Emergence of transformation tolerant representations of visual objects in rat lateral extrastriate cortex. Davide Zoccolan
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1 Emergence of transformation tolerant representations of visual objects in rat lateral extrastriate cortex Davide Zoccolan
2 Layout of the talk 1. Background Why vision? Why rats (rodents)?
3 Why to study vision? 1. Because it s an extraordinary complex cognitive function Sensation Perception
4
5 Il riconoscimento visivo Identificare/classificare gli oggetti in una scena
6 Il riconoscimento visivo Identificare/classificare gli oggetti in una scena
7 Il riconoscimento visivo Identificare/classificare gli oggetti in una scena
8 Il riconoscimento visivo Identificare/classificare gli oggetti in una scena
9 Il riconoscimento visivo Identificare/classificare gli oggetti in una scena pixels = photoreceptors
10 Il riconoscimento visivo Identificare/classificare gli oggetti in una scena bananas
11 Il riconoscimento visivo Identificare/classificare gli oggetti in una scena clothes
12 Il riconoscimento visivo Identificare/classificare gli oggetti in una scena face
13 faces
14 Why to study vision? 1. Because it s an extraordinary complex cognitive function Sensation Perception
15 Why to study vision? 1. Because it s an extraordinary complex cognitive function Sensation Perception 2. Because the underlying cortical machinery is extraordinary complex
16 Why to study vision?
17 Why to study vision? DiCarlo, Zoccolan and Rust (2012)
18 Why to study vision? 1. Because it s an extraordinary complex cognitive function Sensation Perception 2. Because the underlying cortical machinery is extraordinary complex Understanding vision: understanding the brain achieving artificial intelligence
19 Layout of the talk 1. Background Why vision? Why rats (rodents)?
20 Issue with monkey studies
21 Issue with monkey studies the complexity of the primate visual system compared to the limited experimental approaches
22 Is there any more manageable model system of visual functions? 1. Simpler visual system lower number of visual areas Less intricate feedforward and feedback circuitry 1. More experimentally accessible high-throughput behavioral testing and large-scale recordings molecular and genetic manipulation In-vivo 2-photon imaging, optogenetics, etc. Rodents 2. Still embodying the core computations underlying visual object recognition and shape processing
23 Are rodents (mice and rats) suitable models to study higher-level vision? largely neglected by vision scientists (until recently)
24 Can rats/mice see objects? E.g., rats have a much poorer spatial resolution (~1cycle/deg) compared to human fovea (~60 cycles/deg) Human Rat (Fisher-Norway) Rat (Long-Evans) Rat (Sprague-Dawley) Prusky, Harker, Douglas and Whishaw (2002)
25 Since 2007 rodents are emerging as novel models of visual functions Niell and Stryker (Neuron) Adesnik et al (Nature) Wang and Burkhalter (J Comp Neurol) Niell and Stryker (J Neurosci) Marshel et al (Neuron) Anderman et al (Neuron) Bonin et al (J Neurosci) Wang et al (J Neurosci) Busse et al (J Neurosci) Histed et al (J Neurophys) Ayaz et al (Curr Biol) Scholl et al (J Neurosci) Zhao et al (J Neurosci) Bennet et al (Neuron) Saleem et al (Nat Neurosci) Mouse Physiology/imaging of lower-level visual areas Rat Psychophysics of visual perception/recognition Zoccolan et al (PNAS) Meyer et al (J Vision) Greenberg et al (Nat Neurosci) Petruno et al (Plos One) Reinagel (Plos One) Alemi et al (J Neurosci) Hengen et al (Neuron) Wallace et al (Nature) Tafazoli et al (J Neurosci) Vermaercke and Op de Beeck (Curr Biol)
26 Layout of the talk 1. Background Why vision? Why rats (rodents)? 1. What is rat vision capable of? Behavior Neurophysiology
27 Layout of the talk 1. Background Why vision? Why rats (rodents)? 1. What is rat vision capable of? Behavior Zoccolan, Oertelt, DiCarlo, Cox (2009) Tafazoli, Di Filippo, Zoccolan (2012) Alemi-Neissi, Rosselli, Zoccolan (2013) Rosselli, Alemi-Neissi, Ansuini, Zoccolan (2015) Djurdjevic, Bertolini, Ansuini, Zoccolan (in preparation) Neurophysiology
28 Layout of the talk 1. Background Why vision? Why rats (rodents)? 1. What is rat vision capable of? Behavior Zoccolan, Oertelt, DiCarlo, Cox (2009) Tafazoli, Di Filippo, Zoccolan (2012) Alemi-Neissi, Rosselli, Zoccolan (2013) Rosselli, Alemi-Neissi, Ansuini, Zoccolan (2015) Djurdjevic, Bertolini, Ansuini, Zoccolan (in preparation) Neurophysiology Tafazoli, Safaai, De Franceschi, Rosselli, Riggi, Buffolo, Panzeri, Zoccolan (2017)
29 Summary of the behavioral studies 1. Rats are capable of invariant visual object recognition (Zoccolan, Oertelt, DiCarlo, Cox, 2009) Frontal views (0º in-depth rotation) Default size (40º of visual angle) Default position (center of the screen)
30 Summary of the behavioral studies 1. Rats are capable of invariant visual object recognition (Zoccolan, Oertelt, DiCarlo, Cox, 2009)
31 Summary of the behavioral studies 1. Rats are capable of invariant visual object recognition (Zoccolan, Oertelt, DiCarlo, Cox, 2009)
32 Summary of the behavioral studies 1. Rats are capable of invariant visual object recognition (Zoccolan, Oertelt, DiCarlo, Cox, 2009)
33 Summary of the behavioral studies 1. Rats are capable of invariant visual object recognition (Zoccolan, Oertelt, DiCarlo, Cox, 2009) Default views 108 size-rotation combinations Variation axes
34 The rat in action
35 Summary of the behavioral studies 1. Rats are capable of invariant visual object recognition (Zoccolan, Oertelt, DiCarlo, Cox, 2009)
36 Summary of the behavioral studies 1. Rats are capable of invariant visual object recognition (Zoccolan, Oertelt, DiCarlo, Cox, 2009)
37 Summary of the behavioral studies 1. Rats are capable of invariant visual object recognition (Zoccolan, Oertelt, DiCarlo, Cox, 2009) 1. Rats spontaneously generalize their recognition to novel object views (Tafazoli, Di Filippo, Zoccolan, 2012) Default view perceptually similar? Visual priming paradigm 16 novel views
38 Summary of the behavioral studies 1. Rats are capable of invariant visual object recognition (Zoccolan, Oertelt, DiCarlo, Cox, 2009) 1. Rats spontaneously generalize their recognition to novel object views (Tafazoli, Di Filippo, Zoccolan, 2012) 2. Rats rely on an invariant, shape-based, multifeatural processing strategy of visual objects (Alemi-Neissi, Rosselli, Zoccolan, 2013)
39 Summary of the behavioral studies 1. Rats are capable of invariant visual object recognition (Zoccolan, Oertelt, DiCarlo, Cox, 2009) 1. Rats spontaneously generalize their recognition to novel object views (Tafazoli, Di Filippo, Zoccolan, 2012) 2. Rats rely on an invariant, shape-based, multifeatural processing strategy of visual objects (Alemi-Neissi, Rosselli, Zoccolan, 2013) Rats good models to study the neuronal basis of visual object recognition and shape processing
40 Layout of the talk 1. Background Why vision? Why rats (rodents)? 1. What is rat vision capable of? Behavior Neurophysiology
41 Does an homologous of the primate ventral stream exist in the rat brain? Monkey brain Rat brain
42 The primate ventral stream: an object processing pathway AIT PIT V1 Adapted from Rousselet et al. (2004)
43 The primate ventral stream: an object processing pathway 1 Tuning complexity AIT V1 tuning properties PIT Adapted from Rousselet et al. (2004) V1 Adapted from Kandel, Schwartz and Jessell
44 The primate ventral stream: an object processing pathway 1 Tuning complexity AIT tuning properties AIT Desimone et al. (1984) PIT V1 Adapted from Rousselet et al. (2004) Kobatake et al. (1994)
45 The primate ventral stream: an object processing pathway 1 2 Tuning complexity Latency AIT ms PIT V ms Adapted from Rousselet et al. (2004)
46 The primate ventral stream: an object processing pathway 1 Tuning complexity 2 Latency 3 RF size AIT ms 2.5º-70º PIT V ms 0.5º-1.5º Adapted from Rousselet et al. (2004)
47 The primate ventral stream: builds explicit representations of visual objects V1 representation AIT representation r 1 r 1 car r 2 r 2 r N r 4 r 3 r N r 4 r 3
48 Does an homologous of the primate ventral stream exist in the rat brain? Monkey brain Rat brain
49 Hierarchical organization of rat visual areas Anterior Adapted from Sereno & Allaman (1991) Medial Lateral AM M A Posterior P1/2 RL AL LI LL LM V1 Adapted from Coogan & Burkhalter (1993)
50 Hierarchical organization of rat visual areas the object processing pathway Anterior Adapted from Sereno & Allaman (1991) Medial Lateral AM M A Posterior P1/2 RL AL LI LL LM V1 Adapted from Coogan & Burkhalter (1993)
51 Experimental design 1. multi-electrode recordings in anesthetized rats Insertion track
52 Experimental design 2. stimulus set and presentation
53 Experimental design 2. stimulus set and presentation
54 Experimental design 2. stimulus set and presentation RSVP: Rapid Sequence Visual Presentation
55 Experimental design 3. receptive fields mapping RF size Spikes/s 5 10º long moving bars presented over a 6x11 grid
56 Results: basic properties 1. receptive field size increases from V1 to LL
57 V1 Results: basic properties 1. receptive field size increases from V1 to LL
58 Results: basic properties 1. receptive field size increases from V1 to LL V1 LM
59 Results: basic properties 1. receptive field size increases from V1 to LL V1 LM LI
60 Results: basic properties 1. receptive field size increases from V1 to LL V1 LM LI LL
61 Results: basic properties 1. receptive field size increases from V1 to LL RF size
62 Results: basic properties 1. receptive field size increases from V1 to LL RF size
63 Results: basic properties 1. receptive field size increases from V1 to LL 3. latency increases from V1 to LL RF size Latency
64 Results: basic properties 1. receptive field size increases from V1 to LL 2. latency increases from V1 to LL Processing hierarchy AM M A P1/2 RL AL LI LL LM V1
65 Results: basic properties 1. receptive field size increases from V1 to LL 2. latency increases from V1 to LL The monkey ventral stream Latency RF size AIT ms 2.5º-70º Processing hierarchy AM M A PIT P1/2 RL AL LI LL LM V1 V ms 0.5º-1.5º Adapted from Rousselet et al. (2004)
66 Results: basic properties 1. receptive field size increases from V1 to LL 2. latency increases from V1 to LL Tuning complexity Latency The monkey ventral stream RF size AIT ms 2.5º-70º PIT V ms 0.5º-1.5º Adapted from Rousselet et al. (2004)
67 Shape processing & invariant recognition V1 representation? Low-level representation LL representation? High-level representation r 1 r 1 car r 2 r 2 r N r 4 r 3 r N r 4 r 3
68 The approach: 1. mutual information between stimulus and response 2. decoding stimulus identity using machine learning Quiroga & Panzeri (2009)
69 Examples of neuronal tuning very effective object poorly effective object V1 neuron
70 Examples of neuronal tuning V1 neuron LL neuron
71 object identity object identity Examples of neuronal tuning Two sources of modulation of neuronal firing rate: 1. variation in object transformation T 2. variation in object identity O object transformation object transformation V1 neuron LL neuron
72 Probability First question: total information per neuron across visual areas How much information do neurons carry about the identity of all our object views? all object views treated as independent stimuli stimulus: vs vs vs vs response: Response (firing rate)
73 Probability First question: total information per neuron across visual areas How much information do neurons carry about the identity of all our object views? all object views treated as independent stimuli stimulus: vs vs vs vs response: Response (firing rate)
74 First question: total information per neuron across visual areas How much information do neurons carry about the identity of all our object views? Approach: Compute mutual information between stimulus and response: S = stimulus = O & T
75 Mutual information: results 1. stimulus information across visual areas
76 Mutual information: results 1. stimulus information across visual areas
77 Mutual information: conclusions 1. Mutual information between neuronal response and individual stimulus conditions decreases from V1 to LL
78 Mutual information: conclusions 1. Mutual information between neuronal response and individual stimulus conditions decreases from V1 to LL consistent with the existence of a processing hierarchy AM M A P1/2 RL AL LI LL LM V1
79 Second question: what kind of information do neurons code? lowest-level visual attributes overall luminance/contrast low luminance high luminance oriented edges & corners luminance patches curved boundaries higher-order features
80 Variation of luminous energy 1. neuronal sensitivity to object transformations 2. neuronal selectivity for object identity sensitivity to transformation could explain: object selectivity V1 neuron LL neuron
81 Effective RF luminance: the luminous energy impinged by a stimulus on a neuronal RF dot product Effective RF luminance
82 Dependence of neuronal firing on RF luminance sensitivity to transformation object selectivity V1 neuron
83 Dependence of neuronal firing on RF luminance bin #1 bin #2 bin #3 bin #4 bin #N-1 bin #N S7 S 8 S 9 S 1 S 2 S 3 S 230 S 229 Effective RF luminance Take every stimulus S i (10 objects x 23 transformations) and: 1. Compute the corresponding RF luminance value 2. Bin the resulting luminance axis in N equipopulated bins V1 neuron LL neuron
84 Dependence of neuronal firing on RF luminance bin #1 bin #2 bin #3 bin #4 bin #N-1 bin #N S7 S 8 S 9 S 1 S 2 S 3 S 230 S 229 Effective RF luminance V1 neuron
85 Dependence of neuronal firing on RF luminance LL neuron V1 neuron
86 Dependence of neuronal firing on RF luminance LL neuron V1 neuron
87 Second question: what kind of information do neurons code? What fraction of information do neurons carry about the overall luminance/contrast of visual objects? What fraction of information do neurons carry about higher-order features? higher-order = everything not accounted by luminance alone
88 Second question: what kind of information do neurons code? What fraction of information do neurons carry about the overall luminance/contrast of visual objects? What fraction of information do neurons carry about higher-order features? RF L = RF luminance S = object identity defined by higher-order features S = stimulus = S & L I(R; S) = I(R; S &L) = I(R; S L) + I(R; L)
89 Second question: what kind of information do neurons code? What fraction of information do neurons carry about the overall luminance/contrast of visual objects? What fraction of information do neurons carry about higher-order features? RF L = RF luminance S = object identity defined by higher-order features S = stimulus = S & L I(R; S) = I(R; S &L) = I(R; S L) + I(R; L) higher-order information luminance information
90 Mutual information: results 2. fraction of higher-order information I(R; S &L) = I(R; S L) + I(R; L) I(R; S &L) total information
91 Mutual information: results 2. fraction of higher-order information I(R; S &L) = I(R; S L) + I(R; L) I(R; L) luminance information I(R; S L) higher-order information
92 Mutual information: results 2. fraction of higher-order information Fraction of information about object higherorder features: f high = I(R;S' L) I(R;S'& L)
93 Mutual information: conclusions 1. Mutual information between neuronal response and individual stimulus conditions decreases from V1 to LL consistent with the existence of a processing hierarchy 2. The fraction of information carried by neurons about higher-order features increases from V1 to LL consistent with formation of higher-order representations
94 Third question: how view-invariant is object information? face (many views) rat (many views) Take a pair of objects: O = {o i,o j } Take multiple transformations per object: T = {t 1,, t 23 } Define each stimulus (object view) as: S = O & T Decompose the total stimulus information as: I(R; S) = I(R; O&T) = I(R; T O) + I(R; O) total information
95 Third question: how view-invariant is object information? face (many views) rat (many views) Take a pair of objects: O = {o i,o j } Take multiple transformations per object: T = {t 1,, t 23 } Define each stimulus (object view) as: S = O & T Decompose the total stimulus information as: I(R; S) = I(R; O&T) = I(R; T O) + I(R; O) total information view-specific information view-invariant information
96 Third question: how view-invariant is object information? I(R; S) = I(R; O&T) = I(R; T O) + I(R; O) total information view-invariant information
97 Mutual information: results 3. view-invariant object information view-invariant information fraction of viewinvariant information
98 Mutual information: results 3. view-invariant object information view-invariant information fraction of viewinvariant information
99 Mutual information: conclusions 1. Mutual information between neuronal response and individual stimulus conditions decreases from V1 to LL 2. The fraction of information carried by neurons about higher-order features increases from V1 to LL 3. The view-invariant object information per neuron increases from V1 to LL consistent with formation of invariant representations
100 The approach: 1. mutual information between stimulus and response 2. decoding stimulus identity using machine learning Quiroga & Panzeri (2009)
101 The approach: 1. mutual information between stimulus and response 2. decoding stimulus identity using machine learning Quiroga & Panzeri (2009) mutual information upper bound to how well we can reconstruct a stimulus property (e.g., object luminance or identity) from observing a neuronal response
102 The approach: 1. mutual information between stimulus and response 2. decoding stimulus identity using machine learning decoding how easily a stimulus property (e.g., object identity) can be read out from the neuronal response? Quiroga & Panzeri (2009) mutual information upper bound to how well we can reconstruct a stimulus property (e.g., object luminance or identity) from observing a neuronal response
103 Forth question: how easily readable is object identity?
104 Forth question: how easily readable is object identity?
105 Forth question: how easily readable is object identity? face (many views) rat (many views) Take a pair of objects Take multiple views of each object Key question:
106 Forth question: how easily readable is object identity? face (many views) rat (many views) Vs. Take a pair of objects Take multiple views of each object Key question: If: a decoder learns to discriminate two views of these objects then: does it generalize to any other pair of views? Goodness of a representation to support invariant recognition
107 Good vs. bad representations: how easily readable is object identity?
108 Probability Good vs. bad representations: how easily readable is object identity? Vs. Spike count
109 Probability Good vs. bad representations: how easily readable is object identity? Vs. decision boundary Spike count
110 Probability Good vs. bad representations: how easily readable is object identity? Vs. decision boundary Bad representation Spike count
111 Probability Good vs. bad representations: how easily readable is object identity? Vs. Spike count
112 Probability Good vs. bad representations: how easily readable is object identity? Vs. decision boundary Spike count
113 Probability Good vs. bad representations: how easily readable is object identity? Vs. Good representation decision boundary Spike count
114 Probability Probability Method: measure generalization of recognition performance Train the decoder Test the decoder decision boundary Test decision boundary Spike count Spike count
115 Single-cell decoding: results generalization to novel object views
116 Single-cell decoding: results generalization to novel object views
117 Single-cell decoding: results generalization to novel object views
118 Single-cell decoding: results generalization to novel object views
119 Single-cell decoding: results generalization to novel object views
120 Decoding: conclusions 1. Coding of object identity becomes more invariant to transformation from V1 to LL consistent with formation of invariant representations
121 Last question: invariant recognition at the neuronal population level Discrimination performance (bits) Discrimination performance (% correct) Can we observe macroscopic differences at the neuronal population level?
122 Last question: invariant recognition at the neuronal population level
123 Last question: invariant recognition at the neuronal population level
124 Last question: invariant recognition at the neuronal population level
125 Last question: invariant recognition at the neuronal population level
126 Last question: invariant recognition at the neuronal population level
127 Last question: invariant recognition at the neuronal population level
128 Last question: invariant recognition at the neuronal population level
129 Last question: invariant recognition at the neuronal population level
130 Last question: invariant recognition at the neuronal population level
131 Decoding: conclusions 1. Coding of object identity becomes more invariant to transformation from V1 to LL At both the single-cell and neuronal population level Consistent with formation of invariant representations
132 Perceived similarity (priming magnitude) Rats are interesting models of object vision Behavior Neurophysiology
133 Acknowledgements Sina Tafazoli Alessandro Di Filippo Alireza Alemi-Neissi Federica Rosselli Gioa De Franceschi Margherita Riggi Federica Buffolo David Cox Nadja Oertelt James DiCarlo Bold font = first authors or co-authors Houman Safaai Stefano Panzeri
134 Acknowledgements
135 Experimental design 4. identification of visual cortical areas Base: ch. 32 recording area (32 sites) Tip: ch. 1
136 Experimental design 4. identification of visual cortical areas Nasal RF maps Temporal Base: ch. 32 recording area (32 sites) RF centers Tip: ch. 1
137 Experimental design 4. identification of visual cortical areas Nasal RF maps Temporal Base: ch. 32 recording area (32 sites) RF centers Tip: ch. 1
138 Experimental design 4. identification of visual cortical areas Nasal RF maps Temporal Base: ch. 32 recording area (32 sites) RF centers Tip: ch. 1
139 Experimental design 4. identification of visual cortical areas Nasal RF maps Temporal Base: ch. 32 recording area (32 sites) RF centers Tip: ch. 1
140 Experimental design 4. identification of visual cortical areas Nasal RF maps Temporal Base: ch. 32 recording area (32 sites) RF centers Tip: ch. 1
141 The confound of luminance tuning object selectivity V1 neuron LL neuron LL neuron
142 The confound of luminance tuning V1 neuron LL neuron
143 The confound of luminance tuning V1 neuron LL neuron
144 The confound of luminance tuning V1 neuron LL neuron
145 The confound of luminance tuning V1 neuron LL neuron
146 The confound of luminance tuning apparent invariance real invariance V1 neuron LL neuron
147 Decoding: results (after matching RF luminance)
148 Decoding: results (after matching RF luminance)
149 Decoding: results (after matching RF luminance)
150 Decoding: results (after matching RF luminance) LI & LM LL & V1
151 Decoding: results (after matching RF luminance) LI LM LL V1
152 Decoding: results (after matching RF luminance) LI LM LL V1
153 Decoding: results (after matching RF luminance) LL LI LM V1
154 Decoding: results (after matching RF luminance) LL LI LM V1
155 Decoding: conclusions 1. Coding of object identity becomes more invariant to transformation from V1 to LL consistent with formation of invariant representations
156 Decoding: results 2. superficial vs. deep cortical layers
157 Decoding: results 2. superficial vs. deep cortical layers recordings from deep layers
158 Decoding: results 2. superficial vs. deep cortical layers recordings from superficial layers recordings from deep layers
159 Decoding: results 2. superficial vs. deep cortical layers
160 Decoding: results 3. individual variation axes
161 Decoding: results 3. individual variation axes
162 Decoding: results 3. individual variation axes
163 Decoding: results 4. parametric position changes
164 Decoding: conclusions 1. Mutual information between neuronal response and individual stimulus conditions decreases from V1 to LL 2. The fraction of information carried by neurons about shape attributes increases from V1 to LL 3. Coding of object identity becomes more invariant to transformation from V1 to LL consistent with formation of invariant representations
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