Biomedical subjects

H Sebastian Seung

Publications and source records attributed to H Sebastian Seung.

22 records · Page 2Linked to original sources

PubMed2002

Double-ring network model of the head-direction system.

In the head-direction system, the orientation of an animal's head in space is encoded internally by persistent activities of a pool of cells whose firing rates are tuned to the animal's directional heading. To maintain an accurate representation of the heading information when the animal moves, the system integrates horizontal angular head-velocity signals from the vestibular nuclei and updates the representation of directional heading. The integration is a difficult process, given that head velocities can vary over a large range and the neural system is highly nonlinear. Previous models of integration have relied on biologically unrealistic mechanisms, such as instantaneous changes in synaptic strength, or very fast synaptic dynamics. In this paper, we propose a different integration model with two populations of neurons, which performs integration based on the differential input of the vestibular nuclei to these two populations. We mathematically analyze the dynamics of the model and demonstrate that with carefully tuned synaptic connections it can accurately integrate a large range of the vestibular input, with potentially slow synapses.

Animals↗

PubMed2002

Fast computation with spikes in a recurrent neural network.

Neural networks with recurrent connections are sometimes regarded as too slow at computation to serve as models of the brain. Here we analytically study a counterexample, a network consisting of N integrate-and-fire neurons with self excitation, all-to-all inhibition, instantaneous synaptic coupling, and constant external driving inputs. When the inhibition and/or excitation are large enough, the network performs a winner-take-all computation for all possible external inputs and initial states of the network. The computation is done very quickly: As soon as the winner spikes once, the computation is completed since no other neurons will spike. For some initial states, the winner is the first neuron to spike, and the computation is done at the first spike of the network. In general, there are M potential winners, corresponding to the top M external inputs. When the external inputs are close in magnitude, M tends to be larger. If M>1, the selection of the actual winner is strongly influenced by the initial states. If a special relation between the excitation and inhibition is satisfied, the network always selects the neuron with the maximum external input as the winner.

Journal Article↗

PubMed2002

Selectively grouping neurons in recurrent networks of lateral inhibition.

Winner-take-all networks have been proposed to underlie many of the brain's fundamental computational abilities. However, not much is known about how to extend the grouping of potential winners in these networks beyond single neuron or uniformly arranged groups of neurons. We show that competition between arbitrary groups of neurons can be realized by organizing lateral inhibition in linear threshold networks. Given a collection of potentially overlapping groups (with the exception of some degenerate cases), the lateral inhibition results in network dynamics such that any permitted set of neurons that can be coactivated by some input at a stable steady state is contained in one of the groups. The information about the input is preserved in this operation. The activity level of a neuron in a permitted set corresponds to its stimulus strength, amplified by some constant. Sets of neurons that are not part of a group cannot be coactivated by any input at a stable steady state. We analyze the storage capacity of such a network for random groups--the number of random groups the network can store as permitted sets without creating too many spurious ones. In this framework, we calculate the optimal sparsity of the groups (maximizing group entropy). We find that for dense inputs, the optimal sparsity is unphysiologically small. However, when the inputs and the groups are equally sparse, we derive a more plausible optimal sparsity. We believe our results are the first steps toward attractor theories in hybrid analog-digital networks.

Brain↗

PubMed1998

Continuous attractors and oculomotor control.

A recurrent neural network can possess multiple stable states, a property that many brain theories have implicated in learning and memory. There is good evidence for such multistability in the brainstem neural network that controls eye position. Because the stable states are arranged in a continuous dynamical attractor, the network can store a memory of eye position with analog neural encoding. Continuous attractors in model networks depend on precisely tuned positive feedback, and their robust maintenance requires mechanisms of synaptic plasticity. These ideas may have wider scope than just the oculomotor system. More generally, the internal models postulated by theories of biological motor control may be recurrent networks with continuous attractors.

Journal Article↗