Implications of Neuronal Diversity on Population Coding

2006 ◽  
Vol 18 (8) ◽  
pp. 1951-1986 ◽  
Author(s):  
Maoz Shamir ◽  
Haim Sompolinsky
Keyword(s):  
Weighted Average ◽  
Population Coding ◽  
Information Capacity ◽  
Correlated Noise ◽  
Neuronal Diversity ◽  
Population Vector ◽  
Homogeneous Population ◽  
Optimal Linear ◽  
Stimulus Features ◽  
Experimental Findings

In many cortical and subcortical areas, neurons are known to modulate their average firing rate in response to certain external stimulus features. It is widely believed that information about the stimulus features is coded by a weighted average of the neural responses. Recent theoretical studies have shown that the information capacity of such a coding scheme is very limited in the presence of the experimentally observed pairwise correlations. However, central to the analysis of these studies was the assumption of a homogeneous population of neurons. Experimental findings show a considerable measure of heterogeneity in the response properties of different neurons. In this study, we investigate the effect of neuronal heterogeneity on the information capacity of a correlated population of neurons. We show that information capacity of a heterogeneous network is not limited by the correlated noise, but scales linearly with the number of cells in the population. This information cannot be extracted by the population vector readout, whose accuracy is greatly suppressed by the correlated noise. On the other hand, we show that an optimal linear readout that takes into account the neuronal heterogeneity can extract most of this information. We study analytically the nature of the dependence of the optimal linear readout weights on the neuronal diversity. We show that simple online learning can generate readout weights with the appropriate dependence on the neuronal diversity, thereby yielding efficient readout.

Direct and Indirect Cooperation between Temporal and Parietal Networks for Invariant Visual Recognition

1992 ◽  
Vol 4 (1) ◽  
pp. 35-57 ◽  
Author(s):  
Isabelle Otto ◽  
Philippe Grandguillaume ◽  
Latifa Boutkhil ◽  
Yves Burnod ◽  
Emmanuel GuigonBurnod
Keyword(s):  
Receptive Field ◽  
Visual System ◽  
Visual Recognition ◽  
Single Cells ◽  
Central Zone ◽  
Object Identification ◽  
Population Coding ◽  
Significant Shift ◽  
Learning Session ◽  
Population Vector

A new type of biologically inspired multilayered network is proposed to model the properties of the primate visual system with respect to invariant visual recognition (IVR). This model is based on 10 major neurobiological and psychological constraints. The first five constraints shape the architecture and properties of the network. 1. The network model has a Y-like double-branched multilayered architecture, with one input (the retina) and two parallel outputs, the “What” and the “Where,” which model, respectively, the temporal pathway, specialized for “object” identification, and the parietal pathway specialized for “spatial” localization. 2. Four processing layers are sufficient to model the main functional steps of primate visual system that transform the retinal information into prototypes (object-centered reference frame) in the “What” branch and into an oculomotor command in the “Where” branch. 3. The distribution of receptive field sizes within and between the two functional pathways provides an appropriate tradeoff between discrimination and invariant recognition capabilities. 4. The two outputs are represented by a population coding: the ocular command is computed as a population vector in the “Where” branch and the prototypes are coded in a “semidistributed” way in the “What” branch. In the intermediate associative steps, processing units learn to associate prototypes (through feedback connections) to component features (through feedforward ones). 5. The basic processing units of the network do not model single cells but model the local neuronal circuits that combine different information flows organized in separate cortical layers. Such a biologically constrained model shows shift-invariant and size-invariant capabilities that resemble those of humans (psychological constraints): 6. During the Learning session, a set of patterns (26 capital letters and 2 geometric figures) are presented to the network: a single presentation of each pattern in one position (at the center) and with one size is sufficient to learn the corresponding prototypes (internal representations). These patterns are thus presented in widely varying new sizes and positions during the Recognition session: 7. The “What” branch of the network succeeds in immediate recognition for patterns presented in the central zone of the retina with the learned size. 8. The recognition by the “What” branch is resistant to changes in size within a limited range of variation related to the distribution of receptive field (RF) sizes in the successive processing steps of this pathway. 9. Even when ocular movements are not allowed, the recognition capabilities of the “What” branch are unaffected by changing positions around the learned one. This significant shift-invariance of the “What” branch is also related to the distribution of RF sizes. 10. When varying both sizes and locations, the “What” and the “Where” branches cooperate for recognition: the location coding in the “Where” branch can command, under the control of the “What” branch, an ocular movement efficient to reset peripheral patterns toward the central zone of the retina until successful recognition. This model results in predictions about anatomical connections and physiological interactions between temporal and parietal cortices.

Decorrelation of Spiking Variability and Improved Information Transfer Through Feedforward Divisive Normalization

2012 ◽  
Vol 24 (4) ◽  
pp. 867-894 ◽  
Author(s):  
Bryan P. Tripp
Keyword(s):  
Visual Cortex ◽  
Information Transfer ◽  
Response Variability ◽  
Signal Integration ◽  
Correlated Noise ◽  
Stimulus Parameter ◽  
Divisive Normalization ◽  
Optimal Linear ◽  
Large Groups ◽  
Simple Average

Response variability is often positively correlated in pairs of similarly tuned neurons in the visual cortex. Many authors have considered correlated variability to prevent postsynaptic neurons from averaging across large groups of inputs to obtain reliable stimulus estimates. However, a simple average of variability ignores nonlinearities in cortical signal integration. This study shows that feedforward divisive normalization of a neuron's inputs effectively decorrelates their variability. Furthermore, we show that optimal linear estimates of a stimulus parameter that are based on normalized inputs are more accurate than those based on nonnormalized inputs, due partly to reduced correlations, and that these estimates improve with increasing population size up to several thousand neurons. This suggests that neurons may possess a simple mechanism for substantially decorrelating noise in their inputs. Further work is needed to reconcile this conclusion with past evidence that correlated noise impairs visual perception.

scholarly journals Optimal Tuning Widths in Population Coding of Periodic Variables

2006 ◽  
Vol 18 (7) ◽  
pp. 1555-1576 ◽  
Author(s):  
Marcelo A. Montemurro ◽  
Stefano Panzeri
Keyword(s):  
Cortical Neurons ◽  
Neuronal Population ◽  
Population Coding ◽  
Model Parameters ◽  
Population Activity ◽  
Sensory Stimuli ◽  
Tuning Curves ◽  
Periodic Stimulus ◽  
Tuning Width ◽  
Stimulus Features

We study the relationship between the accuracy of a large neuronal population in encoding periodic sensory stimuli and the width of the tuning curves of individual neurons in the population. By using general simple models of population activity, we show that when considering one or two periodic stimulus features, a narrow tuning width provides better population encoding accuracy. When encoding more than two periodic stimulus features, the information conveyed by the population is instead maximal for finite values of the tuning width. These optimal values are only weakly dependent on model parameters and are similar to the width of tuning to orientation ormotion direction of real visual cortical neurons. A very large tuning width leads to poor encoding accuracy, whatever the number of stimulus features encoded. Thus, optimal coding of periodic stimuli is different from that of nonperiodic stimuli, which, as shown in previous studies, would require infinitely large tuning widths when coding more than two stimulus features.

scholarly journals Neuronal correlations in MT and MST impair population decoding of opposite directions of random dot motion

2018 ◽  
Author(s):  
Tristan A. Chaplin ◽  
Maureen A. Hagan ◽  
Benjamin J. Allitt ◽  
Leo L. Lui
Keyword(s):  
Population Coding ◽  
Correlation Structure ◽  
Single Neurons ◽  
Electrode Arrays ◽  
Preferred Direction ◽  
Dot Motion ◽  
Random Dot Patterns ◽  
Stimulus Features ◽  
Random Dot Motion ◽  
Spiking Activity

AbstractThe study of neuronal responses to random-dot motion patterns has provided some of the most valuable insights into how the activity of neurons is related to perception. In the opposite directions of motion paradigm, the motion signal strength is decreased by manipulating the coherence of random dot patterns to examine how well the activity of single neurons represents the direction of motion. To extend this paradigm to populations of neurons, studies have used modelling based on data from pairs of neurons, but several important questions require further investigation with larger neuronal datasets. We recorded neuronal populations in the middle temporal (MT) and medial superior temporal (MST) areas of anaesthetized marmosets with electrode arrays, while varying the coherence of random dot patterns in two opposite directions of motion (left and right). Using the spike rates of simultaneously recorded neurons, we decoded the direction of motion at each level of coherence with linear classifiers. We found that the presence of correlations had a detrimental effect to decoding performance, but that learning the correlation structure produced better decoding performance compared to decoders that ignored the correlation structure. We also found that reducing motion coherence increased neuronal correlations, but decoders did not need to be optimized for each coherence level. Finally, we showed that decoder weights depend of left-right selectivity at 100% coherence, rather than the preferred direction. These results have implications for understanding how the information encoded by populations of neurons is affected by correlations in spiking activity.Significance StatementMany studies have examined how the spiking activity of single neurons can encode stimulus features, such the direction of motion of visual stimuli. However, majority of such studies to date have only recorded from a small number of neurons at the same time, meaning that one cannot adequately account for the trial-to-trial correlations in spiking activity between neurons. Using multi-channel recordings, we were able to measure the neuronal correlations, and their effects on population coding of stimulus features. Our results have implications on the way which neural populations must be readout in order to maximize information.

A Neural Network for Coding of Trajectories by Time Series of Neuronal Population Vectors

1994 ◽  
Vol 6 (1) ◽  
pp. 19-28 ◽  
Author(s):  
Alexander V. Lukashin ◽  
Apostolos P. Georgopoulos
Keyword(s):  
Neural Network ◽  
Motor Cortex ◽  
Simulated Annealing Algorithm ◽  
Neuronal Population ◽  
Population Vector ◽  
Feedback Neural Network ◽  
Neuronal Pair ◽  
The Mean ◽  
Annealing Algorithm ◽  
Experimental Findings

The neuronal population vector is a measure of the combined directional tendency of the ensemble of directionally tuned cells in the motor cortex. It has been found experimentally that a trajectory of limb movement can be predicted by adding together population vectors, tip-to-tail, calculated for successive instants of time to construct a neural trajectory. In the present paper we consider a model of the dynamic evolution of the population vector. The simulated annealing algorithm was used to adjust the connection strengths of a feedback neural network so that it would generate a given trajectory by a sequence of population vectors. This was repeated for different trajectories. Resulting sets of connection strengths reveal a common feature regardless of the type of trajectories generated by the network: namely, the mean connection strength was negatively correlated with the angle between the preferred directions of neuronal pair involved in the connection. The results are discussed in the light of recent experimental findings concerning neuronal connectivity within the motor cortex.

Erratum: Population coding in neuronal systems with correlated noise [Phys. Rev. E64, 051904 (2001)]

2002 ◽  
Vol 65 (4) ◽  
Author(s):  
Haim Sompolinsky ◽  
Hyoungsoo Yoon ◽  
Kukjin Kang ◽  
Maoz Shamir
Keyword(s):  
Population Coding ◽  
Correlated Noise ◽  
Neuronal Systems

scholarly journals Statistically Efficient Estimation Using Population Coding

1998 ◽  
Vol 10 (2) ◽  
pp. 373-401 ◽  
Author(s):  
Alexandre Pouget ◽  
Kechen Zhang ◽  
Sophie Deneve ◽  
Peter E. Latham
Keyword(s):  
Maximum Likelihood ◽  
Recurrent Network ◽  
Population Coding ◽  
Efficient Estimation ◽  
Population Code ◽  
Population Vector ◽  
Uncorrelated Noise ◽  
Vector Estimate ◽  
The Brain ◽  
Code Format

Coarse codes are widely used throughout the brain to encode sensory and motor variables. Methods designed to interpret these codes, such as population vector analysis, are either inefficient (the variance of the estimate is much larger than the smallest possible variance) or biologically implausible, like maximum likelihood. Moreover, these methods attempt to compute a scalar or vector estimate of the encoded variable. Neurons are faced with a similar estimation problem. They must read out the responses of the presynaptic neurons, but, by contrast, they typically encode the variable with a further population code rather than as a scalar. We show how a nonlinear recurrent network can be used to perform estimation in a near-optimal way while keeping the estimate in a coarse code format. This work suggests that lateral connections in the cortex may be involved in cleaning up uncorrelated noise among neurons representing similar variables.

Theoretical Considerations for the Analysis of Population Coding in Motor Cortex

1994 ◽  
Vol 6 (1) ◽  
pp. 29-37 ◽  
Author(s):  
Terence D. Sanger
Keyword(s):  
Motor Cortex ◽  
Hand Movement ◽  
Personal Space ◽  
Population Coding ◽  
Movement Direction ◽  
Population Vector ◽  
Weak Support ◽  
Rectangular Coordinates ◽  
Cartesian Coordinate ◽  
Multiple Cells

Recent evidence of population coding in motor cortex has led some researchers to claim that certain variables such as hand direction or force may be coded within a Cartesian coordinate system with respect to extra personal space. These claims are based on the ability to predict the rectangular coordinates of hand movement direction using a “population vector” computed from multiple cells' firing rates. I show here that such a population vector can always be found given a very general set of assumptions. Therefore the existence of a population vector constitutes only weak support for the explicit use of a particular coordinate representation by motor cortex.

Population coding in neuronal systems with correlated noise

2001 ◽  
Vol 64 (5) ◽  
Author(s):  
Haim Sompolinsky ◽  
Hyoungsoo Yoon ◽  
Kukjin Kang ◽  
Maoz Shamir
Keyword(s):  
Population Coding ◽  
Correlated Noise ◽  
Neuronal Systems
Sign in / Sign up

Export Citation Format

Share Document