… | x 1 : n , y 1 : n ) is the probability distribution of the hidden parameter v c after n trials. Importantly,
… of v c n + 1 directly from v c n , because to calculate v c n + 1 = ∫ v ′ c p ( v ′ c | x 1 : …

Lysosomal H(+)-ATPase was purified to homogeneity from rat liver lysosomes. It is a bafilomycin
A1-sensitive Mg(2+)-ATPase, which reacts with antibodies against the 16- and 70-kDa …

To survive, animals must adapt synaptic weights based on external stimuli and rewards.
And they must do so using local, biologically plausible, learning rules--a highly nontrivial …

… where c is a N c -dimensional vector and ψ is a nonlinear mapping ψ : R N × R N → R N c
⁠. This … If we define Qi j k = ∑ n Q i j n A n k and Pl m k = ∑ n P l m n A - 1 k n with an N c × …

… where n is the noise due to sensory variability and unreliable OSN-spiking activity, and
the affinity, or the mixing weight, w ij , determines how strongly odor j activates glomerulus i (Fig. …

… We defined the mean potential of a dendritic subunit k as u b k (t) ≡ ∑ i=1 N b E w i k u i k
(t)/(w o E N b E ), and calculated the somatic membrane potential as u soma (t) ≡ ∑ k=1 K g b …

… The model consists of N E excitatory neurons and N I inhibitory neurons. The excitatory
neurons are connected with each other by a lognormally weight-distributed Hebbian-type …

… For simplicity, we assume that the input and the intermediate layer have identical size N,
and we consider N to be large. The sensory input vector is indicated with x. Activity in the …

… The network consists of N E excitatory neurons and N I inhibitory neurons (N E = 2500, N
I = 500), connected randomly with connection probability c XY (X,Y = E or I). We defined …

… The number of input neurons was N I = 26 in all simulations except S4C Fig, in which N I
was 5s with s being the size of the chunk. In all simulations, τ = 10 [ms] and g G = 1.5. The …