) and the superior temporal sulcus (STS), show that the increment in response latency with decreasing stimulus contrast is considerably greater in higher visual areas than that seen in primary visual cortex. This suggests that the majority of the latency change is not retinal or V1 in origin, instead each cortical processing area adds latency at low contrast. I show that, as in earlier visual areas, response latency is more strongly dependent on stimulus contrast than stimulus identity. There is large variation in the extent to which response latency increases with decreasing stimulus contrast. I show that this between cell variability is, at least in part, related to the stimulus specificity of the neurones: the increase in response latency as stimulus contrast decreases is greater for neurones that respond to few stimuli compared to neurones that respond to many stimuli.
    Journal of Physiology-Paris 11/2009; 104(3-4):167-75. DOI:10.1016/j.jphysparis.2009.11.021 · 1.90 Impact Factor
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    • "They are motivated by information theory [14] and widely believed to estimate the mutual information (or mutual information rate) between stimulus and spike train response. They are frequently calculated using data from experiments where the stimulus and response are dynamic and time-varying [8] [12] [13] [11]. "
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    ABSTRACT: Information estimates such as the direct method of Strong, Koberle, de Ruyter van Steveninck, and Bialek (1998) sidestep the difficult problem of estimating the joint distribution of response and stimulus by instead estimating the difference between the marginal and conditional entropies of the response. While this is an effective estimation strategy, it tempts the practitioner to ignore the role of the stimulus and the meaning of mutual information. We show here that as the number of trials increases indefinitely, the direct (or plug-in) estimate of marginal entropy converges (with probability 1) to the entropy of the time-averaged conditional distribution of the response, and the direct estimate of the conditional entropy converges to the time-averaged entropy of the conditional distribution of the response. Under joint stationarity and ergodicity of the response and stimulus, the difference of these quantities converges to the mutual information. When the stimulus is deterministic or nonstationary the direct estimate of information no longer estimates mutual information, which is no longer meaningful, but it remains a measure of variability of the response distribution across time.
    Neural Computation 11/2008; 21(3):688-703. DOI:10.1162/neco.2008.01-08-700 · 2.21 Impact Factor
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