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Control of computational dynamics of coupled integrate-and-fire neurons
1Department of Theoretical Physics, Royal Institute of Technology, Stockholm, Sweden. boc@theophys.kth.se
Biological Cybernetics
|May 1, 1997
Summary
Neuronal adaptation in interconnected integrate-and-fire neuron networks generates distinct computational modes. Strong adaptation enables exploration of complex dynamics, while weak adaptation facilitates pattern retrieval, crucial for neural coding studies.
Area of Science:
- Computational Neuroscience
- Neural Networks
- Systems Neuroscience
Background:
- Cortical structures utilize interconnected excitatory and inhibitory neurons.
- Integrate-and-fire models approximate detailed neuronal dynamics.
- Neuronal adaptation influences network behavior.
Purpose of the Study:
- To demonstrate generation and control of dynamical modes in a neuronal network.
- To investigate the role of neuronal adaptation in network dynamics.
- To explore computational functions of adaptive neuronal networks.
Main Methods:
- Utilized integrate-and-fire neurons based on neocortical cell models.
- Implemented firing-rate adaptation in excitatory units via calcium ion regulation.
- Incorporated synaptic conductance saturation for interconnections.
- Modeled a network architecture resembling cortical structures.
Main Results:
- Neuronal adaptation generates richer network dynamics compared to fixed-point attractors.
- Strong adaptation leads to complex, aperiodic, or limit-cycle dynamics (exploratory mode).
- Weak adaptation results in fixed-point attractors (pattern retrieval mode).
Conclusions:
- Neuronal adaptation is key to switching between exploratory and retrieval computational modes.
- The model's realism enhances understanding of biological neural systems.
- This model is valuable for studying temporal aspects of neural coding.