population coding Population vector population coding Bayes Population
あらすじ 古典的population coding仮説 Population vector 確率的population coding仮説 Bayes/最尤推定 不確定性の表現 複数入力の統合
Population Vectors (Georgopoulos) 1次運動野ニューロンの運動方向選択性 cosine tuning fi(m) = bi + ai cos( qm-qi) preferred direction qi は一様に分布 Population vector v(m) = Si fi(m) vi simplicity and robustness.
Probabilistic Population Codes (Zemel, Dayan, Pouget 1998) Encoding: underlying quantity x noisy response: P[r|x] Decoding Bayesian: P[x|r] P[r|x] P[x] Standard Poisson model encode single value of x P[ri|x] = e-fi(x) (fi(x))ri/ri! P[x|r] = P[x] Pi e-fi(x) (fi(x))ri/ri!
Extended Poisson Model Encode the probability distribution P[x|w] uncertainty, multiple values <ri> = x P[x|w] fi(x) dx Decoding for histogram representation <ri> = Sj fj fij log. P[{fj}|{ri}] = K + log. P[{fj}] + Si ri log[Sj fj fij]
Roles of the Cerebral Cortex Representations for good generalization modality, invariance, resolution, . . . task oriented context and working memory Combining multiple outputs reasons for poplulation coding?
Population Coding (Deneve et al. , 2001) Function approximation and Cue Integration
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