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Biomedical subjects

H S Seung

Publications and source records attributed to H S Seung.

10 recordsLinked to original sources
PubMed2001

In vivo intracellular recording and perturbation of persistent activity in a neural integrator.

To investigate the mechanisms of persistent neural activity, we obtained in vivo intracellular recordings from neurons in an oculomotor neural integrator of the goldfish during spontaneous saccades and fixations. Persistent changes in firing rate following saccades were associated with step changes in interspike membrane potential that were correlated with changes in eye position. Perturbation of persistent activity with brief intracellular current pulses designed to mimic saccadic input only induced transient changes of firing rate and membrane potential. When neurons were hyperpolarized below action potential threshold, position-correlated step changes in membrane potential remained. Membrane potential fluctuations were greater during more depolarized steps. These results suggest that sustained changes in firing rate are supported not by either membrane multistability or changes in pacemaker currents, but rather by persistent changes in the rate or amplitude of synaptic inputs.

Action Potentials↗
PubMed2000

Cognition. The manifold ways of perception.

One of the great puzzles of visual perception is how an image that is in perpetual flux can still be seen by the observer as the same object. In an informative Perspective, Seung and Lee explain the mathematical intricacies of two new algorithms for modeling the variability of perceptual stimuli and other types of high-dimensional data (Tenenbaum et al., and Roweis and Saul).

Algorithms↗
PubMed2000

Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit.

Digital circuits such as the flip-flop use feedback to achieve multistability and nonlinearity to restore signals to logical levels, for example 0 and 1. Analogue feedback circuits are generally designed to operate linearly, so that signals are over a range, and the response is unique. By contrast, the response of cortical circuits to sensory stimulation can be both multistable and graded. We propose that the neocortex combines digital selection of an active set of neurons with analogue response by dynamically varying the positive feedback inherent in its recurrent connections. Strong positive feedback causes differential instabilities that drive the selection of a set of active neurons under the constraints embedded in the synaptic weights. Once selected, the active neurons generate weaker, stable feedback that provides analogue amplification of the input. Here we present our model of cortical processing as an electronic circuit that emulates this hybrid operation, and so is able to perform computations that are similar to stimulus selection, gain modulation and spatiotemporal pattern generation in the neocortex.

Electrophysiology↗
PubMed2000

Stability of the memory of eye position in a recurrent network of conductance-based model neurons.

Studies of the neural correlates of short-term memory in a wide variety of brain areas have found that transient inputs can cause persistent changes in rates of action potential firing, through a mechanism that remains unknown. In a premotor area that is responsible for holding the eyes still during fixation, persistent neural firing encodes the angular position of the eyes in a characteristic manner: below a threshold position the neuron is silent, and above it the firing rate is linearly related to position. Both the threshold and linear slope vary from neuron to neuron. We have reproduced this behavior in a biophysically plausible network model. Persistence depends on precise tuning of the strength of synaptic feedback, and a relatively long synaptic time constant improves the robustness to mistuning.

Animals↗
PubMed2000

Anatomy and discharge properties of pre-motor neurons in the goldfish medulla that have eye-position signals during fixations.

Previous work in goldfish has suggested that the oculomotor velocity-to-position neural integrator for horizontal eye movements may be confined bilaterally to a distinct group of medullary neurons that show an eye-position signal. To establish this localization, the anatomy and discharge properties of these position neurons were characterized with single-cell Neurobiotin labeling and extracellular recording in awake goldfish while monitoring eye movements with the scleral search-coil method. All labeled somata (n = 9) were identified within a region of a medially located column of the inferior reticular formation that was approximately 350 microm in length, approximately 250 microm in depth, and approximately 125 microm in width. The dendrites of position neurons arborized over a wide extent of the ventral half of the medulla with especially heavy ramification in the initial 500 microm rostral of cell somata (n = 9). The axons either followed a well-defined ventral pathway toward the ipsilateral abducens (n = 4) or crossed the midline (n = 2) and projected toward the contralateral group of position neurons and the contralateral abducens. A mapping of the somatic region using extracellular single unit recording revealed that position neurons (n > 120) were the dominant eye-movement-related cell type in this area. Position neurons did not discharge below a threshold value of horizontal fixation position of the ipsilateral eye. Above this threshold, firing rates increased linearly with increasing temporal position [mean position sensitivity = 2.8 (spikes/s)/ degrees, n = 44]. For a given fixation position, average rates of firing were higher after a temporal saccade than a nasal one (n = 19/19); the magnitude of this hysteresis increased with increasing position sensitivity. Transitions in firing rate accompanying temporal saccades were overshooting (n = 43/44), beginning, on average, 17.2 ms before saccade onset (n = 17). Peak firing rate change accompanying temporal saccades was correlated with eye velocity (n = 36/41). The anatomical findings demonstrate that goldfish medullary position neurons have somata that are isolated from other parts of the oculomotor system, have dendritic fields overlapping with axonal terminations of neurons with velocity signals, and have axons that are capable of relaying commands to the abducens. The physiological findings demonstrate that the signals carried by position neurons could be used by motoneurons to set the fixation position of the eye. These results are consistent with a role for position neurons as elements of the velocity-to-position neural integrator for horizontal eye movements.

Abducens Nerve↗
PubMed1999

Learning the parts of objects by non-negative matrix factorization.

Is perception of the whole based on perception of its parts? There is psychological and physiological evidence for parts-based representations in the brain, and certain computational theories of object recognition rely on such representations. But little is known about how brains or computers might learn the parts of objects. Here we demonstrate an algorithm for non-negative matrix factorization that is able to learn parts of faces and semantic features of text. This is in contrast to other methods, such as principal components analysis and vector quantization, that learn holistic, not parts-based, representations. Non-negative matrix factorization is distinguished from the other methods by its use of non-negativity constraints. These constraints lead to a parts-based representation because they allow only additive, not subtractive, combinations. When non-negative matrix factorization is implemented as a neural network, parts-based representations emerge by virtue of two properties: the firing rates of neurons are never negative and synaptic strengths do not change sign.

Algorithms↗
PubMed1996

How the brain keeps the eyes still.

The brain can hold the eyes still because it stores a memory of eye position. The brain's memory of horizontal eye position appears to be represented by persistent neural activity in a network known as the neural integrator, which is localized in the brainstem and cerebellum. Existing experimental data are reinterpreted as evidence for an "attractor hypothesis" that the persistent patterns of activity observed in this network form an attractive line of fixed points in its state space. Line attractor dynamics can be produced in linear or nonlinear neural networks by learning mechanisms that precisely tune positive feedback.

Animals↗
PubMed1993

Simple models for reading neuronal population codes.

In many neural systems, sensory information is distributed throughout a population of neurons. We study simple neural network models for extracting this information. The inputs to the networks are the stochastic responses of a population of sensory neurons tuned to directional stimuli. The performance of each network model in psychophysical tasks is compared with that of the optimal maximum likelihood procedure. As a model of direction estimation in two dimensions, we consider a linear network that computes a population vector. Its performance depends on the width of the population tuning curves and is maximal for width, which increases with the level of background activity. Although for narrowly tuned neurons the performance of the population vector is significantly inferior to that of maximum likelihood estimation, the difference between the two is small when the tuning is broad. For direction discrimination, we consider two models: a perceptron with fully adaptive weights and a network made by adding an adaptive second layer to the population vector network. We calculate the error rates of these networks after exhaustive training to a particular direction. By testing on the full range of possible directions, the extent of transfer of training to novel stimuli can be calculated. It is found that for threshold linear networks the transfer of perceptual learning is nonmonotonic. Although performance deteriorates away from the training stimulus, it peaks again at an intermediate angle. This nonmonotonicity provides an important psychophysical test of these models.

Animals↗
PubMedDate not supplied

The autapse: a simple illustration of short-term analog memory storage by tuned synaptic feedback.

According to a popular hypothesis, short-term memories are stored as persistent neural activity maintained by synaptic feedback loops. This hypothesis has been formulated mathematically in a number of recurrent network models. Here we study an abstraction of these models, a single neuron with a synapse onto itself, or autapse. This abstraction cannot simulate the way in which persistent activity patterns are distributed over neural populations in the brain. However, with proper tuning of parameters, it does reproduce the continuously graded, or analog, nature of many examples of persistent activity. The conditions for tuning are derived for the dynamics of a conductance-based model neuron with a slow excitatory autapse. The derivation uses the method of averaging to approximate the spiking model with a nonspiking, reduced model. Short-term analog memory storage is possible if the reduced model is approximately linear and if its feedforward bias and autapse strength are precisely tuned.

Action Potentials↗