Physiology and functions of the mammalian visual system (an introduction to systems/ computational neuroscience) Davide Zoccolan!
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1 Physiology and functions of the mammalian visual system (an introduction to systems/ computational neuroscience) Davide Zoccolan!
2 Physiology and functions of the mammalian visual system (an introduction to systems/ computational neuroscience) Davide Zoccolan!
3 Layout of the lectures! 1. Introduction to anatomy and physiology of the visual system! ~3 hours 2. Descriptive models of visual neurons! 3. Mechanistic models of the visual system! 4. Functional models of the visual system! Computational approaches ~3 hours
4 Layout of the lecture! 1. Introduction to anatomy and physiology of the visual system! A systems/computational approach to the study of the visual system! Anatomy of the visual system! Classic findings about physiology of lower-level visual areas! Classic findings about physiology of higher-level visual areas! Beyond classic findings!
5 Layout of the lecture! 1. Introduction to anatomy and physiology of the visual system! A systems/computational approach to the study of the visual system"
6 Systems neuroscience"!neuronal processing =!!information processing =!!computation!
7 Systems neuroscience" Data! Result!!Computation:!!Processing of input data according to certain rules!
8 Example of computation: vision of a scene! Data:! Visual information! Thorpe & Fabre-Thorpe 2001
9 Example of computation: vision of a scene! Scene understanding:" What objects?!where?! Result (1)!
10 Example of computation: vision of a scene! Motor action:" get the orange!! Result (2)!
11 Example of computation: vision of a scene! Thorpe & Fabre-Thorpe 2001 The macaque brain!
12 The macaque brain! Thorpe & Fabre-Thorpe 2001!Macaque brain = standard model system for most higher-level cognitive processes!
13 Modular-hierarchical organization of the brain! Thorpe & Fabre-Thorpe 2001
14 Modular-hierarchical organization of the brain!
15 Visual information is re-formatted and transformed at each processing stage! Chain of computations!!visual Scene!
16 Ultimate goal of systems and computational neuroscience!!to understand the computation at each processing stage! I(t)! T?! O(t)!!Visual Scene!
17 what kind of understanding?! 1. What a processing stage is doing?! Descriptive understanding! 2. How is it doing it?! Mechanistic understanding! 3. Why is it doing it?! Functional understanding! Adapted from Dayan & Abbott, 2001
18 Systems!" Computational! 1. What a processing stage is doing?! Descriptive models" 2. How is it doing it?! Mechanistic models" 3. Why is it doing it?! Functional models" Adapted from Dayan & Abbott, 2001
19 Ultimate goal of systems and computational neuroscience!!to understand the computation at each processing stage! I(t)! T?! O(t)!!Visual Scene!
20 Ultimate goal of systems and computational neuroscience! What a processing stage is made of?! I(t)! T?! O(t)!!Visual Scene!
21 The neuron: the fundamental processing unit!
22 Neuronal networks: the fundamental processing modules!
23 The neuron (overview)! Input! Output! I(t)! Computation! T! O(t)!
24 The neuron (overview)! Input! Output! Computation! Cellular electrophysiology" Biophysics" Well understood:! Cellular/molecular mechanisms! functions!
25 Action potential: the neuronal signal! Extracellular environment (0 mv)! Cell membrane! Intracellular environment (-70 mv)!
26 Action potential: the neuronal signal! Extracellular environment (0 mv)! Na+! Ionic channel! Intracellular environment (-70 mv)!
27 Action potential: the neuronal signal! "Action potential:"!fast depolarization of cell membrane! Na+! Ionic channel!
28 Action potential: the neuronal signal!!input from presynaptic cells!!output to postsynaptic cells!
29 Action potential: the neuronal signal!!input from presynaptic cells!!output to postsynaptic cells! The synapse"
30 Action potential (spikes) trains"!input from presynaptic cells!!output to postsynaptic cells! Neuronal response:! Number of spikes per second (spikes/s)! Firing rate: r(t:t+!t)! 250 ms! r(0:250) = 24 spikes/s!
31 Neuronal networks: the fundamental processing modules!!in a neuronal network, spike trains convey information from one neuron to the next in the processing hierarchy!
32 Summary! Brain! Brain Area! Neuronal network! Neuron! Molecule! Genes!
33 The goal of neuroscience! Brain! Brain Area! To understand the computation! Neuronal network! I(t)! T?! U(t)! Neuron! Molecule! Genes!
34 Multidisciplinary nature of neuroscience! Cognitive Sciences! Psycology! Systems Neurosciences! Computational Neuroscience! Cell electrophysiology! Biophysics of ionic channels! Molecular biology! Genetics!
35 Multidisciplinary nature of neuroscience! Cognitive Sciences! Psycology! Systems Neurosciences! Computational Neuroscience! Cell electrophysiology! Biophysics of ionic channels! Molecular biology! Genetics!
36 Layout of the lecture! 1. Introduction to anatomy and physiology of the visual system! A systems/computational approach to the study of the visual system!
37 Layout of the lecture! 1. Introduction to anatomy and physiology of the visual system! A systems/computational approach to the study of the visual system! Anatomy of the visual system"
38 Vision and visual pathways! Thorpe & Fabre-Thorpe 2001
39 Vision begins with the eye!
40 The visual field!
41 Adapted from Hubel! Retina!
42 Retina! Photoreceptors:"!- cones (~5 millions)!!- rods (~100 millions)! Interneurons:"!- bipolar cells!!- horizontal cells!!- amacrine cells! Ganglion cells (~1 million)!!(output spiking neurons)!
43 Fovea!
44 Photoreceptors distribution! Cone System" - Cones concentrated in fovea! - High acuity! - Daily vision (low light sensitivity)! - High temporal resolution (55 Hz)! Rod System" - Rods not present in fovea! - Low acuity! - Night vision (high light sensitivity)! - Low temporal resolution (12 Hz)!
45 Photoreceptors distribution!
46 Visual processing in the retina! Photoreceptors:"!- cones (~5 millions)!!- rods (~100 millions)! Interneurons:"!- bipolar cells!!- horizontal cells!!- amacrine cells! Ganglion cells (~1 million)!!(output spiking neurons)!
47 Visual processing in the retina!!what kind of signal any given Ganglion (i.e., output) cell receives?!
48 Visual processing in the retina! Cone direct pathway:" # central cone! # bipolar cell! # ganglion cell!
49 Visual processing in the retina! Cone direct pathway:" # central cone! # bipolar cell! # ganglion cell!
50 Visual processing in the retina! Cone direct pathway:" # central rod! # bipolar cell! # ganglion cell!
51 Visual processing in the retina! Cone direct pathway:" # central rod! # bipolar cell! # ganglion cell!
52 Visual processing in the retina! Cone lateral pathway:" # lateral cones! # horizontal cell! # bipolar cell! # ganglion cell!
53 Visual processing in the retina! Cone lateral pathway:" # lateral cones! # horizontal cell! # bipolar cell! # ganglion cell!
54 Visual processing in the retina! Rod pathway:" # rod! # rod bipolar cell! # amacrine cell! # ganglion cell!
55 Visual processing in the retina! Rod pathway:" # rod! # rod bipolar cell! # amacrine cell! # ganglion cell!
56 Convergence in the retina!!in the periphery:"!massive convergence of photoreceptor signals onto single ganglion cells:! Both cones and rods project to ganglion cells! As many as 1,500 rods can converge on a single ganglion cell! "In the fovea:"!no convergence:! A single ganglion cell receives input form a single cone!
57 Convergence in the retina! Overall: " Strong convergence (~105 millions photoreceptors # 1 million ganglion cells)! photoreceptors" ganglion" cells"
58 Two classes of retinal ganglion cells:!1) Midget (small dendritic field)!2) Parasol (large dendritic field)!
59 Two classes of retinal ganglion cells:!1) Midget (small dendritic field)!2) Parasol (large dendritic field)! "Parvocellular pathway" "Magnocellular" "pathway"
60 Two classes of retinal ganglion cells:!1) Midget (small dendritic field)!2) Parasol (large dendritic field)!!midget: gets input from fewer photoreceptors!!parasol: gets input from more photoreceptors! Parvo" Magno"
61 Two classes of retinal ganglion cells:!1) Midget (small dendritic field)!2) Parasol (large dendritic field)! 1. Midget (Parvocellular pathway):! Small receptive fields" Encode images at high resolution: up to 60 cpd (full sampling resolution of photoreceptors)! Respond to specific wavelength (colors)! # Analysis of fine features: form and color perception" 2. Parasol (Magnocellular pathway):! Large receptive fields" Encode images at lower resolution: up to 20 cpd " Able to follow rapid changes of the stimulus!! "Analysis of gross features and movements"
62 The notion of Visual Receptive Field (RF)! 1. How do we measure the size of objects in the visual field?! 2. What units?!!# we want a measure of the size of the projection of an object onto the retina (through the eyeʼs optics)!!# degrees of visual angle"
63 The notion of Visual Receptive Field (RF)! " = tan #1 h d h d "
64 The notion of Visual Receptive Field (RF)!
65 Two classes of retinal ganglion cells:!1) Midget (small dendritic field)!2) Parasol (large dendritic field)! 1. Midget (Parvocellular pathway):! Small receptive fields" Encode images at high resolution: up to 60 cpd (full sampling resolution of photoreceptors)! Respond to specific wavelength (colors)! # Analysis of fine features: form and color perception" 2. Parasol (Magnocellular pathway):! Large receptive fields" Encode images at lower resolution: up to 20 cpd " Able to follow rapid changes of the stimulus!! "Analysis of gross features and movements"
66 Minimal and average RF size of Ganglion cells increase linearly as a function of eccentricity!
67 Minimal and average RF size of Ganglion cells increase linearly as a function of eccentricity! "Periphery " "! Lower resolution!!# RF ~ 3º-5º! Fovea " # High resolution! # # RF ~ few minutes of arc (60 min = 1 deg)!
68 Minimal and average RF size of Ganglion cells increase linearly as a function of eccentricity!
69 Minimal and average RF size of Ganglion cells increase linearly as a function of eccentricity!
70 Image sampling by the retina!
71 Image sampling by the retina!
72 Layout of the lecture! 1. Introduction to anatomy and physiology of the visual system! A systems/computational approach to the study of the visual system! Anatomy of the visual system"
73 Primary visual pathway! Retina! Thalamus" Lateral Geniculate Nucleus (LGN)"
74 Lateral Geniculate Nucleus (LGN)!
75 Lateral Geniculate Nucleus (LGN)!
76 Lateral Geniculate Nucleus (LGN)! In LGN the fovea is overrepresented!! ½ LGN neurons represent the fovea! Six layers of cell bodies! Retinal Magno- and Parvocellular inputs remain segregated! 4 P layers! 2 M layers! Inputs from each eye remains segregated!
77 Primary visual pathway! Retina! Thalamus! Lateral Geniculate Nucleus (LGN)! Primary visual cortex (V1)"!(each visual hemifield -not each eye- projects to the opposite hemisphere )!
78 Primary visual cortex (V1)!
79 Primary visual cortex (V1)!
80 The retinotopic map in V1! Right hemi-field!!left occiptal lobe! Tootell et al 1982!Imaging: a radioactive analogue of glucose taken up by active neurons!
81 The retinotopic map in V1! Schwartz, 1980 Tootell et al 1982 Radial grid # cartesian grid!
82 The retinotopic map in V1! Schwartz, 1980 Tootell et al 1982 Radial grid # cartesian grid!
83 The retinotopic map in V1! Schwartz, 1980 Tootell et al 1982 Radial grid # cartesian grid!
84 The retinotopic map in V1! Schwartz, 1980 Tootell et al 1982 Radial grid # cartesian grid!
85 The retinotopic map in V1! Schwartz, 1980 Tootell et al 1982 Radial grid # cartesian grid!
86 The retinotopic map in V1! Schwartz, 1980 Tootell et al 1982 Radial grid # cartesian grid!
87 The retinotopic map in V1! y r ϑ z = (x,y) = (r,ϑ) w = f (z) v w = (u,v) x u z = x + iy = re i"!complex logarithmic (log-polar) map! w = f (z) = u(z) + iv(z) w = log(z) = log r + i"
88 The retinotopic map in V1! Schwartz, 1980 Tootell et al 1982 w = log(z + a)
89 The retinotopic map in V1! Tootell et al 1982!½ of V1 is devoted to representation of the foveal region!!
90 Beyond V1!
91 Beyond V1: area V2" Staining with Cytochrome Oxidase!
92 Beyond V1: two parallel pathways" Magno # Dorsal" Parvo, Magno # Ventral"
93 The dorsal and ventral visual pathways" Monkey visual cerebral cortex V1 LIP 7a VIP DP MST MT V4 V2 VP V4 TEO PIT CIT AIT
94 The dorsal and ventral visual pathways" Streams of Processing Ungerleider & Mishkin Parietal Pathway "Where" Spatial relationships Motion V1 LIP 7a VIP DP MST MT V4 V2 V4 PIT VP TEO Temporal Pathway CIT AIT "What" Pattern Identity
95 The dorsal and ventral visual pathways" Streams of Processing Ungerleider & Mishkin, 1982 Parietal Pathway where, orienting, ambient, spatial V1 LIP 7a VIP DP MST MT V4 V2 VP V4 TEO PIT CIT Temporal Pathway AIT what, evaluating, focal, foveal, figural
96 The dorsal and ventral visual pathways" Parallel Pathways in Visual Cortex Parietal Pathway Temporal Pathway AIT 7a CIT VIP MST LIP PIT Parietal Pathway MT V4 V1 7a DP V4 V2 LIP VIP MST MT V2 V2 V1 V1 VP V4 TEO PIT CIT AIT Temporal Pathway
97 The dorsal and ventral visual pathways" Hierarchical elaboration of response selectivities Parietal pathway Temporal pathway AIT 7a CIT VIP MST LIP PIT MT V4 V2 V2 V1 V1
98 The dorsal and ventral visual pathways" Hierarchy of Cortical Visual Areas Felleman and Van Essen 1991 LGN RGC
99 Adapted from John Maunsell
100 Layout of the lecture! 1. Introduction to anatomy and physiology of the visual system! A systems/computational approach to the study of the visual system! Anatomy of the visual system! Classic findings about physiology of lowerlevel visual areas"
101 Physiology of low-level visual areas! Retina!Visual Scene!
102 Physiology of low-level visual areas!!goal: To understand the computation at each processing stage! I(t)! T?! O(t)!!Visual Scene!
103 Understanding the system! 1. What a processing stage is doing?! Descriptive understanding! 2. How is it doing it?! Mechanistic understanding! 3. Why is it doing it?! Functional understanding!
104 Layout of the lecture! 1. Introduction to anatomy and physiology of the visual system! A systems/computational approach to the study of the visual system! Anatomy of the visual system! Classic findings about physiology of lowerlevel visual areas" Experimental approaches in Systems Neuroscience"
105 Image adapted from Hubel 1988! Experimental approach!
106 Experimental approach! Image adapted from Hubel 1988! Extracellular recordings:" How many spikes does a given neuron produce in a given time interval as a response to a given stimulus?!
107 Experimental approach!
108 Multi-electrode arrays: monkeys!!bundle of 32 microwires made of platinum-iridium! Patil et al, 2004!
109 Multi-electrode arrays: monkeys!!bundle of 32 microwires made of platinum-iridium! Multielectrode array! Hochberg et al, 2006! Patil et al, 2004!
110 Multi-electrode arrays: monkeys! Multielectrode array! Patil et al, 2004! Hochberg et al, 2006!
111 Multi-electrode arrays: humans!
112 Multi-electrode arrays: rodents! Tetrode: four 12 µm wires twisted together to form a single probe. Microdrive: load and microposition tetrodes
113 Layout of the lecture! 1. Introduction to anatomy and physiology of the visual system! A systems/computational approach to the study of the visual system! Anatomy of the visual system! Classic findings about physiology of lowerlevel visual areas"
114 Understanding the system! 1. What a processing stage is doing?! Descriptive understanding! 2. How is it doing it?! Mechanistic understanding! 3. Why is it doing it?! Functional understanding!
115 Classic physiology! Stephen Kuffler! David Hubel! Torsten Wiesel!
116 Approach of classical physiology! Map the input-output relationship of neurons in a given area using the simplest possiple stimuli! Identify the receptive field of recorded neurons! Build tuning curves of neuronal responses! Start from low-level areas (e.g., sensory) and move gradually to higher-lever areas adjusting stimulus complexity!
117 Retinal ganglion cells! Adapted from Hubel!
118 Retinal ganglion cells! Receptive field structure! Adapted from Hubel! Adapted from Kandel, Schwartz and Jessell!
119 The receptive field! Units:! Spatial units = degrees of visual angle (deg)! deg (elevation)! Adapted from Kandel, Schwartz and Jessell! deg (azimuth)! Response INCREASES (more spikes) when luminance is ABOVE background! Response DECREASES (less spikes) when luminance is ABOVE background!
120 Retinal ganglion cells! Effective stimuli:! Light spots! Light rings! excitation! inhibition! Adapted from Kandel, Schwartz and Jessell!
121 LGN cells (in the thalamus)!!rfs very similar to retinal ganglion cells! Adapted from Kandel, Schwartz and Jessell!
122 Primary visual cortex (V1): simple cells" Adapted from Kandel, Schwartz and Jessell!
123 Primary visual cortex (V1): simple cells" Effective stimuli:! Light bar with:! - a given orientation! - in a gven position! Adapted from Kandel, Schwartz and Jessell!
124 Primary visual cortex (V1): simple cells" Ineffective stimuli:! Light bars that are orthogonal to the preferred orientation! Adapted from Kandel, Schwartz and Jessell!
125 Primary visual cortex (V1): simple cells"
126 Primary visual cortex (V1): simple cells" Ineffective stimuli:! Light bars outside the excitatory lobe!
127 Primary visual cortex (V1): simple cells" Ineffective stimuli:! Light bars outside the excitatory lobe" Diffuse Illumination of a large area!
128 Primary visual cortex (V1): simple cells" Ineffective stimuli:! Light spot! Light ring! Adapted from Kandel, Schwartz and Jessell!
129 Physiology of low-level visual areas!!goal: To understand the computation at each processing stage! I(t)! T?! O(t)!!Visual Scene!
130 Understanding the system! 1. What a processing stage is doing?! Descriptive understanding! 2. How is it doing it?! Mechanistic understanding! 3. Why is it doing it?! Functional understanding!
131 What is going on?! Retina / LGN! V1 (simple cells)! Complexity of tuning (selectivity) of visual neurons increases!
132 How is that happening?! V1 (simple cells)! LGN!
133 Primary visual cortex (V1): complex cells" simple cells! complex cells!
134 What is going on?! simple cells! complex cells! Invariance of neuronal responses to translation increases!
135 How is that happening?! simple cells! simple cells! complex cells! complex cell!
136 Architecture of the visual cortex! simple cells! complex cells! Orientation columns"
137 Architecture of the visual cortex! Ocular dominance columns" Orientation columns" Blobs"
138 Physiology of low-level visual areas! Retina!Visual Scene!
139 Physiology of low-level visual areas! Three fundamental questions:! What do visual neurons do?!!!!!(what they respond to?)! How do they do that?!!!!(what make them respond that way?)! Why do they do that?! (what function are such responses underlying?)!
140 Physiology of low-level visual areas! Three fundamental questions:! What do visual neurons do?! Qualitative description of their receptive field properties!
141 Physiology of low-level visual areas! Three fundamental questions:! What do visual neurons do?! How do they do that?!! Hypotheses on mechanisms shaping the receptive fields! simple cells! LGN! V1 (simple cells)! complex cell!
142 Physiology of low-level visual areas! Three fundamental questions:! What do visual neurons do?! How do they do that?!! Why do they do that?! Hypotheses on function of visual neurons! Retina LGN: local contrast detectors!
143 Physiology of low-level visual areas! Three fundamental questions:! What do visual neurons do?! How do they do that?!! Why do they do that?! Hypotheses on function of visual neurons! Retina LGN: local contrast detectors!
144 Physiology of low-level visual areas! Three fundamental questions:! What do visual neurons do?! How do they do that?!! Why do they do that?! Hypotheses on function of visual neurons! Retina LGN: local contrast detectors!
145 Physiology of low-level visual areas! Three fundamental questions:! What do visual neurons do?! How do they do that?!! Why do they do that?! Hypotheses on function of visual neurons! Retina LGN: do not respond to uniform background!
146 Physiology of low-level visual areas! Three fundamental questions:! What do visual neurons do?! How do they do that?!! Why do they do that?! Hypotheses on function of visual neurons! Retina LGN: do respond to local contrast changes!
147 Physiology of low-level visual areas! Three fundamental questions:! What do visual neurons do?! How do they do that?!! Why do they do that?! Hypotheses on function of visual neurons! V1 simple cells: edge detectors!
148 Physiology of low-level visual areas! Three fundamental questions:! What do visual neurons do?! How do they do that?!! Why do they do that?! Hypotheses on function of visual neurons! V1 simple cells: edge detectors!
149 Physiology of low-level visual areas! Three fundamental questions:! What do visual neurons do?! How do they do that?!! Why do they do that?! Hypotheses on function of visual neurons! V1 simple cells: do not respond to uniform background!
150 Physiology of low-level visual areas! Three fundamental questions:! What do visual neurons do?! How do they do that?!! Why do they do that?! Hypotheses on function of visual neurons! V1 simple cells: do not respond to not elongated contrast changes!
151 Physiology of low-level visual areas! Three fundamental questions:! What do visual neurons do?! How do they do that?!! Why do they do that?! Hypotheses on function of visual neurons! V1 simple cells: do respond to elongated contrast changes (edges)!
152 Physiology of low-level visual areas! Three fundamental questions:! What do visual neurons do?! How do they do that?!! Why do they do that?! Hypotheses on function of visual neurons! Image segmentation"
153 Physiology of low-level visual areas! Three fundamental questions:! What do visual neurons do?! How do they do that?!! Why do they do that?! Hypotheses on function of visual neurons! Image segmentation"
154 Layout of the lecture! 1. Introduction to anatomy and physiology of the visual system! A systems/computational approach to the study of the visual system! Anatomy of the visual system! Classic findings about physiology of lowerlevel visual areas" Data analysis approaches in Systems Neuroscience"
155 Beyond the qualitative description of receptive fields "!Two key tools for quantifying neuronal responses:! 1. The peristimulus time histogram (PSTH)! 2. The tuning curve" But before a key notion:! The variability of neuronal responses"
156 The variability of neuronal responses! Stimulus presentation! 500 ms! Time! Spike trains are not reproducible! 1 s!
157 The variability of neuronal responses! Neuronal responses are not reproducible: they slightly vary from presentation to presentation! One single presentation is not sufficient to quantify the neuronal response! The presentation of a given stimulus must be repeated many times! This gives the neuronal average firing rate as a function of time: AFR(t)" AFR(t) is visualized through a peristimulus time histogram: PSTH"
158 The peristimulus time histogram (PSTH)!
159 The peristimulus time histogram (PSTH)!
160 The peristimulus time histogram (PSTH)!
161 The peristimulus time histogram (PSTH)!
162 The peristimulus time histogram (PSTH)!
163 The peristimulus time histogram (PSTH)!
164 PSTHs in earlier visual areas! Kara et al. 2000!
165 PSTHs in earlier visual areas! Kara et al. 2000!
166 PSTHs in earlier visual areas! Kara et al. 2000!
167 PSTHs are the building blocks of tuning curves"
168 PSTHs are the building blocks of tuning curves" Schmolesky et al. 2000!
169 PSTHs are the building blocks of tuning curves" Schmolesky et al. 2000!
170 Tuning curves as a measure of neuronal selectivity" Orientation selective V1 cell! Nonselective V1 cell!
171 Layout of the lecture! 1. Introduction to anatomy and physiology of the visual system! A systems/computational approach to the study of the visual system! Anatomy of the visual system! Classic findings about physiology of lower-level visual areas! Classic findings about physiology of higherlevel visual areas"
172 Physiology of low-level visual areas! Retina!
173 Physiology of higher-level visual areas! Retina!
174 The dorsal and ventral visual pathways" Streams of Processing Ungerleider & Mishkin Parietal Pathway "Where" Spatial relationships Motion V1 LIP 7a VIP DP MST MT V4 V2 V4 PIT VP TEO Temporal Pathway CIT AIT "What" Pattern Identity
175 The dorsal and ventral visual pathways" Parallel Pathways in Visual Cortex Parietal Pathway Temporal Pathway AIT 7a CIT VIP MST LIP PIT Parietal Pathway MT V4 V1 7a DP V4 V2 LIP VIP MST MT V2 V2 V1 V1 V4 PIT VP TEO CIT Temporal Pathway AIT Ventral-temporal pathway"! Visual object recognition"
176 The dorsal and ventral visual pathways" Hierarchical elaboration of response selectivities Parietal pathway Temporal pathway AIT 7a CIT VIP MST LIP PIT MT V4 V2 V2 V1 V1
177 Adapted from John Maunsell
178 Higher visual areas: ventral visual stream"!basic understanding achievable using PSTHs and tuning curves" Retina!
179 Shape tuning in higher visual areas!!!!!( some example)! neuron in area V4! PSTH: Post Stimulus Time Histogram! Kobatake e Tanake (1994)!
180 Shape tuning in higher visual areas!!!!!( some example)! neuron in area V4! Kobatake e Tanake (1994)! respond to elongated shapes!
181 Shape tuning in higher visual areas!!!!!( some example)! neuron in area V4! it looks like a V1 simple cell! Kobatake e Tanake (1994)!
182 Shape tuning in higher visual areas!!!!!( some example)! neuron in area V4! Kobatake e Tanake (1994)! respond to the shape of a hand!?!
183 Shape tuning in higher visual areas!!!!!( some example)! neuron in area V4! Kobatake e Tanake (1994)! actually it respond also to much simpler shapes!
184 Shape tuning in higher visual areas!!!!!( some example)! neuron in area V4! actually it respond also to much simpler shapes! but how much simpler?! Kobatake e Tanake (1994)!
185 Shape tuning in higher visual areas!!!!!( some example)! Kobatake e Tanake (1994)! neuron in area V4:! the tuning is more complex than for a V1 simple cell!
186 Shape tuning in higher visual areas!!!!!( some example)! neuron in anterior IT! Kobatake e Tanake (1994)! it responds to a pearlike shape!?!
187 Shape tuning in higher visual areas!!!!!( some example)! neuron in anterior IT! but it seems to respond also to simpler shapes! (e.g., just parts of the pear )! Kobatake e Tanake (1994)!
188 Shape tuning in higher visual areas!!!!!( some example)! Kobatake e Tanake (1994)! neuron in anterior IT! it does NOT respond to simple oriented bars!
189 Shape tuning in higher visual areas!!!!!( some example)! neuron in anterior IT! Kobatake e Tanake (1994)! its tuning is definitely more complex than for a V1 simple cell!
190 Shape tuning in higher visual areas!!!!!( some example)! neuron in anterior IT! Kobatake e Tanake (1994)! its tuning is definitely more complex than for a V1 simple cell!
191 Face representation in IT!!!!!( some example)! Kobatake e Tanake (1994)! do IT contain a neuronal subpopulation that represent faces?"
192 Desimone et al (1984)! Face representation in IT!!!!!( some example)!
193 Freiwald et al (2009)! Face representation in IT!!!!!( some example)!
194 Shape tuning in higher visual areas! (Tanakaʼs reduction method)! Tanaka et al!
195 Shape tuning in higher visual areas! (Tanakaʼs reduction method)! Tanaka et al! IT neurons respond to relatively complex combinations of visual features!
196 The complexity of the shapes that are effective in activating visual neurons increase along the ventral visual stream" Kobatake e Tanake (1994)!
197 The receptive field size of visual neurons increases along the ventral visual stream " Kobatake e Tanake (1994)!
198 Physiology of ventral visual areas! Three fundamental questions:! What do visual neurons do?!!!!!(what they respond to?)! How do they do that?!!!!(what make them respond that way?)! Why do they do that?! (what function are such responses underlying?)!
199 What do visual neurons do?! 1) diventano sempre piuʼ selettivi! 2) diventano sempre piuʼ invarianti" AIT" V1 tuning properties" PIT" Adapted from Rousselet et al. (2004)! Adapted from Kandel, Schwartz and Jessell!
200 What do visual neurons do?! 1) diventano sempre piuʼ selettivi! 2) diventano sempre piuʼ invarianti" AIT tuning properties" AIT" PIT" Desimone et al. (1984)! Adapted from Rousselet et al. (2004)! Kobatake et al. (1994)!
201 What do visual neurons do?! 1) Become more selective! 2) diventano sempre piuʼ invarianti" High" AIT" PIT" Shape selectivity" Adapted from Rousselet et al. (2004)! Low"
202 What do visual neurons do?! 1) Become more selectivediventano sempre piuʼ invarianti" High" AIT" PIT" Invariance" Low" Adapted from Rousselet et al. (2004)!
203 What do visual neurons do?! 1) Become more selective! 2) Become more invariant" High" AIT" PIT" Shape selectivity" Invariance" Adapted from Rousselet et al. (2004)! Low"
204 How do they do it?!(what mechanisms?)! 1) diventano sempre piuʼ selettivi! 2) diventano sempre piuʼ invarianti" 1) Neurons with simpler tuning may be combined to obtain neurons with more complex tuning! 2) Neurons with lower invariance may be combined to obtain neurons with higher invariance! simple cells! LGN! V1 (simple cells)! complex cell!
205 Why do they do it?!(what function?)! To achieve an invariant representation of visual objects! 1) diventano sempre piuʼ invarianti" V1" IT" Adapted from Rousselet et al. (2004)!
206 Visual object recognition" For primates:! Fast! Accurate! Robust! Effortless! Essential for survival!
207 Why is object recognition hard?! Target" This is not my target!" This is still my target! (just transformed)"
208 Requirements for a recognition system:! 1) Must be sensitive to changes in object identity! Target" No Target!" SELECTIVITY" even relatively SMALL changes" 2) Must be insensitive to identity-preserving image changes! Target" Still the Target!" INVARIANCE" even very BIG changes"
209 What do visual neurons do?! 1) Become more selective" 2) Become more invariant" High" AIT" PIT" Shape selectivity" Invariance" Adapted from Rousselet et al. (2004)! Low"
210 Requirements for a recognition system:! 1) Must be sensitive to changes in object identity! Target" No Target!" SELECTIVITY" even relatively SMALL changes" 2) Must be insensitive to identity-preserving image changes! Target" Still the Target!" INVARIANCE" even very BIG changes"
211 Why do they do it?!(what function?)!!to achieve an invariant (explicit) representation of visual objects! 1) diventano sempre piuʼ invarianti" V1" IT" Adapted from Rousselet et al. (2004)!
212 How much selectivity and invariance can be achieved?! Recordings from the human middle temporal lobe of epileptic (MTL) subjects!
213 How much selectivity and invariance can be achieved?! Jennifer Aniston" Quiroga et al (2005)!
214 How much selectivity and invariance can be achieved?! Jennifer Aniston" with Brad Pitt" Quiroga et al (2005)!
215 How much selectivity and invariance can be achieved?! Jennifer Aniston" with Brad Pitt" other famous people" Quiroga et al (2005)!
216 How much selectivity and invariance can be achieved?! Jennifer Aniston" with Brad Pitt" buildings" Quiroga et al (2005)!
The Visual Cortex 0 http://www.tutis.ca/neuromd/index.htm 20 February 2013
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