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感覚情報の階層的処理機構

先進理工学専攻
樫森 与志喜感覚受容と脳内の機能的地図1

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視覚経路

大脳皮質の神経細胞

大脳皮質は6層構造を取り、ざまざまな形態の神経細胞で構成されている。

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Models of a single neuron
Network models

Highly nonlinear system
Hodgkin‐Huxley model

力学系の解の振る舞い
Limit cycle
Point attractor

Hodgkin‐HuxleyEquation

Quasi‐periodic

Chaotic attractor

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注目すべきこと
全体の秩序（巨視的）異なるスケールの階層構造
空間的スケール
時間的スケール
動的秩序の創発システムとしての機能

局所的な相互作用研究に関連するキーワード• 自己組織化・自己組織臨界現象・動的秩序
創発、相転移• シンクロナイゼーション・アトラクタ・カオス・非線形効果・引き込み42015/5/15

研究テーマ• 脳における情報処理機構の解明• 生物システムの動的秩序創発のメカニズム
細胞集団（免疫系の自己・非自己）、魚群の行動、人間社会の付き合い行動 など

脳の情報処理機構• ボトムアップ的アプローチ
たんぱく質や遺伝子、細胞などの構成要素について現実的でミクロスコピックなモデルを作り、それを用いて、簡単なシステムを構築し、そのシステムモデルを用いてさらに上位のシステムを構築していくという、下からの積み上げ方式である。

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ボトムアップ的アプローチ

Electrolocation6

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Amplitude and phase coding

Detection ability of phasedifference(Hyperacuity; Y. Kashimori et al., Biol Cybern, 2001)

The neural pathway of electrosensory processing

Phase coding(~ submilisecond)
Neural coding of object features(distance, size, shape)
Encoding of EOD AM and PM

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Electric image
Calculation of electric image
by finite‐element method
fish

Water tank
(Fujita and Kashimori, Biol Cybern ,2010)

Electric image

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Burst coding of object features
Neuron model
Soma

Dendrite
WhereObject(Fujita et al., Biol Cybern, 2007)

Distance and size

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The role of feedback in burst coding

Echolocation10

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The brain and the auditory cortex of the mustachedbat(Suga and Xiao 2001)

Neural pathway for detecting soundfeatures of a flying insect112015/5/15

Egocentric selection

Network model(Fujita and Kashimori, Neural Proc Lett,
2015)12

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Equations of the network model

Centrifugal and centripetal BF shifts

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Future works• Electrolocatin: electric image, neural coding oftarget distance and its size, hyperacuity of phase
information,→ shape ?
• Echolocation: neural coding of target distance,neural mechanism of corticofugal modulation oftuning property→ detection of a flying insect ?
• Sound localization: information processing of IIDand ITD, binding mechanism→ head direction, attention ?

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脳の情報処理機構• トップダウン的アプローチ
構成要素のモデルはできる簡単にして、システム全体の状態や機能についてどのようなものが生じてくるのかを調べる。システム全体の働きをある程度理解してから、サブシステムさらには構成要素をより現実的なものに近づけていく方式である。

トップダウン的アプローチ
Dynamic processing of sensory information(Attractor models)• Olfactory systemodor information processing in olfactory bulb and piriform cortex• Visual system
categorization, face perception, top‐down influence• Auditory systemword perception, information processing in A1• Gustatory system
Interaction between taste and odor information• Somatosensory system (tactile, haptic)

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Dynamical attractor model
記憶の階層的表現
A1
A2

A3

B1
B2

短期シナプス変化

Input AOFF
B3

リミットサイクル
アトラクタ

ポイントアトラクタ

Network model

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Hierarchical processing in memory storage
and recollection

Working memory in pair association task
Which is the pairof the pattern 1?

time(Sakai and Miyashita, 1991)

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Working memory(PFC)
Learning

Inputs
Input B2 B1+D1

Prediction

Dynamic coding of odor information18

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Dynamic coding of odor informationOBepitherium
EOG

epitherium
VSD

Neural model of olfactory
systemW. Freeman (1987)
Spike coding of stimulus
intensity
J.J. Hopfield (1995)

EEG

Neural pathway of olfactory processing

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Spike coding of stimulus intensity

Oscillationtime(Hopfield, 1995)

Neural network model of olfactory
bulb

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Coding of odor information inolfactory bulb

Representation of odor information by
dynamical attractors

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Neural network model of olfactorycortexHierarchical discrimination of odor
components

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Neural mechanism of visual
categorization

Visual categorization（Sigala & Logothetis, 2000)（Freedman et al., 2001)

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Neural mechanism for visual
categorization
ModelAttractors

V4(Soga and Kashimori, Vision Res, 2008)

Equations of PP neurons

Equations of ITC neurons

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Network model of PFC

Responses of ITC and PFC neurons

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Response preference of ITC
neurons for face features
PFC

ITC

Response of the PFC network and of an ITC
neuron induced by lesion of PP

Neural mechanism of dynamic responses ofneurons in inferior temporal cortex in faceperception

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Hierarchical processing of face information ininferior temporal cortex

Highly correlated responses of IT neurons toface object(Hiyabayashi and Miyashita, 2006)

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Temporal properties of spike correlations andfiring rates(Hiyabayashi and Miyashita, 2006)

Spike coding of global and fine featuresof face images(Sugase et al., 1999)

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Network model of hierarchical processingof face information
Ventral visual pathway

a whole image

outline arrangementparts(Yamada, and Kashimori,Cogn Neurodyn, 2013)

Coding of object features in V4 network

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Equation of ITC neurons
Membrane potentials of ith neuron in ITX layer(X=B,M,F)
CmdVi , ITXdt
 I FF  I FB  g Na m 3 h(Vi , ITX  VNa )  g K n 4 (Vi , ITX  VK )  g L (Vi , ITX  VL )Synaptic weights between ith neuron in X layer andjth neuron in Y layer (X,Y=ITB,ITM,ITF, and FRL)
wdwijZ, XYdt

  wijZ, XY  SiX S jY

Response properties for a pair of ITF neurons encoding
the features of eye and nose30

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Effect of top-down signal on spike correlationof a pair of IT neurons

Calculated results

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Coarse‐to‐fineprocess

Top‐down influence on V1 responses
in perceptual learning32

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Top‐down influence on V1 responses in perceptual
learning(Gilbert and Li, 2013)

Li et al. 2004
Ramalingam et al. 2013
Li et al. 2006
Gilbert and Sigman ,2007
Tong, 2003

(Super et al., 2001)

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Five‐bar discrimination task
Bisection task

Vernier task

0~196ｍｓ
196～496ms
496ms~(Li et al. 2004)

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67(Li et al. 2004)

Our approach
Top‐down influence reflecting
perceptual strategies of the two tasks
Synchronous oscillation(Bisection task)
Asynchronous oscillation(Vernier task)

A higher visual area (V2)
V2
Modulation of Long‐range horizontal
connections in V1Gating of task‐relevant information in earlyvisual area

V1

(Kamiyama et al., to be published)34

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Bisection taskvernier task

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Spike correlations36

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LFP coherence

Bisectiontask

Veriniertask

Formation of functional connectivity in the V1 network
Spike‐timing dependent
plasticity

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Decoding Word Information from Spatiotemporal
Activity of Sensory Neurons

Hierarchical processing of auditory informationpSTG(Chang et al., 2010)(Horikawa et al. 1996)(Burger and Pollak, 1998)

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ModelAttractor

Feature detectionSpatiotemporal activity(Hara et al., Cog Comput, 2014)

The model of A139

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Model of FD layer

Responses of FD neurons

Model of FB layer40

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Attractor models• Olfactory system• Visual system
categorization, face perception, top‐down influence• Auditory systemword perception, information processing in A1• Gustatory system,interaction between odor and gustatory information• Somatosensory system (tactile, haptic)

Future work41

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Medial temporal cortex

Prediction
FastprocessingSCLGNRetinaCommunication between brain areas through differentrhythms(Watrous et al., 2013)42