Biomedical subjects

Richard H R Hahnloser

Publications and source records attributed to Richard H R Hahnloser.

10 recordsLinked to original sources

PubMed2006

Sleep-related spike bursts in HVC are driven by the nucleus interface of the nidopallium.

The function and the origin of replay of motor activity during sleep are currently unknown. Spontaneous activity patterns in the nucleus robustus of the arcopallium (RA) and in HVC (high vocal center) of the sleeping songbird resemble premotor patterns in these areas observed during singing. We test the hypothesis that the nucleus interface of the nidopallium (NIf) has an important role for initiating and shaping these sleep-related activity patterns. In head-fixed, sleeping zebra finches we find that injections of the GABA(A)-agonist muscimol into NIf lead to transient abolishment of premotor-like bursting activity in HVC neurons. Using antidromic activation of NIf neurons by electrical stimulation in HVC, we are able to distinguish a class of HVC-projecting NIf neurons from a second class of NIf neurons. Paired extracellular recordings in NIf and HVC show that NIf neurons provide a strong bursting drive to HVC. In contrast to HVC neurons, whose bursting activity waxes and wanes in burst epochs, individual NIf projection neurons are nearly continuously bursting and tend to burst only once on the timescale of song syllables. Two types of HVC projection neurons-premotor and striatal projecting-respond differently to the NIf drive, in agreement with notions of HVC relaying premotor signals to RA and an anticipatory copy thereof to areas of a basal ganglia pathway.

Action Potentials↗

PubMed2006

Sleep-related neural activity in a premotor and a basal-ganglia pathway of the songbird.

During singing, neurons in premotor nucleus RA (robust nucleus of the arcopallium) of the zebra finch produce complex temporal sequences of bursts that are recapitulated during sleep. RA receives input from nucleus HVC via the premotor pathway, and also from the lateral magnocellular nucleus of the anterior nidopallium (LMAN), part of a basal ganglia-related circuit essential for vocal learning. We explore the propagation of sleep-related spike patterns in these two pathways and their influences on RA activity. We promote sleep in head-fixed birds by injections of melatonin and make single-neuron recordings from the three major classes of neurons in HVC: RA-projecting neurons, Area X-projecting neurons, and interneurons. We also record LMAN neurons that project to RA. In paired recordings, spike trains from identified HVC neuron types are strongly coherent with spike trains in RA neurons, whereas LMAN projection neurons on average exhibit only a weak coherency with neurons in HVC and RA. We further examine the relative roles of HVC and LMAN in generating RA burst sequences with reversible inactivation. Lidocaine inactivation of HVC completely abolishes bursting in RA, whereas inactivation of LMAN has no effect on burst rates in RA. In combination, our data suggest that in adult birds, RA burst sequences in sleep are driven via the premotor pathway from HVC. We present a simple generative model of spike trains in HVC, RA, and LMAN neurons that is able to qualitatively reproduce observed coherency functions. We propose that commonly observed coherency peaks at positive and negative time lags are caused by sequentially correlated HVC activity.

Algorithms↗

PubMed2004

Temporal sparseness of the premotor drive is important for rapid learning in a neural network model of birdsong.

Sparse neural codes have been widely observed in cortical sensory and motor areas. A striking example of sparse temporal coding is in the song-related premotor area high vocal center (HVC) of songbirds: The motor neurons innervating avian vocal muscles are driven by premotor nucleus robustus archistriatalis (RA), which is in turn driven by nucleus HVC. Recent experiments reveal that RA-projecting HVC neurons fire just one burst per song motif. However, the function of this remarkable temporal sparseness has remained unclear. Because birdsong is a clear example of a learned complex motor behavior, we explore in a neural network model with the help of numerical and analytical techniques the possible role of sparse premotor neural codes in song-related motor learning. In numerical simulations with nonlinear neurons, as HVC activity is made progressively less sparse, the minimum learning time increases significantly. Heuristically, this slowdown arises from increasing interference in the weight updates for different synapses. If activity in HVC is sparse, synaptic interference is reduced, and is minimized if each synapse from HVC to RA is used only once in the motif, which is the situation observed experimentally. Our numerical results are corroborated by a theoretical analysis of learning in linear networks, for which we derive a relationship between sparse activity, synaptic interference, and learning time. If songbirds acquire their songs under significant pressure to learn quickly, this study predicts that HVC activity, currently measured only in adults, should also be sparse during the sensorimotor phase in the juvenile bird. We discuss the relevance of these results, linking sparse codes and learning speed, to other multilayered sensory and motor systems.

Algorithms↗

PubMed2004

Neural mechanisms of vocal sequence generation in the songbird.

Little is known about the biophysical and neuronal circuit mechanisms underlying the generation and learning of behavioral sequences. Songbirds provide a marvelous animal model in which to study these phenomena. By use of a motorized microdrive to record the activity of single neurons in the singing bird, we are beginning to understand the circuits that generate complex vocal sequences. We describe recent experiments elucidating the role of premotor song-control nucleus HVC in the production of song. We find that HVC neurons projecting to premotor nucleus RA each generate a single burst of spikes at a particular time in the song and may form a sparse representation of temporal order. We incorporate this observation into a working hypothesis for the generation of vocal sequences in the songbird, and examine some implications for song learning.

Animals↗

PubMed2003

Stationary transmission distribution of random spike trains by dynamical synapses.

Many nonlinearities in neural media are strongly dependent on spike timing jitter and intrinsic dynamics of synaptic transmission. Here we are interested in the stationary density of evoked postsynaptic potentials transmitted by depressing synapses for Poisson spike trains of fixed mean rates. We present a nonperturbative iterative method for computing the stationary density over increasing intervals. We conclude by showing how this method generalizes to other types of synapses, such as facilitating and hybrid synapses.

Journal Article↗

PubMed2003

Permitted and forbidden sets in symmetric threshold-linear networks.

The richness and complexity of recurrent cortical circuits is an inexhaustible source of inspiration for thinking about high-level biological computation. In past theoretical studies, constraints on the synaptic connection patterns of threshold-linear networks were found that guaranteed bounded network dynamics, convergence to attractive fixed points, and multistability, all fundamental aspects of cortical information processing. However, these conditions were only sufficient, and it remained unclear which were the minimal (necessary) conditions for convergence and multistability. We show that symmetric threshold-linear networks converge to a set of attractive fixed points if and only if the network matrix is copositive. Furthermore, the set of attractive fixed points is nonconnected (the network is multiattractive) if and only if the network matrix is not positive semidefinite. There are permitted sets of neurons that can be coactive at a stable steady state and forbidden sets that cannot. Permitted sets are clustered in the sense that subsets of permitted sets are permitted and supersets of forbidden sets are forbidden. By viewing permitted sets as memories stored in the synaptic connections, we provide a formulation of long-term memory that is more general than the traditional perspective of fixed-point attractor networks. There is a close correspondence between threshold-linear networks and networks defined by the generalized Lotka-Volterra equations.

Animals↗

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

An ultra-sparse code underlies the generation of neural sequences in a songbird.

Sequences of motor activity are encoded in many vertebrate brains by complex spatio-temporal patterns of neural activity; however, the neural circuit mechanisms underlying the generation of these pre-motor patterns are poorly understood. In songbirds, one prominent site of pre-motor activity is the forebrain robust nucleus of the archistriatum (RA), which generates stereotyped sequences of spike bursts during song and recapitulates these sequences during sleep. We show that the stereotyped sequences in RA are driven from nucleus HVC (high vocal centre), the principal pre-motor input to RA. Recordings of identified HVC neurons in sleeping and singing birds show that individual HVC neurons projecting onto RA neurons produce bursts sparsely, at a single, precise time during the RA sequence. These HVC neurons burst sequentially with respect to one another. We suggest that at each time in the RA sequence, the ensemble of active RA neurons is driven by a subpopulation of RA-projecting HVC neurons that is active only at that time. As a population, these HVC neurons may form an explicit representation of time in the sequence. Such a sparse representation, a temporal analogue of the 'grandmother cell' concept for object recognition, eliminates the problem of temporal interference during sequence generation and learning attributed to more distributed representations.

Action Potentials↗

PubMed2002

Attentional recruitment of inter-areal recurrent networks for selective gain control.

There is strong anatomical and physiological evidence that neurons with large receptive fields located in higher visual areas are recurrently connected to neurons with smaller receptive fields in lower areas. We have previously described a minimal neuronal network architecture in which top-down attentional signals to large receptive field neurons can bias and selectively read out the bottom-up sensory information to small receptive field neurons (Hahnloser, Douglas, Mahowald, & Hepp, 1999). Here we study an enhanced model, where the role of attention is to recruit specific inter-areal feedback loops (e.g., drive neurons above firing threshold). We first illustrate the operation of recruitment on a simple example of visual stimulus selection. In the subsequent analysis, we find that attentional recruitment operates by dynamical modulation of signal amplification and response multistability. In particular, we find that attentional stimulus selection necessitates increased recruitment when the stimulus to be selected is of small contrast and of small distance away from distractor stimuli. The selectability of a low-contrast stimulus is dependent on the gain of attentional effects; for example, low-contrast stimuli can be selected only when attention enhances neural responses. However, the dependence of attentional selection on stimulus-distractor distance is not contingent on whether attention enhances or suppresses responses. The computational implications of attentional recruitment are that cortical circuits can behave as winner-take-all mechanisms of variable strength and can achieve close to optimal signal discrimination in the presence of external noise.

Animals↗

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↗