11/11/2011 Reading L44. Neural Simmons and Young (2010) Chapter 7: - - PDF document

11/11/2011 1

• L44. Neural

Circuits for Motion Detection

Friday, 11/11/11

• C. D. Hopkins

Cajal and Sanchez (1915)

Reading

Simmons and Young (2010) Chapter 7: pp. 104-125. Borst A, Euler T. Seeing things in motion: models, circuits, and mechanisms. Neuron. 2011 Sep 22;71(6):974-94.

Neural Circuits for Motion

1. The ability to see motion is fundamental to vision, nearly as important as seeing differences between light and dark. Yet, motion is not encoded in the photoreceptor layers of the visual system, but it is encoded after just a few synapses. What is the neural circuit that is responsible for creating directional selectivity, and a sensitivity to motion? 2. Motion detection is a fundamental example of neuronal computation. 3. Motion detection is framed in the behavioral and theoretical work of Hassenstein and Reichardt studying fly vision. The logic also applies to primate cortex, to human physiology and psychophysics, and to the vertebrate retina of some species (mice, rabbits). Theory and physiology are both important for understanding general principles of neural circuit for motion.

The computational problem

moving bar.swf

Borst and Euler (2011) Object position = x(t) Object velocity = dx/dt Brightness = l(x) Brightness gradient = dl/dx slow movement intensity ramps up slowly. response ramps up rapidly fast movement A single photoreceptor can distinguish neither brightness nor speed

The computational problem

moving bar.swf

Borst and Euler (2011) Object position = x(t) Object velocity = dx/dt Brightness = l(x) Brightness gradient = dl/dx intensity ramps up slowly. response ramps up rapidly A single photoreceptor can distinguish neither brightness nor speed same velocity shallow steep ramp

A Gradient Calculator could, in theory, act as a velocity detector in the visual system

Response is high pass filtered to approximate the derivative dR/dt Summed from two detectors. Divided by the difference in light intensity between the two points Result is pure velocity, independent of contrast

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Gradient Calculator

• Gradient calculators

are used in machine vision.

• Relatively simple.
• Insensitive to the

pattern of stimulation.

• sensitive to noise.

Correlation Calculator

• REICHARDT

DETECTOR Correlation Calculator

• Computes the

correlation between two detectors after

• ne has been low pass

filtered with a time constant

The Reichardt (Correlation) Motion Detector

Photoreceptors Low-pass filters create delay Multiplier Subtractor

t)

• (T

(T)F A

• t)
• (T

(T)F A R(t)

2 1 1 2

1

F

1 2

1

A

2

A

2

F

Barlow Levick Detector

• Barlow and Levick

(1965)

• Identical to Reichardt

but with one half of basic Reichardt model

• And not (veto) from

inhibition delay

All motion detector models share common features. Models differ in response characteristics SIMILARITIES

• 1. All have spatially separated inputs that record

brightness

• 2. All have asymmetry in temporal filtering

– delay line in one channel – time derivative in one channel – low pass filter in one channel

• 3. Non linear computation (division, multiplication, veto)

DIFFERENCES Reichardt: sensitive to image contrast; output is maximum at one particular image velocity (which depends on spatial pattern wavelength) Gradient: insensitive to image contrast

Predicted responses of Reichardt versus Gradient Detectors

Reichardt detector shows preferred velocity Gradient detector is linear Borst, Al Phil. Trans. R. Soc. B 2007 362, 369-374

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Velocity Response of the Fly H1 Neuron

• Experiments show H1

neurons have peak response at a fixed temporal frequency (directly related to velocity), as in the Reichardt detector models

Theory: Gradient detector is susceptible to noise, while Reichardt detector is insensitive to noise. Signal to Noise Ratios for Reichardt vs. Gradient Detectors

Borst, Al Phil. Trans. R. Soc. B 2007 362, 369-374 Stimulus: sine wave grating moving with noisy velocity distribution in low luminance

DS CELLS

Directionally Selective: respond more to motion in one direction than in the opposite direction. DS in Flies lobula plate (3rd neuropil) HS: horizontal sensitive in red

Borst and Euler (2011)

• Lobula plate: third layer of

neurons in the optic pathway

• (cells in the medulla are too small

to study)

• Lobula Plate Tangential cells (LPTC)

respond to time ordered signals.

• Number about 50. Varies by
• species. HS horizontal sensitive VS

vertical sensitive

• HS: front to back motion sensitive.

Tangential Cell

Response of HS cell to moving grating DS response from HS cell to moving grating

response to black moving bar above response to white moving bar below Response to moving grating as a function of the contrast. Note that the response increases with contrast PD = preferred direction

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Tuned to velocity

The HS cell is responsive to moving gratings with a preferred velocity. The preferred velocity is dependent upon the spatial frequency of the pattern. When the temporal frequency is plotted, the two patterns are identical. Temporal frequency is the grating velocity divided by spatial wavelength.

William Levick and Horace Barlow (photo John Lisman) Levick: Australian National University (Psychology) – student with Barlow Barlow: Univ. Cambridge (student of Adrian and Rushton, post doc Kuffler)

DS cell in rabbit retina

Barlow and Levick, 1965

Barlow and Levick – two models for DS

Barlow and Levick, 1965 The Barlow and Levick DS cells are ON/OFF ganglion cells. They have dendrites that terminate in both the inner part

• f the IPL and the outer part of the IPL (accounting for the

Off response and the On response). Broad tuning to both temporal frequency and spatial frequency, but with a preference for temporal tuning (i.e. Reichardt detector) 4 sub types defined by direction (DV; VD; AP; PA) ON DS ganglion cells. Dendrites only in On sublamina of IPL OFF DS ganglion cells. Dendrites only in the Off sublamina

• f IPL.

Borst and Euler, 2011 Borst and Euler, 2011

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Borst and Euler, 2011

Network mechanisms at the cellular and subcellular level

INSECT DS Neurons in the lobula plate receive numerous inputs from Reichardt type detectors all over the visual field, but which cells? Lobula Plate Tangential Cells receive excitatory and inhibitory input from local motion sensitive elements with opposite preferred direction. Depolarize and Hyperpolarize LPTC during motion stimulation. With no depolarization, the preferred direction evokes depolarization, the null direction evokes hyperpolarization. When depolarized, the response to preferred direction is smaller When hyperpolarized the response to the preferred direction is larger. Model: two inputs, one with reversal potential above the RP, and one with reveral potential below the RP Excitatory inputs to the LPTC is via nAChRs Inhibition to LPTC is via GABA receptors. In Drosophila, both receptor types were colocalized in dendrites of HS and VS neurons. “The last decade has witnessed much progress in our understanding of the cellular and subcellular mechanisms underlying direction selectivity. To a large extent, this is due to the application of advanced optical as well as genetic methods to this problem….. Another amazing fact is how much effort over so many years had to be invested in this

• ne single problem of direction selectivity in order to achieve the current level of

understanding, a problem that, in terms of computation and information processing, seems quite modest (telling leftward from rightward), compared to the complex intellectual capabilities of humans. Our hope is that understanding this simple neural computation of direction selectivity in full detail will provide an important stepping stone toward our understanding of more complex functions of the nervous system.” Borst and Euler (2011)

Bibliography

Barlow, H. B. and Levick, W. R. (1965). The mechanism of directionally selective units in rabbit's retina. J Physiol 178, 477-504. Borst A, Euler T. Seeing things in motion: models, circuits, and mechanisms.Neuron. 2011 Sep 22;71(6):974-94. Epub 2011 Sep 21. PubMed PMID: 21943597. Borst, A. (2006) Correlation versus gradient type motion detectors: the pros and the cons. Phil. Trans of Roy. Soc. B. 362:369-374. Borst, A. & Egelhaaf, M. 1989 Principles of visual motion detection. Trends Neurosci. 12, 297–306.Clifford CW, Ibbotson

• MR. Fundamental mechanisms of visual motion detection: models, cells and functions. Prog Neurobiol. 2002 Dec;68(6):409-

37. Douglass JK, Strausfeld NJ. Optic flow representation in the optic lobes of Diptera: modeling innervation matrices onto collators and their evolutionary implications. J Comp Physiol A. 2000 Sep;186(9):799-811. PubMed PMID: 11085634. Clifford, C. W. G. and Ibbotson, M. R. (2003). Fundamental mechanisms of visual motion detection: models, cells and functions. Progress in Neurobiology 68, 409-437. Joesch M, Schnell B, Raghu SV, Reiff DF, Borst A. ON and OFF pathways in Drosophila motion vision. Nature. 2010 Nov 11;468(7321):300-4. PubMed PMID: 21068841. Kay, J. N., De la Huerta, I., Kim, I. J., Zhang, Y., Yamagata, M., Chu, M. W., Meister, M. and Sanes, J. R. (2011). Retinal ganglion cells with distinct directional preferences differ in molecular identity, structure, and central projections. J Neurosci 31, 7753-62. Kim, I. J., Zhang, Y., Yamagata, M., Meister, M. and Sanes, J. R. (2008). Molecular identification of a retinal cell type that responds to upward motion. Nature 452, 478-82. Münch TA, da Silveira RA, Siegert S, Viney TJ, Awatramani GB, Roska B.Approach sensitivity in the retina processed by a multifunctional neural circuit.Nat Neurosci. 2009 Oct;12(10):1308-16. Epub 2009 Sep 6. PubMed PMID: 19734895. Köhler T, Röchter F, Lindemann JP, Möller R. Bio-inspired motion detection in an FPGA-based smart camera module. Bioinspir

• Biomim. 2009 Mar;4(1):015008. Epub 2009 Mar 4. PubMed PMID: 19258686.

Hassenstein, B. & Reichardt, W. 1956 Systemtheoretische Analyse der Zeit-Reihenfolgen- und Vorzeichenauswertung bei der Bewegungsperzeption des Russelkafers Chlorophanus. Z. Naturforsch. 11b, 513–524. Reichardt, W. 1961 Autocorrelation, a principle for the evaluation of sensory information by the central nervous system. In Sensory communication (ed. W. A. Rosenblith), pp. 303–317. New York, NY; London, UK: MIT Press; Wiley.