Setting the activity level in sparse random networks
Neural Computation
2005 Special issue: Interpreting hippocampal function as recoding and forecasting
Neural Networks - Special issue: Computational theories of the functions of the hippocampus
Decision functions that can support a hippocampal model
Neurocomputing
2005 Special issue: Interpreting hippocampal function as recoding and forecasting
Neural Networks - Special issue: Computational theories of the functions of the hippocampus
Decision functions that can support a hippocampal model
Neurocomputing
| Hi-index | 0.01 |
Our hippocampal model depends on randomization. In principle, randomizations, e.g., chaotic activity fluctuations, quantal synaptic failures, or initial state randomization, can be overcome by strong external excitation. However, if external activity is too low, randomization will destroy the information transmitted by the inputs. Here, computer simulations of the transitive inference paradigm reveal an optimal range of external excitation. At lower activity levels, optimal performance occurs when the relative external excitation accounts for 35-40% of the total with activity, while at higher activity, external activity can be as low as 30% of the total.