Journal of Neuroscience · 2005 · 168 citations · 54 references
Life SciencesNeurobiologyNeuroscienceComputational NeuroscienceNeurophysiologySystems NeuroscienceNeural Circuit DynamicsNeural CircuitsNeural System ModelingNeuronal DynamicsSpiking Neural NetworksSynaptic TransmissionAttractor NetworksComputational Neuroscience ModelsNeuron DynamicsNeuronal Network DynamicsRecurrent ExcitationSpiking ActivitySpiking Neural Network ModelsSpiking Neuron Models
Head‑direction information is encoded by persistent neural activity, traditionally modeled as a recurrently excited hill of activity, but recent data show it arises from a reciprocal LMN‑DTN loop lacking local excitation, and its update by angular head velocity remains unclear. The authors sought to determine how head‑direction signals can be generated and updated without recurrent excitation. They constructed a biophysically realistic LMN‑DTN network of noisy spiking neurons with cross‑inhibitory connections to test this hypothesis. The model demonstrates that uniform external drive plus recurrent cross‑inhibition produces direction‑selective persistent activity, reproduces the three experimentally observed HD tuning curve types modulated by angular velocity, shows that strong external cues can calibrate or reset the internal representation, and identifies microcircuit properties that enable time integration.
During spatial navigation, the head orientation of an animal is encoded internally by neural persistent activity in the head-direction (HD) system. In computational models, such a bell-shaped “hill of activity” is commonly assumed to be generated by recurrent excitation in a continuous attractor network. Recent experimental evidence, however, indicates that HD signal in rodents originates in a reciprocal loop between the lateral mammillary nucleus (LMN) and the dorsal tegmental nucleus (DTN), which is characterized by a paucity of local excitatory axonal collaterals. Moreover, when the animal turns its head to a new direction, the heading information is updated by a time integration of angular head velocity (AHV) signals; the underlying mechanism remains unresolved. To investigate these issues, we built and investigated an LMN-DTN network model that consists of three populations of noisy and spiking neurons coupled by biophysically realistic synapses. We found that a combination of uniform external excitation and recurrent cross-inhibition can give rise to direction-selective persistent activity. The model reproduces the experimentally observed three types of HD tuning curves differentially modulated by AHV and anticipatory firing activity in LMN HD cells. Time integration is assessed by using constant or sinusoidal angular velocity stimuli, as well as naturalistic AHV inputs (from rodent recordings). Furthermore, the internal representation of head direction is shown to be calibrated or reset by strong external cues. We identify microcircuit properties that determine the ability of our model network to subserve time integration function.
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Advances in neural information processing systems 7
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