Dual Roles for Spike Signaling in Cortical Neural Populations

A prominent feature of signaling in cortical neurons is that of randomness in the action potential. The output of a typical pyramidal cell can be well fit with a Poisson model, and variations in the Poisson rate repeatedly have been shown to be correlated with stimuli. However while the rate provides a very useful characterization of neural spike data, it may not be the most fundamental description of the signaling code. Recent data showing γ frequency range multi-cell action potential correlations, together with spike timing dependent plasticity, are spurring a re-examination of the classical model, since precise timing codes imply that the generation of spikes is essentially deterministic. Could the observed Poisson randomness and timing determinism reflect two separate modes of communication, or do they somehow derive from a single process? We investigate in a timing-based model whether the apparent incompatibility between these probabilistic and deterministic observations may be resolved by examining how spikes could be used in the underlying neural circuits. The crucial component of this model draws on dual roles for spike signaling. In learning receptive fields from ensembles of inputs, spikes need to behave probabilistically, whereas for fast signaling of individual stimuli, the spikes need to behave deterministically. Our simulations show that this combination is possible if deterministic signals using γ latency coding are probabilistically routed through different members of a cortical cell population at different times. This model exhibits standard features characteristic of Poisson models such as orientation tuning and exponential interval histograms. In addition, it makes testable predictions that follow from the γ latency coding

The scalable mammalian brain: emergent distributions of glia and neurons

In this paper, we demonstrate that two characteristic properties of mammalian brains emerge when scaling-up modular, cortical structures. Firstly, the glia-to-neuron ratio is not constant across brains of different sizes: large mammalian brains have more glia per neuron than smaller brains. Our analyses suggest that if one assumes that glia number is proportional to wiring, a particular quantitative relationship emerges between brain size and glia-to-neuron ratio that fits the empirical data. Secondly, many authors have reported that the number of neurons underlying one mm2 of mammalian cortex is remarkably constant, across both areas and species. Here, we will show that such a constancy emerges when enlarging modular, cortical brain structures. Our analyses thus corroborate recent studies on the mammalian brain as a scalable architecture, providing a possible mechanism to explain some of the principles, constancies and rules that hold across brains of different size

Interactions between higher and lower visual areas improve shape selectivity of higher level neurons-explaining crowding phenomena

Recent theories of visual perception propose that feedforward cortical processing enables rapid and automatic object categorizations, yet incorporates a limited amount of detail. Subsequent feedback processing highlights high-resolution representations in early visual areas and provides spatial detail. To verify this hypothesis, we separate the contributions of feedforward and feedback signals to the selectivity of cortical neurons in a neural network simulation that is modeled after the hierarchical feedforward-feedback organization of cortical areas. We find that in such a network the responses of high-level neurons can initially distinguish between low-resolution aspects of objects but are 'blind' to differences in detail. After several feedback-feedforward cycles of processing, however, they can also distinguish between objects that differ in detail. Moreover, we find that our model captures recent paradoxical results of crowding phenomena, showing that spatial detail that is lost in visual crowding is nevertheless able to evoke specific adaptation effects. Our results thus provide an existence proof of the feasibility of novel theoretical models and provide a mechanism to explain various psychophysical and physiological results. Introduction Within 40 ms after the light of an image hits the retina, cells in the primary visual cortex (V1) start to fire. The very first spikes already express orientation and spatial frequency selectivity. The same applies to cells in extrastriate areas that, only 10 ms later, instantaneously code for color, motion, stereo depth, etc. Even the highest levels of selectivity, such as face versus nonface in inferotemporal cortex, appear to be already expressed some 80-120 ms after the image is presente

Predictive Feedback Can Account for Biphasic Responses in the Lateral Geniculate Nucleus

Janneke F. M. Jehee is with University of Rochester and Vanderbilt University, Dana H. Ballard is with University of Rochester and UT Austin.Biphasic neural response properties, where the optimal stimulus for driving a neural response changes from one stimulus pattern to the opposite stimulus pattern over short periods of time, have been described in several visual areas, including lateral geniculate nucleus (LGN), primary visual cortex (V1), and middle temporal area (MT). We describe a hierarchical model of predictive coding and simulations that capture these temporal variations in neuronal response properties. We focus on the LGN-V1 circuit and find that after training on natural images the model exhibits the brain's LGN-V1 connectivity structure, in which the structure of V1 receptive fields is linked to the spatial alignment and properties of center-surround cells in the LGN. In addition, the spatio-temporal response profile of LGN model neurons is biphasic in structure, resembling the biphasic response structure of neurons in cat LGN. Moreover, the model displays a specific pattern of influence of feedback, where LGN receptive fields that are aligned over a simple cell receptive field zone of the same polarity decrease their responses while neurons of opposite polarity increase their responses with feedback. This phase-reversed pattern of influence was recently observed in neurophysiology. These results corroborate the idea that predictive feedback is a general coding strategy in the brain.This work was supported by National Institutes of Health research grants EY05729 and R01 RR009283 and by a Rubicon grant from the Netherlands Organization for Scientific Research (NWO). The funding agencies did not have any role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Computer Science

Motion direction is represented as a bimodal probability distribution in the human visual cortex

Abstract Humans infer motion direction from noisy sensory signals. We hypothesize that to make these inferences more precise, the visual system computes motion direction not only from velocity but also spatial orientation signals – a ‘streak’ created by moving objects. We implement this hypothesis in a Bayesian model, which quantifies knowledge with probability distributions, and test its predictions using psychophysics and fMRI. Using a probabilistic pattern-based analysis, we decode probability distributions of motion direction from trial-by-trial activity in the human visual cortex. Corroborating the predictions, the decoded distributions have a bimodal shape, with peaks that predict the direction and magnitude of behavioral errors. Interestingly, we observe similar bimodality in the distribution of the observers’ behavioral responses across trials. Together, these results suggest that observers use spatial orientation signals when estimating motion direction. More broadly, our findings indicate that the cortical representation of low-level visual features, such as motion direction, can reflect a combination of several qualitatively distinct signals

Connection weights after training.

<p>The figure depicts learned connection weights from 64 LGN off-center type units and from 64 LGN on-center type units to one representative V1 unit. Red: connection weights from on-center type cells, blue: connection weights from off-center type cells. Brighter values indicate higher connection weights. The value zero is represented by the color black. The on- and off-center units are spatially aligned with the on- and off-zones of the model V1 receptive field.</p

Predictive feedback using the matching pursuit algorithm.

<p>(A) Model receptive fields (RFs) are represented as basis vectors. When the input (blue vector) arrives in model V1, a basis vector that has high overlap with the input is selected (red vector ). The V1 basis vector weighted by its response is then subtracted from the input and the selection-subtraction process is repeated on the residual LGN representation (green vector). (B,C) Model V1 prediction and residual LGN representation over time. (B) The blue vector represents the actual input, its prediction is represented by the red dot. (C) Black depicts off-regions, white depicts on-regions. A prediction is obtained by summing the selected V1 basis vectors weighted by their response. LGN difference detectors represent the error between V1 prediction and actual input. (B,C) Subsequent feedforward-feedback cycles refine the higher-level prediction of the input. Without predictive feedback, the model would represent just the initial, less accurate prediction.</p

Effects of feedback on LGN on-center and off-center type cells.

<p>Dashed: probability that the LGN cell coding for this location is active (i.e. response>0) in the first feedforward sweep of the model when a V1 region will subsequently be selected that codes for the same or opposite polarity, blank: probability that the LGN cell is active after the first feedforward-feedback pass (i.e. when feedback exerts its effect) when a V1 region is selected that codes for the same or opposite polarity. Red: on-center type cell, blue: off-center type cell. The results were obtained after presenting the model with a white-noise stimulus every 100 milliseconds for a total of 10,000 images. Comparing initial feedforward activity with subsequent LGN activity shows that feedback has a negative influence on cells of similar sign, and a positive influence on cells of opposite sign. Thus, the probability that an LGN off-cell is active increases after feedback from a V1 on-region (lower right, blue), and the probability that an LGN on-type cell is active decreases after feedback from an on-region in V1 (upper left, red).</p

Spatio-temporal response of LGN on-center type cell.

<p>The response was mapped with the reverse correlation algorithm, using either non-biphasic retinal inputs (A–C,E) or biphasic retinal inputs (D). (A) Spatio-temporal response of an on-center type cell in model LGN. Responses were obtained by cross-correlating stimulus and response at the time intervals given below the figures. Red: response to bright stimulus at that location, blue: response to dark stimulus at that location. Note the change in sign after 50 milliseconds. Similar results were obtained for other LGN on-center type units. (B) Spatio-temporal response profile of on-center type cells in cat LGN obtained with the reverse correlation algorithm <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000373#pcbi.1000373-Alonso1" target="_blank">[1]</a>. (C) The removal of feedback in the model causes the previously biphasic responses to disappear. (D) Temporal response profile of on-center type cell in a model with biphasic retinal inputs. Model activity is normalized by the initial response magnitudes. The biphasic response in LGN is more pronounced in the presence of predictive feedback compared to a situation in which the LGN response is fully determined by biphasic retinal input (for comparison, see e.g. <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1000373#pcbi.1000373-Usrey1" target="_blank">[21]</a>). (E) Average model LGN and V1 representations after reference stimuli consisting of bright stimulus regions have been presented. Black depicts off regions, white depicts on regions. When V1 predictions of the bright reference stimulus arrive in model LGN, they are compared against a new and unexpected stimulus representation. The difference between the predicted bright region and the second stimulus is negative, exciting LGN off-center type cells.</p