To solve the given optimization problem, we first need to translate the natural language description into a symbolic representation. This involves defining variables, an objective function, and constraints using mathematical notation.

Let's denote:
- $x_0$ as the amount of grams of carbohydrates,
- $x_1$ as the amount of milligrams of vitamin B4.

The objective function is to maximize: $7x_0 + x_1$

Given the resources/attributes, we can infer the following coefficients for the immune support and cognitive performance indices:
- Immune support index per gram of carbohydrates: 4
- Cognitive performance index per gram of carbohydrates: 1
- Immune support index per milligram of vitamin B4: 8
- Cognitive performance index per milligram of vitamin B4: 1

Constraints as described:
1. Total combined immune support index is at least 12: $4x_0 + 8x_1 \geq 12$
2. Total combined cognitive performance index is at least 13: $x_0 + x_1 \geq 13$
3. Constraint on grams of carbohydrates and milligrams of vitamin B4: $6x_0 - 3x_1 \geq 0$
4. Maximum total combined immune support index is 36: $4x_0 + 8x_1 \leq 36$
5. Total combined cognitive performance index should be at most 16: $x_0 + x_1 \leq 16$

The symbolic representation of the problem is thus:
```json
{
  'sym_variables': [('x0', 'grams of carbohydrates'), ('x1', 'milligrams of vitamin B4')],
  'objective_function': '7*x0 + x1',
  'constraints': [
    '4*x0 + 8*x1 >= 12',
    'x0 + x1 >= 13',
    '6*x0 - 3*x1 >= 0',
    '4*x0 + 8*x1 <= 36',
    'x0 + x1 <= 16'
  ]
}
```

Now, let's express this problem in Gurobi code. We'll use Python as our programming language for this purpose.

```python
from gurobipy import *

# Create a new model
m = Model("Optimization_Problem")

# Add variables
x0 = m.addVar(name="grams_of_carbohydrates", lb=-GRB.INFINITY, ub=GRB.INFINITY)
x1 = m.addVar(name="milligrams_of_vitamin_B4", lb=-GRB.INFINITY, ub=GRB.INFINITY)

# Set the objective function
m.setObjective(7*x0 + x1, GRB.MAXIMIZE)

# Add constraints
m.addConstr(4*x0 + 8*x1 >= 12, name="immune_support_index_min")
m.addConstr(x0 + x1 >= 13, name="cognitive_performance_index_min")
m.addConstr(6*x0 - 3*x1 >= 0, name="carbohydrates_vitamin_b4_constraint")
m.addConstr(4*x0 + 8*x1 <= 36, name="immune_support_index_max")
m.addConstr(x0 + x1 <= 16, name="cognitive_performance_index_max")

# Optimize model
m.optimize()

if m.status == GRB.OPTIMAL:
    print("Optimal solution found:")
    print(f"Grams of carbohydrates: {x0.x}")
    print(f"Milligrams of vitamin B4: {x1.x}")
    print(f"Objective function value: {m.objVal}")
else:
    print("No optimal solution found")
```