closed form solution
时间: 2023-10-09 13:13:28 浏览: 130
A closed-form solution is a mathematical expression that can be evaluated in a finite number of operations, using only known and accepted operations. In other words, it is a formula that provides an exact solution to a problem, without the need for iterative or numerical methods. Examples of problems that have closed-form solutions include linear equations, quadratic equations, and some integrals. The advantage of having a closed-form solution is that it can be computed quickly and accurately, without the need for approximations or simulations.
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什么是 closed form solution
Closed form solution 是指可以用一个公式或者表达式来直接求得解的问题。这个公式或者表达式可以包含基本的数学运算、常量和变量,但不能包含未知的函数或者变量。简单来说,closed form solution 就是能够用一个封闭的数学表达式来计算出问题的解,而不需要进行迭代或者逼近的计算。在某些数学问题中,存在closed form solution 可以大大简化问题的求解过程。
function [v_final]=Closed_form_solution_neuron(neuron_number,v_pre,delta_t,y_init,g_l,v_l,c_m,sigma,mu,W,E) y_1 = y_init; %[v_pre_resampled time_ref]= resample(v_pre,[1:size(v_pre,1)],1/delta_t); time = 0:delta_t:(size(v_pre{neuron_number},1)*delta_t)-delta_t; time = time'; v_connections_pre = zeros(size(v_pre{neuron_number},1),1); %tau = (y_1 - sum(E)).*exp(-time'.*[(g_l(1,1)./c_m(1,1)) + sum([W.*1./(exp(-sigma.*(v_pre{neuron_number}'-mu))+1)],1)./c_m(1,1)]); %v_final = tau.*sum(1./(exp(sigma.*(v_pre{neuron_number}'-mu))+1).^W,1)+sum(v_l(1,1)+E); for i=1:size(v_pre{neuron_number},2) v_connections(:,i) = (y_1 - E(i,1)).* exp(-time.*[(g_l(1,1)./c_m(1,1)) + [W(i)*1./(exp(-sigma(i,1).*(v_pre{neuron_number}(:,i)-mu(i,1)))+1)]/c_m(1,1)]).*(1./(exp(sigma(i,1).*(v_pre{neuron_number}(:,i)-mu(i,1)))+1).^W(i))+E(i); %v_connections(:,i) = (y_1 - E(i,1)).* exp(-time.*[(g_l(1,1)./c_m(1,1))]).*(1./(exp(sigma(i,1).*(v_pre{neuron_number}(:,i)-mu(i,1)))+1).^W(i))+E(i); v_final = v_connections_pre + v_connections(:,i); v_connections_pre = v_final; end %v_final = v_final + v_l(1,1); clear g_l v_l c_m sigma mu W E time v_connections
这段代码实现了一个神经元模型的闭合解析解法,输入参数包括预先记录的外部电压v_pre,时间间隔delta_t,初始状态y_init,以及神经元模型的参数g_l, v_l, c_m, sigma, mu, W和E。其中,g_l是静息电导,v_l是静息电位,c_m是膜电容,sigma和mu是激活函数的参数,W是突触权值矩阵,E是突触电位。该代码通过解析求解微分方程,计算出神经元的动态响应v_final,并返回结果。其中,v_connections表示每个突触的动态响应,v_connections_pre表示上一个突触的动态响应,通过循环累加每个突触的动态响应得到最终结果v_final。
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