scholarly journals Weighting neurons by selectivity produces near-optimal population codes

2019 ◽  
Vol 121 (5) ◽  
pp. 1924-1937
Author(s):  
Elizabeth Zavitz ◽  
Nicholas S. C. Price
Keyword(s):  
A Priori ◽  
Maximum Level ◽  
Optimization Methods ◽  
Neuronal Population ◽  
Direction Selectivity ◽  
Motion Direction ◽  
Temporal Area ◽  
Linear Discriminant ◽  
Preferred Direction

Perception is produced by “reading out” the representation of a sensory stimulus contained in the activity of a population of neurons. To examine experimentally how populations code information, a common approach is to decode a linearly weighted sum of the neurons’ spike counts. This approach is popular because of the biological plausibility of weighted, nonlinear integration. For neurons recorded in vivo, weights are highly variable when derived through optimization methods, but it is unclear how the variability affects decoding performance in practice. To address this, we recorded from neurons in the middle temporal area (MT) of anesthetized marmosets ( Callithrix jacchus) viewing stimuli comprising a sheet of dots that moved coherently in 1 of 12 different directions. We found that high peak response and direction selectivity both predicted that a neuron would be weighted more highly in an optimized decoding model. Although learned weights differed markedly from weights chosen according to a priori rules based on a neuron’s tuning profile, decoding performance was only marginally better for the learned weights. In the models with a priori rules, selectivity is the best predictor of weighting, and defining weights according to a neuron’s preferred direction and selectivity improves decoding performance to very near the maximum level possible, as defined by the learned weights. NEW & NOTEWORTHY We examined which aspects of a neuron’s tuning account for its contribution to sensory coding. Strongly direction-selective neurons are weighted most highly by optimal decoders trained to discriminate motion direction. Models with predefined decoding weights demonstrate that this weighting scheme causally improved direction representation by a neuronal population. Optimizing decoders (using a generalized linear model or Fisher’s linear discriminant) led to only marginally better performance than decoders based purely on a neuron’s preferred direction and selectivity.

scholarly journals The Most Informative Neural Code Accounts For Population Heterogeneity

2017 ◽  
Author(s):  
Elizabeth Zavitz ◽  
Nicholas SC Price
Keyword(s):  
Machine Learning ◽  
Maximum Level ◽  
Direction Selectivity ◽  
Machine Learning Algorithms ◽  
Population Heterogeneity ◽  
Motion Direction ◽  
Temporal Area ◽  
Firing Rates ◽  
Preferred Direction

AbstractPerception is produced by ‘reading out’ the representation of a sensory stimulus contained in the firing rates of a population of neurons. To examine experimentally how populations code information, a common approach is to decode a linearly-weighted sum of the neurons’ firing rates. This approach is popular because of its biological validity: weights in a computational decoder are analogous to synaptic strengths. For neurons recorded in vivo, weights are highly variable when derived through machine learning methods, but it is unclear what neuronal properties explain this variability, and how the variability affects decoding performance. To address this, we recorded from neurons in the middle temporal area (MT) of anesthetized marmosets (Callithrix jacchus) viewing stimuli comprising a sheet of dots that moved coherently in one of twelve different directions. We found that high gain and direction selectivity both predicted that a neuron would be weighted more highly in an optimised decoding model. Although learned weights differed markedly from weights chosen according to a priori rules based on a neuron’s tuning profile, decoding performance was only marginally better for the learned weights. In the models with a priori rules, selectivity is the best predictor of weighting, and defining weights according to a neuron’s preferred direction and selectivity improves decoding performance to very near the maximum level possible, as defined by the learned weights.New & NoteworthyWe examined which aspects of a neuron’s tuning account for its contribution to sensory coding. Strongly direction-selective neurons were weighted most highly by machine learning algorithms trained to discriminate motion direction. Models with a priori defined decoding weights demonstrate that the learned weighting scheme causally improved direction representation by a neuronal population. Optimising decoders (using machine learning) lead to only marginally better performance than decoders based purely on a neuron’s preferred direction and selectivity.

scholarly journals Comparison of the Spatial Limits on Direction Selectivity in Visual Areas MT and V1

2005 ◽  
Vol 93 (3) ◽  
pp. 1235-1245 ◽  
Author(s):  
Mark M. Churchland ◽  
Nicholas J. Priebe ◽  
Stephen G. Lisberger
Keyword(s):  
Apparent Motion ◽  
Great Majority ◽  
Spatial Separation ◽  
Direction Selectivity ◽  
Temporal Separation ◽  
Temporal Area ◽  
Mean Values ◽  
V1 Neurons ◽  
Preferred Direction ◽  
The Difference

We recorded responses to apparent motion from directionally selective neurons in primary visual cortex (V1) of anesthetized monkeys and middle temporal area (MT) of awake monkeys. Apparent motion consisted of multiple stationary stimulus flashes presented in sequence, characterized by their temporal separation (Δ t) and spatial separation (Δ x). Stimuli were 8° square patterns of 100% correlated random dots that moved at apparent speeds of 16 or 32°/s. For both V1 and MT, the difference between the response to the preferred and null directions declined with increasing flash separation. For each neuron, we estimated the maximum flash separation for which directionally selective responses were observed. For the range of speeds we used, Δ x provided a better description of the limitation on directional responses than did Δ t. When comparing MT and V1 neurons of similar preferred speed, there was no difference in the maximum Δ x between our samples from the two areas. In both V1 and MT, the great majority of neurons had maximal values of Δ x in the 0.25–1° range. Mean values were almost identical between the two areas. For most neurons, larger flash separations led to both weaker responses to the preferred direction and increased responses to the opposite direction. The former mechanism was slightly more dominant in MT and the latter slightly more dominant in V1. We conclude that V1 and MT neurons lose direction selectivity for similar values of Δ x, supporting the hypothesis that basic direction selectivity in MT is inherited from V1, at least over the range of stimulus speeds represented by both areas.

Neural responses to visual texture patterns in middle temporal area of the macaque monkey

1992 ◽  
Vol 68 (1) ◽  
pp. 164-181 ◽  
Author(s):  
J. F. Olavarria ◽  
E. A. DeYoe ◽  
J. J. Knierim ◽  
J. M. Fox ◽  
D. C. van Essen
Keyword(s):  
Macaque Monkey ◽  
Direction Selectivity ◽  
Neural Responses ◽  
Temporal Area ◽  
Middle Temporal ◽  
Tuning Curves ◽  
Preferred Direction ◽  
Orientation Contrast ◽  
Background Texture ◽  
Moving Texture

1. We studied how neurons in the middle temporal visual area (MT) of anesthetized macaque monkeys responded to textured and nontextured visual stimuli. Stimuli contained a central rectangular ,figure- that was either uniform in luminance or consisted of an array of oriented line segments. The figure moved at constant velocity in one of four orthogonal directions. The region surrounding the figure was either uniform in luminance or contained a texture array (whose elements were identical or orthogonal in orientation to those of the figure), and it either was stationary or moved along with the figure. 2. A textured figure moving across a stationary textured background (,texture bar- stimulus) often elicited vigorous neural responses, but, on average, the responses to texture bars were significantly smaller than to solid (uniform luminance) bars. 3. Many cells showed direction selectivity that was similar for both texture bars and solid bars. However, on average, the direction selectivity measured when texture bars were used was significantly smaller than that for solid bars, and many cells lost significant direction selectivity altogether. The reduction in direction selectivity for texture bars generally reflected a combination of decreased responsiveness in the preferred direction and increased responsiveness in the null (opposite to preferred) direction. 4. Responses to a texture bar in the absence of a texture background (,texture bar alone-) were very similar to the responses to solid bars both in the magnitude of response and in the degree of direction selectivity. Conversely, adding a static texture surround to a moving solid bar reduced direction selectivity on average without a reduction in response magnitude. These results indicate that the static surround is largely responsible for the differences in direction selectivity for texture bars versus solid bars. 5. In the majority of MT cells studied, responses to a moving texture bar were largely independent of whether the elements in the bar were of the same orientation as the background elements or of the orthogonal orientation. Thus, for the class of stimuli we used, orientation contrast does not markedly affect the responses of MT neurons to moving texture patterns. 6. The optimum figure length and the shapes of the length tuning curves determined with the use of solid bars and texture bars differed significantly in most of the cells examined. Thus neurons in MT are not simply selective for a particular figure shape independent of whatever cues are used to delineate the figure.

scholarly journals Dendritic Calcium Accumulation Associated With Direction-Selective Adaptation in Visual Motion-Sensitive Neurons In Vivo

2000 ◽  
Vol 84 (4) ◽  
pp. 1914-1923 ◽  
Author(s):  
Rafael Kurtz ◽  
Volker Dürr ◽  
Martin Egelhaaf
Keyword(s):  
Visual Motion ◽  
Selective Adaptation ◽  
Direction Selectivity ◽  
Motion Adaptation ◽  
Stimulus Velocity ◽  
Calcium Accumulation ◽  
Calcium Dependent ◽  
Preferred Direction ◽  
Cell Class

Motion adaptation in directionally selective tangential cells (TC) of the fly visual system has previously been explained as a presynaptic mechanism. Based on the observation that adaptation is in part direction selective, which is not accounted for by the former models of motion adaptation, we investigated whether physiological changes located in the TC dendrite can contribute to motion adaptation. Visual motion in the neuron's preferred direction (PD) induced stronger adaptation than motion in the opposite direction and was followed by an afterhyperpolarization (AHP). The AHP subsides in the same time as adaptation recovers. By combining in vivo calcium fluorescence imaging with intracellular recording, we show that dendritic calcium accumulation following motion in the PD is correlated with the AHP. These results are consistent with a calcium-dependent physiological change in TCs underlying adaptation during continuous stimulation with PD motion, expressing itself as an AHP after the stimulus stops. However, direction selectivity of adaptation is probably not solely related to a calcium-dependent mechanism because direction-selective effects can also be observed for fast moving stimuli, which do not induce sizeable calcium accumulation. In addition, a comparison of two classes of TCs revealed differences in the relationship of calcium accumulation and AHP when the stimulus velocity was varied. Thus the potential role of calcium in motion adaptation depends on stimulation parameters and cell class.

Computation of motion direction in the vertebrate retina

e-Neuroforum ◽  
2012 ◽  
Vol 18 (3) ◽  
Author(s):  
T. Euler ◽  
S.E. Hausselt
Keyword(s):  
Large Scale ◽  
Petri Dish ◽  
Cell Types ◽  
Direction Selectivity ◽  
Dendritic Morphology ◽  
Image Motion ◽  
Target Area ◽  
Motion Direction ◽  
Preferred Direction ◽  
The Brain

AbstractHow direction of image motion is detected as early as at the level of the vertebrate eye has been intensively studied in retina research. Although the first direction-selective (DS) ret­inal ganglion cells were already described in the 1960s and have since then been in the fo­cus of many studies, scientists are still puz­zled by the intricacy of the neuronal circuits and computational mechanisms underlying retinal direction selectivity. The fact that the retina can be easily isolated and studied in a Petri dish-by presenting light stimuli while recording from the various cell types in the retinal circuits-in combination with the ex­tensive anatomical, molecular and physiolog­ical knowledge about this part of the brain presents a unique opportunity for studying this intriguing visual circuit in detail. This ar­ticle provides a brief overview of the histo­ry of research on retinal direction selectivi­ty, but then focuses on the past decade and the progress achieved, in particular driven by methodological advances in optical record­ing techniques, molecular genetics approach­es and large-scale ultrastructural reconstruc­tions. As it turns out, retinal direction selec­tivity is a complex, multi-tiered computation, involving dendrite-intrinsic mechanisms as well as several types of network interactions on the basis of highly selective, likely genet­ically predetermined synaptic connectivi­ty. Moreover, DS ganglion cell types appear to be more diverse than previously thought, differing not only in their preferred direction and response polarity, but also in physiology, DS mechanism, dendritic morphology and, importantly, the target area of their projec­tions in the brain.

scholarly journals Mechanism of Motion Direction Detection Based on Barlow’s Retina Inhibitory Scheme in Direction-Selective Ganglion Cells

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1663
Author(s):  
Mianzhe Han ◽  
Yuki Todo ◽  
Zheng Tang
Keyword(s):  
Ganglion Cells ◽  
Direction Selectivity ◽  
Detection Accuracy ◽  
Local Motion ◽  
Motion Direction ◽  
Preferred Direction ◽  
Directional Detection ◽  
Direction Detection ◽  
Series Of Experiments ◽  
Better Than

Previous studies have reported that directionally selective ganglion cells respond strongly in their preferred direction, but are only weakly excited by stimuli moving in the opposite null direction. Various studies have attempted to elucidate the mechanisms underlying direction selectivity with cellular basis. However, these studies have not elucidated the mechanism underlying motion direction detection. In this study, we propose the mechanism based on Barlow’s inhibitory scheme for motion direction detection. We described the local motion-sensing direction-selective neurons. Next, this model was used to construct the two-dimensional multi-directional detection neurons which detect the local motion directions. The information of local motion directions was finally used to infer the global motion direction. To verify the validity of the proposed mechanism, we conducted a series of experiments involving a dataset with a number of images. The proposed mechanism exhibited good performance in all experiments with high detection accuracy. Furthermore, we compare the performance of our proposed system and traditional Convolution Neural Network (CNN) on motion direction prediction. It is found that the performance of our system is much better than that of CNN in terms of accuracy, calculation speed and cost.

MST Neurons Respond to Optic Flow and Translational Movement

1998 ◽  
Vol 80 (4) ◽  
pp. 1816-1827 ◽  
Author(s):  
Charles J. Duffy
Keyword(s):  
Optic Flow ◽  
Direction Selectivity ◽  
Movement Detection ◽  
Temporal Area ◽  
Medial Superior Temporal ◽  
Preferred Direction ◽  
Translational Movement ◽  
Mst Neurons ◽  
Best Responses ◽  
Stimulus Direction

Duffy, Charles J. MST neurons respond to optic flow and translational movement . J. Neurophysiol. 80: 1816–1827, 1998. We recorded the responses of 189 medial superior temporal area (MST) neurons by using optic flow, real translational movement, and combined stimuli in which matching directions of optic flow and real translational movement were presented together. One-half of the neurons (48%) showed strong responses to optic flow simulating self-movement in the horizontal plane, and 24% showed strong responses to translational movement. Combining optic flow stimuli with matching directions of translational movement caused substantial changes in both the amplitude of the best responses (44% of neurons) and the strength of direction selectivity (71% of neurons), with little effect on which stimulus direction was preferred. However, combining optic flow and translational movement such that opposite directions were presented together changed the preferred direction in 45% of the neurons with substantial changes in the strength of direction selectivity. These studies suggest that MST neurons combine visual and vestibular signals to enhance self-movement detection and disambiguate optic flow that results from either self-movement or the movement of large objects near the observer.

Temporal Dynamics of Direction Tuning in Motion-Sensitive Macaque Area MT

2005 ◽  
Vol 93 (4) ◽  
pp. 2104-2116 ◽  
Author(s):  
János A. Perge ◽  
Bart G. Borghuis ◽  
Roger J. E. Bours ◽  
Martin J. M. Lankheet ◽  
Richard J. A. van Wezel
Keyword(s):  
Temporal Dynamics ◽  
Random Sequence ◽  
Nonlinear Effects ◽  
Direction Selectivity ◽  
Second Phase ◽  
Motion Direction ◽  
Area Mt ◽  
Mt Neurons ◽  
Static Nonlinearity ◽  
Preferred Direction

We studied the temporal dynamics of motion direction sensitivity in macaque area MT using a motion reverse correlation paradigm. Stimuli consisted of a random sequence of motion steps in eight different directions. Cross-correlating the stimulus with the resulting neural activity reveals the temporal dynamics of direction selectivity. The temporal dynamics of direction selectivity at the preferred speed showed two phases along the time axis: one phase corresponding to an increase in probability for the preferred direction at short latencies and a second phase corresponding to a decrease in probability for the preferred direction at longer latencies. The strength of this biphasic behavior varied between neurons from weak to very strong and was uniformly distributed. Strong biphasic behavior suggests optimal responses for motion steps in the antipreferred direction followed by a motion step in the preferred direction. Correlating spikes to combinations of motion directions corroborates this distinction. The optimal combination for weakly biphasic cells consists of successive steps in the preferred direction, whereas for strongly biphasic cells, it is a reversal of directions. Comparing reverse correlograms to combinations of stimuli to predictions based on correlograms for individual directions revealed several nonlinear effects. Correlations for successive presentations of preferred directions were smaller than predicted, which could be explained by a static nonlinearity (saturation). Correlations to pairs of (nearly) opposite directions were larger than predicted. These results show that MT neurons are generally more responsive when sudden changes in motion directions occur, irrespective of the preferred direction of the neurons. The latter nonlinearities cannot be explained by a simple static nonlinearity at the output of the neuron, but most likely reflect network interactions.

scholarly journals A deep convolutional visual encoding model of neuronal responses in the LGN

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Eslam Mounier ◽  
Bassem Abdullah ◽  
Hani Mahdi ◽  
Seif Eldawlatly
Keyword(s):  
Firing Rate ◽  
Visual Information ◽  
Visual Stimulation ◽  
Neuronal Population ◽  
Neuronal Firing ◽  
Electrode Arrays ◽  
Deep Convolutional Neural Networks ◽  
Firing Rates ◽  
Spatiotemporal Representation

AbstractThe Lateral Geniculate Nucleus (LGN) represents one of the major processing sites along the visual pathway. Despite its crucial role in processing visual information and its utility as one target for recently developed visual prostheses, it is much less studied compared to the retina and the visual cortex. In this paper, we introduce a deep learning encoder to predict LGN neuronal firing in response to different visual stimulation patterns. The encoder comprises a deep Convolutional Neural Network (CNN) that incorporates visual stimulus spatiotemporal representation in addition to LGN neuronal firing history to predict the response of LGN neurons. Extracellular activity was recorded in vivo using multi-electrode arrays from single units in the LGN in 12 anesthetized rats with a total neuronal population of 150 units. Neural activity was recorded in response to single-pixel, checkerboard and geometrical shapes visual stimulation patterns. Extracted firing rates and the corresponding stimulation patterns were used to train the model. The performance of the model was assessed using different testing data sets and different firing rate windows. An overall mean correlation coefficient between the actual and the predicted firing rates of 0.57 and 0.7 was achieved for the 10 ms and the 50 ms firing rate windows, respectively. Results demonstrate that the model is robust to variability in the spatiotemporal properties of the recorded neurons outperforming other examined models including the state-of-the-art Generalized Linear Model (GLM). The results indicate the potential of deep convolutional neural networks as viable models of LGN firing.

scholarly journals Aerosol-based antimicrobial photoinactivation in the lungs: an action spectrum study

2021 ◽  
Author(s):  
Chiara Treghini ◽  
Alfonso Dell’Accio ◽  
Franco Fusi ◽  
Giovanni Romano
Keyword(s):  
Light Source ◽  
A Priori ◽  
Action Spectrum ◽  
Multidrug Resistant ◽  
Theoretical Modelling ◽  
Resistant Bacteria ◽  
Lung Infections ◽  
Killing Action ◽  
Definition Of

AbstractChronic lung infections are among the most diffused human infections, being often associated with multidrug-resistant bacteria. In this framework, the European project “Light4Lungs” aims at synthesizing and testing an inhalable light source to control lung infections by antimicrobial photoinactivation (aPDI), addressing endogenous photosensitizers only (porphyrins) in the representative case of S. aureus and P. aeruginosa. In the search for the best emission characteristics for the aerosolized light source, this work defines and calculates the photo-killing action spectrum for lung aPDI in the exemplary case of cystic fibrosis. This was obtained by applying a semi-theoretical modelling with Monte Carlo simulations, according to previously published methodology related to stomach infections and applied to the infected trachea, bronchi, bronchioles and alveoli. In each of these regions, the two low and high oxygen concentration cases were considered to account for the variability of in vivo conditions, together with the presence of endogenous porphyrins and other relevant absorbers/diffusers inside the illuminated biofilm/mucous layer. Furthermore, an a priori method to obtain the “best illumination wavelengths” was defined, starting from maximizing porphyrin and light absorption at any depth. The obtained action spectrum is peaked at 394 nm and mostly follows porphyrin extinction coefficient behavior. This is confirmed by the results from the best illumination wavelengths, which reinforces the robustness of our approach. These results can offer important indications for the synthesis of the aerosolized light source and definition of its most effective emission spectrum, suggesting a flexible platform to be considered in further applications.

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