Thang Duong - Academia.edu (original) (raw)

Papers by Thang Duong

Research paper thumbnail of Origins of Cross-Orientation Suppression in the Visual Cortex

Journal of Neurophysiology, 2006

The response of a neuron in striate cortex to an optimally oriented stimulus is suppressed by a s... more The response of a neuron in striate cortex to an optimally oriented stimulus is suppressed by a superimposed orthogonal stimulus. The neural mechanism underlying this cross-orientation suppression (COS) may arise from intracortical or subcortical processes or from both. Recent studies of the temporal frequency and adaptation properties of COS suggest that depression at thalamo-cortical synapses may be the principal mechanism. To examine the possible role of synaptic depression in relation to COS, we measured the recovery time course of COS. We find it too rapid to be explained by synaptic depression. We also studied potential subcortical processes by measuring single cell contrast response functions for a population of LGN neurons. In general, contrast saturation is a consistent property of LGN neurons. Combined with rectifying nonlinearities in the LGN and spike threshold nonlinearities in visual cortex, contrast saturation in the LGN can account for most of the COS that is observed in the visual cortex.

Research paper thumbnail of Contrast sensitivity is enhanced by expansive nonlinear processing in the LGN

The firing rates of neurons in the central visual pathway vary with stimulus strength, but not ne... more The firing rates of neurons in the central visual pathway vary with stimulus strength, but not necessarily in a linear manner. In the contrast domain, the neural response function for cells in the primary visual cortex is characterized by expansive and compressive nonlinearities at low and high contrasts, respectively. A compressive nonlinearity at high contrast is also found for early visual pathway neurons in the LGN. This mechanism affects processing in the visual cortex. A fundamentally related issue is the possibility of an expansive nonlinearity at low contrast in LGN. To examine this possibility, we have obtained contrast-response data for a population of LGN neurons. We find for most cells that the best fit function requires an expansive component. Additionally, we have measured the responses of LGN neurons to m-sequence white noise and examine the static relationship between a linear prediction and actual spike rate. We find that this static relationship is well-fit by an expansive nonlinear power law with average exponent of 1.58. These results demonstrate that neurons in early visual pathways exhibit expansive nonlinear responses at low contrasts. While this thalamic expansive nonlinearity has been largely ignored in models of early visual processing, it may have important consequences because it potentially affects the interpretation of a variety of visual functions. intracellular study of the contrast-dependence of neuronal activity in cat visual cortex. Cereb Cortex 7: 559-570, 1997. Albrecht DG and Geisler WS. Motion selectivity and the contrast-response function of simple cells in the visual cortex. Vis Neurosci 7: 531-546, 1991. Albrecht DG and Hamilton DB. Striate cortex of monkey and cat: contrast response function. Anzai A, Ohzawa I, and Freeman RD. Neural mechanisms for processing binocular information I. Simple cells. J Neurophysiol 82: 891-908, 1999a. Anzai A, Ohzawa I, and Freeman RD. Neural mechanisms for processing binocular information II. Complex cells. J Neurophysiol 82: 909-924, 1999b. Bonin V, Mante V, and Carandini M. The statistical computation underlying contrast gain control. J Neurosci 26: 6346-6353, 2006. Bonin V, Mante V, and Carandini M. The suppressive field of neurons in lateral geniculate nucleus. J Neurosci 25: 10844-10856, 2005. Carandini M. Amplification of trial-to-trial response variability by neurons in visual cortex. PLoS Biol 2: E264, 2004. Carandini M and Heeger DJ. Summation and division by neurons in primate visual cortex. Science 264: 1333-1336, 1994. Carandini M, Heeger DJ, and Movshon JA. Linearity and normalization in simple cells of the macaque primary visual cortex. J Neurosci 17: 8621-8644, 1997. Chander D and Chichilnisky EJ. Adaptation to temporal contrast in primate and salamander retina. J Neurosci 21: 9904-9916, 2001. Chichilnisky EJ. A simple white noise analysis of neuronal light responses. Network 12: 199-213, 2001. Contreras D and Palmer L. Response to contrast of electrophysiologically defined cell classes in primary visual cortex. J Neurosci 23: 6936-6945, 2003. DeAngelis GC, Ohzawa I, and Freeman RD. Spatiotemporal organization of simplecell receptive fields in the cat's striate cortex. II. Linearity of temporal and spatial summation. DeAngelis GC, Robson JG, Ohzawa I, and Freeman RD. Organization of suppression in receptive fields of neurons in cat visual cortex. J Neurophysiol 68: 144-163, 1992. Freeman TC, Durand S, Kiper DC, and Carandini M. Suppression without inhibition in visual cortex. Neuron 35: 759-771, 2002. Gardner JL, Anzai A, Ohzawa I, and Freeman RD. Linear and nonlinear contributions to orientation tuning of simple cells in the cat's striate cortex. Vis Neurosci 16: 1115-1121, 1999. Li B, Thompson JK, Duong T, Peterson MR, and Freeman RD. Origins of crossorientation suppression in the visual cortex. J Neurophysiol 96: 1755-1764, 2006. Mante V, Frazor RA, Bonin V, Geisler WS, and Carandini M. Independence of luminance and contrast in natural scenes and in the early visual system. Nat Neurosci 8: 1690-1697, 2005. Miller KD and Troyer TW. Neural noise can explain expansive, power-law nonlinearities in neural response functions. J Neurophysiol 87: 653-659, 2002. Naka KI and Rushton WA. S-potentials from luminosity units in the retina of fish (Cyprinidae). J Physiol 185: 587-599, 1966. Nykamp DQ and Ringach DL. Full identification of a linear-nonlinear system via crosscorrelation analysis. J Vis 2: 1-11, 2002. Priebe NJ and Ferster D. Mechanisms underlying cross-orientation suppression in cat visual cortex. Nat Neurosci 9: 552-561, 2006. Reid RC, Victor JD, and Shapley RM. The use of m-sequences in the analysis of visual neurons: linear receptive field properties.

Research paper thumbnail of Transcranial Magnetic Stimulation Elicits Coupled Neural and Hemodynamic Consequences

Science, 2007

Transcranial magnetic stimulation (TMS) is an increasingly common technique used to selectively m... more Transcranial magnetic stimulation (TMS) is an increasingly common technique used to selectively modify neural processing. However, application of TMS is limited by uncertainty concerning its physiological effects. We applied TMS to the cat visual cortex and evaluated the neural and hemodynamic consequences. Short TMS pulse trains elicited initial activation (~1 minute) and prolonged suppression (5 to 10 minutes) of neural responses. Furthermore, TMS disrupted the temporal structure of activity by altering phase relationships between neural signals. Despite the complexity of this response, neural changes were faithfully reflected in hemodynamic signals; quantitative coupling was present over a range of stimulation parameters. These results demonstrate long-lasting neural responses to TMS and support the use of hemodynamic-based neuroimaging to effectively monitor these changes over time.

Research paper thumbnail of Contrast Sensitivity Is Enhanced by Expansive Nonlinear Processing in the Lateral Geniculate Nucleus

The firing rates of neurons in the central visual pathway vary with stimulus strength, but not ne... more The firing rates of neurons in the central visual pathway vary with stimulus strength, but not necessarily in a linear manner. In the contrast domain, the neural response function for cells in the primary visual cortex is characterized by expansive and compressive nonlinearities at low and high contrasts, respectively. A compressive nonlinearity at high contrast is also found for early visual pathway neurons in the LGN. This mechanism affects processing in the visual cortex. A fundamentally related issue is the possibility of an expansive nonlinearity at low contrast in LGN. To examine this possibility, we have obtained contrast-response data for a population of LGN neurons. We find for most cells that the best fit function requires an expansive component. Additionally, we have measured the responses of LGN neurons to m-sequence white noise and examine the static relationship between a linear prediction and actual spike rate. We find that this static relationship is well-fit by an expansive nonlinear power law with average exponent of 1.58. These results demonstrate that neurons in early visual pathways exhibit expansive nonlinear responses at low contrasts. While this thalamic expansive nonlinearity has been largely ignored in models of early visual processing, it may have important consequences because it potentially affects the interpretation of a variety of visual functions. intracellular study of the contrast-dependence of neuronal activity in cat visual cortex. Cereb Cortex 7: 559-570, 1997. Albrecht DG and Geisler WS. Motion selectivity and the contrast-response function of simple cells in the visual cortex. Vis Neurosci 7: 531-546, 1991. Albrecht DG and Hamilton DB. Striate cortex of monkey and cat: contrast response function. Anzai A, Ohzawa I, and Freeman RD. Neural mechanisms for processing binocular information I. Simple cells. J Neurophysiol 82: 891-908, 1999a. Anzai A, Ohzawa I, and Freeman RD. Neural mechanisms for processing binocular information II. Complex cells. J Neurophysiol 82: 909-924, 1999b. Bonin V, Mante V, and Carandini M. The statistical computation underlying contrast gain control. J Neurosci 26: 6346-6353, 2006. Bonin V, Mante V, and Carandini M. The suppressive field of neurons in lateral geniculate nucleus. J Neurosci 25: 10844-10856, 2005. Carandini M. Amplification of trial-to-trial response variability by neurons in visual cortex. PLoS Biol 2: E264, 2004. Carandini M and Heeger DJ. Summation and division by neurons in primate visual cortex. Science 264: 1333-1336, 1994. Carandini M, Heeger DJ, and Movshon JA. Linearity and normalization in simple cells of the macaque primary visual cortex. J Neurosci 17: 8621-8644, 1997. Chander D and Chichilnisky EJ. Adaptation to temporal contrast in primate and salamander retina. J Neurosci 21: 9904-9916, 2001. Chichilnisky EJ. A simple white noise analysis of neuronal light responses. Network 12: 199-213, 2001. Contreras D and Palmer L. Response to contrast of electrophysiologically defined cell classes in primary visual cortex. J Neurosci 23: 6936-6945, 2003. DeAngelis GC, Ohzawa I, and Freeman RD. Spatiotemporal organization of simplecell receptive fields in the cat's striate cortex. II. Linearity of temporal and spatial summation. DeAngelis GC, Robson JG, Ohzawa I, and Freeman RD. Organization of suppression in receptive fields of neurons in cat visual cortex. J Neurophysiol 68: 144-163, 1992. Freeman TC, Durand S, Kiper DC, and Carandini M. Suppression without inhibition in visual cortex. Neuron 35: 759-771, 2002. Gardner JL, Anzai A, Ohzawa I, and Freeman RD. Linear and nonlinear contributions to orientation tuning of simple cells in the cat's striate cortex. Vis Neurosci 16: 1115-1121, 1999. Li B, Thompson JK, Duong T, Peterson MR, and Freeman RD. Origins of crossorientation suppression in the visual cortex. J Neurophysiol 96: 1755-1764, 2006. Mante V, Frazor RA, Bonin V, Geisler WS, and Carandini M. Independence of luminance and contrast in natural scenes and in the early visual system. Nat Neurosci 8: 1690-1697, 2005. Miller KD and Troyer TW. Neural noise can explain expansive, power-law nonlinearities in neural response functions. J Neurophysiol 87: 653-659, 2002. Naka KI and Rushton WA. S-potentials from luminosity units in the retina of fish (Cyprinidae). J Physiol 185: 587-599, 1966. Nykamp DQ and Ringach DL. Full identification of a linear-nonlinear system via crosscorrelation analysis. J Vis 2: 1-11, 2002. Priebe NJ and Ferster D. Mechanisms underlying cross-orientation suppression in cat visual cortex. Nat Neurosci 9: 552-561, 2006. Reid RC, Victor JD, and Shapley RM. The use of m-sequences in the analysis of visual neurons: linear receptive field properties.

Research paper thumbnail of Cross-Orientation Suppression: Monoptic and Dichoptic Mechanisms Are Different

Journal of Neurophysiology, 2005

Research paper thumbnail of Spatial Frequency-Specific Contrast Adaptation Originates in the Primary Visual Cortex

Journal of Neurophysiology, 2007

tation to a high-contrast grating stimulus causes reduced sensitivity to subsequent presentation ... more tation to a high-contrast grating stimulus causes reduced sensitivity to subsequent presentation of a visual stimulus with similar spatial characteristics. This behavioral finding has been attributed by neurophysiological studies to processes within the visual cortex. However, some evidence indicates that contrast adaptation phenomena are also found in early visual pathways. Adaptation effects have been reported in retina and lateral geniculation nucleus (LGN). It is possible that these early pathways could be the physiological origin of the cortical adaptation effect. To study this, we recorded from single neurons in the cat's LGN. We find that contrast adaptation in the LGN, unlike that in the visual cortex, is not spatial frequency specific, i.e., adaptation effects apply to a broad range of spatial frequencies. In addition, aside from the amplitude attenuation, the shape of spatial frequency tuning curves of LGN cells is not affected by contrast adaptation. Again, these findings are unlike those found for cells in the visual cortex. Together, these results demonstrate that pattern specific contrast adaptation is a cortical process.

Research paper thumbnail of BAN CUU TRO THIETKE KY THAT

Research paper thumbnail of Origins of Cross-Orientation Suppression in the Visual Cortex

Journal of Neurophysiology, 2006

The response of a neuron in striate cortex to an optimally oriented stimulus is suppressed by a s... more The response of a neuron in striate cortex to an optimally oriented stimulus is suppressed by a superimposed orthogonal stimulus. The neural mechanism underlying this cross-orientation suppression (COS) may arise from intracortical or subcortical processes or from both. Recent studies of the temporal frequency and adaptation properties of COS suggest that depression at thalamo-cortical synapses may be the principal mechanism. To examine the possible role of synaptic depression in relation to COS, we measured the recovery time course of COS. We find it too rapid to be explained by synaptic depression. We also studied potential subcortical processes by measuring single cell contrast response functions for a population of LGN neurons. In general, contrast saturation is a consistent property of LGN neurons. Combined with rectifying nonlinearities in the LGN and spike threshold nonlinearities in visual cortex, contrast saturation in the LGN can account for most of the COS that is observed in the visual cortex.

Research paper thumbnail of Contrast sensitivity is enhanced by expansive nonlinear processing in the LGN

The firing rates of neurons in the central visual pathway vary with stimulus strength, but not ne... more The firing rates of neurons in the central visual pathway vary with stimulus strength, but not necessarily in a linear manner. In the contrast domain, the neural response function for cells in the primary visual cortex is characterized by expansive and compressive nonlinearities at low and high contrasts, respectively. A compressive nonlinearity at high contrast is also found for early visual pathway neurons in the LGN. This mechanism affects processing in the visual cortex. A fundamentally related issue is the possibility of an expansive nonlinearity at low contrast in LGN. To examine this possibility, we have obtained contrast-response data for a population of LGN neurons. We find for most cells that the best fit function requires an expansive component. Additionally, we have measured the responses of LGN neurons to m-sequence white noise and examine the static relationship between a linear prediction and actual spike rate. We find that this static relationship is well-fit by an expansive nonlinear power law with average exponent of 1.58. These results demonstrate that neurons in early visual pathways exhibit expansive nonlinear responses at low contrasts. While this thalamic expansive nonlinearity has been largely ignored in models of early visual processing, it may have important consequences because it potentially affects the interpretation of a variety of visual functions. intracellular study of the contrast-dependence of neuronal activity in cat visual cortex. Cereb Cortex 7: 559-570, 1997. Albrecht DG and Geisler WS. Motion selectivity and the contrast-response function of simple cells in the visual cortex. Vis Neurosci 7: 531-546, 1991. Albrecht DG and Hamilton DB. Striate cortex of monkey and cat: contrast response function. Anzai A, Ohzawa I, and Freeman RD. Neural mechanisms for processing binocular information I. Simple cells. J Neurophysiol 82: 891-908, 1999a. Anzai A, Ohzawa I, and Freeman RD. Neural mechanisms for processing binocular information II. Complex cells. J Neurophysiol 82: 909-924, 1999b. Bonin V, Mante V, and Carandini M. The statistical computation underlying contrast gain control. J Neurosci 26: 6346-6353, 2006. Bonin V, Mante V, and Carandini M. The suppressive field of neurons in lateral geniculate nucleus. J Neurosci 25: 10844-10856, 2005. Carandini M. Amplification of trial-to-trial response variability by neurons in visual cortex. PLoS Biol 2: E264, 2004. Carandini M and Heeger DJ. Summation and division by neurons in primate visual cortex. Science 264: 1333-1336, 1994. Carandini M, Heeger DJ, and Movshon JA. Linearity and normalization in simple cells of the macaque primary visual cortex. J Neurosci 17: 8621-8644, 1997. Chander D and Chichilnisky EJ. Adaptation to temporal contrast in primate and salamander retina. J Neurosci 21: 9904-9916, 2001. Chichilnisky EJ. A simple white noise analysis of neuronal light responses. Network 12: 199-213, 2001. Contreras D and Palmer L. Response to contrast of electrophysiologically defined cell classes in primary visual cortex. J Neurosci 23: 6936-6945, 2003. DeAngelis GC, Ohzawa I, and Freeman RD. Spatiotemporal organization of simplecell receptive fields in the cat's striate cortex. II. Linearity of temporal and spatial summation. DeAngelis GC, Robson JG, Ohzawa I, and Freeman RD. Organization of suppression in receptive fields of neurons in cat visual cortex. J Neurophysiol 68: 144-163, 1992. Freeman TC, Durand S, Kiper DC, and Carandini M. Suppression without inhibition in visual cortex. Neuron 35: 759-771, 2002. Gardner JL, Anzai A, Ohzawa I, and Freeman RD. Linear and nonlinear contributions to orientation tuning of simple cells in the cat's striate cortex. Vis Neurosci 16: 1115-1121, 1999. Li B, Thompson JK, Duong T, Peterson MR, and Freeman RD. Origins of crossorientation suppression in the visual cortex. J Neurophysiol 96: 1755-1764, 2006. Mante V, Frazor RA, Bonin V, Geisler WS, and Carandini M. Independence of luminance and contrast in natural scenes and in the early visual system. Nat Neurosci 8: 1690-1697, 2005. Miller KD and Troyer TW. Neural noise can explain expansive, power-law nonlinearities in neural response functions. J Neurophysiol 87: 653-659, 2002. Naka KI and Rushton WA. S-potentials from luminosity units in the retina of fish (Cyprinidae). J Physiol 185: 587-599, 1966. Nykamp DQ and Ringach DL. Full identification of a linear-nonlinear system via crosscorrelation analysis. J Vis 2: 1-11, 2002. Priebe NJ and Ferster D. Mechanisms underlying cross-orientation suppression in cat visual cortex. Nat Neurosci 9: 552-561, 2006. Reid RC, Victor JD, and Shapley RM. The use of m-sequences in the analysis of visual neurons: linear receptive field properties.

Research paper thumbnail of Transcranial Magnetic Stimulation Elicits Coupled Neural and Hemodynamic Consequences

Science, 2007

Transcranial magnetic stimulation (TMS) is an increasingly common technique used to selectively m... more Transcranial magnetic stimulation (TMS) is an increasingly common technique used to selectively modify neural processing. However, application of TMS is limited by uncertainty concerning its physiological effects. We applied TMS to the cat visual cortex and evaluated the neural and hemodynamic consequences. Short TMS pulse trains elicited initial activation (~1 minute) and prolonged suppression (5 to 10 minutes) of neural responses. Furthermore, TMS disrupted the temporal structure of activity by altering phase relationships between neural signals. Despite the complexity of this response, neural changes were faithfully reflected in hemodynamic signals; quantitative coupling was present over a range of stimulation parameters. These results demonstrate long-lasting neural responses to TMS and support the use of hemodynamic-based neuroimaging to effectively monitor these changes over time.

Research paper thumbnail of Contrast Sensitivity Is Enhanced by Expansive Nonlinear Processing in the Lateral Geniculate Nucleus

The firing rates of neurons in the central visual pathway vary with stimulus strength, but not ne... more The firing rates of neurons in the central visual pathway vary with stimulus strength, but not necessarily in a linear manner. In the contrast domain, the neural response function for cells in the primary visual cortex is characterized by expansive and compressive nonlinearities at low and high contrasts, respectively. A compressive nonlinearity at high contrast is also found for early visual pathway neurons in the LGN. This mechanism affects processing in the visual cortex. A fundamentally related issue is the possibility of an expansive nonlinearity at low contrast in LGN. To examine this possibility, we have obtained contrast-response data for a population of LGN neurons. We find for most cells that the best fit function requires an expansive component. Additionally, we have measured the responses of LGN neurons to m-sequence white noise and examine the static relationship between a linear prediction and actual spike rate. We find that this static relationship is well-fit by an expansive nonlinear power law with average exponent of 1.58. These results demonstrate that neurons in early visual pathways exhibit expansive nonlinear responses at low contrasts. While this thalamic expansive nonlinearity has been largely ignored in models of early visual processing, it may have important consequences because it potentially affects the interpretation of a variety of visual functions. intracellular study of the contrast-dependence of neuronal activity in cat visual cortex. Cereb Cortex 7: 559-570, 1997. Albrecht DG and Geisler WS. Motion selectivity and the contrast-response function of simple cells in the visual cortex. Vis Neurosci 7: 531-546, 1991. Albrecht DG and Hamilton DB. Striate cortex of monkey and cat: contrast response function. Anzai A, Ohzawa I, and Freeman RD. Neural mechanisms for processing binocular information I. Simple cells. J Neurophysiol 82: 891-908, 1999a. Anzai A, Ohzawa I, and Freeman RD. Neural mechanisms for processing binocular information II. Complex cells. J Neurophysiol 82: 909-924, 1999b. Bonin V, Mante V, and Carandini M. The statistical computation underlying contrast gain control. J Neurosci 26: 6346-6353, 2006. Bonin V, Mante V, and Carandini M. The suppressive field of neurons in lateral geniculate nucleus. J Neurosci 25: 10844-10856, 2005. Carandini M. Amplification of trial-to-trial response variability by neurons in visual cortex. PLoS Biol 2: E264, 2004. Carandini M and Heeger DJ. Summation and division by neurons in primate visual cortex. Science 264: 1333-1336, 1994. Carandini M, Heeger DJ, and Movshon JA. Linearity and normalization in simple cells of the macaque primary visual cortex. J Neurosci 17: 8621-8644, 1997. Chander D and Chichilnisky EJ. Adaptation to temporal contrast in primate and salamander retina. J Neurosci 21: 9904-9916, 2001. Chichilnisky EJ. A simple white noise analysis of neuronal light responses. Network 12: 199-213, 2001. Contreras D and Palmer L. Response to contrast of electrophysiologically defined cell classes in primary visual cortex. J Neurosci 23: 6936-6945, 2003. DeAngelis GC, Ohzawa I, and Freeman RD. Spatiotemporal organization of simplecell receptive fields in the cat's striate cortex. II. Linearity of temporal and spatial summation. DeAngelis GC, Robson JG, Ohzawa I, and Freeman RD. Organization of suppression in receptive fields of neurons in cat visual cortex. J Neurophysiol 68: 144-163, 1992. Freeman TC, Durand S, Kiper DC, and Carandini M. Suppression without inhibition in visual cortex. Neuron 35: 759-771, 2002. Gardner JL, Anzai A, Ohzawa I, and Freeman RD. Linear and nonlinear contributions to orientation tuning of simple cells in the cat's striate cortex. Vis Neurosci 16: 1115-1121, 1999. Li B, Thompson JK, Duong T, Peterson MR, and Freeman RD. Origins of crossorientation suppression in the visual cortex. J Neurophysiol 96: 1755-1764, 2006. Mante V, Frazor RA, Bonin V, Geisler WS, and Carandini M. Independence of luminance and contrast in natural scenes and in the early visual system. Nat Neurosci 8: 1690-1697, 2005. Miller KD and Troyer TW. Neural noise can explain expansive, power-law nonlinearities in neural response functions. J Neurophysiol 87: 653-659, 2002. Naka KI and Rushton WA. S-potentials from luminosity units in the retina of fish (Cyprinidae). J Physiol 185: 587-599, 1966. Nykamp DQ and Ringach DL. Full identification of a linear-nonlinear system via crosscorrelation analysis. J Vis 2: 1-11, 2002. Priebe NJ and Ferster D. Mechanisms underlying cross-orientation suppression in cat visual cortex. Nat Neurosci 9: 552-561, 2006. Reid RC, Victor JD, and Shapley RM. The use of m-sequences in the analysis of visual neurons: linear receptive field properties.

Research paper thumbnail of Cross-Orientation Suppression: Monoptic and Dichoptic Mechanisms Are Different

Journal of Neurophysiology, 2005

Research paper thumbnail of Spatial Frequency-Specific Contrast Adaptation Originates in the Primary Visual Cortex

Journal of Neurophysiology, 2007

tation to a high-contrast grating stimulus causes reduced sensitivity to subsequent presentation ... more tation to a high-contrast grating stimulus causes reduced sensitivity to subsequent presentation of a visual stimulus with similar spatial characteristics. This behavioral finding has been attributed by neurophysiological studies to processes within the visual cortex. However, some evidence indicates that contrast adaptation phenomena are also found in early visual pathways. Adaptation effects have been reported in retina and lateral geniculation nucleus (LGN). It is possible that these early pathways could be the physiological origin of the cortical adaptation effect. To study this, we recorded from single neurons in the cat's LGN. We find that contrast adaptation in the LGN, unlike that in the visual cortex, is not spatial frequency specific, i.e., adaptation effects apply to a broad range of spatial frequencies. In addition, aside from the amplitude attenuation, the shape of spatial frequency tuning curves of LGN cells is not affected by contrast adaptation. Again, these findings are unlike those found for cells in the visual cortex. Together, these results demonstrate that pattern specific contrast adaptation is a cortical process.

Research paper thumbnail of BAN CUU TRO THIETKE KY THAT