CiteULike: fbaroni's library [686 articles]

Spike train signatures of retinal ganglion cell types

Zeck — 2007-07-20T05:44:21-00:00

European Journal of Neuroscience, Vol. 26, No. 2. (July 2007), pp. 367-380.

Optimal short-term population coding: when Fisher information fails.

M Bethge — 2006-09-27T15:36:41-00:00

Neural Comput, Vol. 14, No. 10. (October 2002), pp. 2317-2351.

Efficient coding has been proposed as a first principle explaining neuronal response properties in the central nervous system. The shape of optimal codes, however, strongly depends on the natural limitations of the particular physical system. Here we investigate how optimal neuronal encoding strategies are influenced by the finite number of neurons N (place constraint), the limited decoding time window length T (time constraint), the maximum neuronal firing rate f(max) (power constraint), and the maximal average rate (f)(max) (energy constraint). While Fisher information provides a general lower bound for the mean squared error of unbiased signal reconstruction, its use to characterize the coding precision is limited. Analyzing simple examples, we illustrate some typical pitfalls and thereby show that Fisher information provides a valid measure for the precision of a code only if the dynamic range (f(min)T, f(max)T) is sufficiently large. In particular, we demonstrate that the optimal width of gaussian tuning curves depends on the available decoding time T. Within the broader class of unimodal tuning functions, it turns out that the shape of a Fisher-optimal coding scheme is not unique. We solve this ambiguity by taking the minimum mean square error into account, which leads to flat tuning curves. The tuning width, however, remains to be determined by energy constraints rather than by the principle of efficient coding.

On the emergence and awareness of auditory objects.

S Shamma — 2008-06-29T13:50:54-00:00

PLoS biology, Vol. 6, No. 6. (24 June 2008)

Mapping the Structural Core of Human Cerebral Cortex

Patric Hagmann — 2008-07-02T03:10:31-00:00

PLoS Biology, Vol. 6, No. 7. (1 July 2008), e159.

Structurally segregated and functionally specialized regions of the human cerebral cortex are interconnected by a dense network of cortico-cortical axonal pathways. By using diffusion spectrum imaging, we noninvasively mapped these pathways within and across cortical hemispheres in individual human participants. An analysis of the resulting large-scale structural brain networks reveals a structural core within posterior medial and parietal cerebral cortex, as well as several distinct temporal and frontal modules. Brain regions within the structural core share high degree, strength, and betweenness centrality, and they constitute connector hubs that link all major structural modules. The structural core contains brain regions that form the posterior components of the human default network. Looking both within and outside of core regions, we observed a substantial correspondence between structural connectivity and resting-state functional connectivity measured in the same participants. The spatial and topological centrality of the core within cortex suggests an important role in functional integration.

Coordination of Motor Neurons by the Leech Heartbeat Central Pattern Generator: Modeling the Role of the Inhibitory Input and Electrical Coupling.

Paul S Garcia — 2008-06-30T13:46:47-00:00

Journal of neurophysiology (25 June 2008)

Previously we presented a quantitative description of the spatiotemporal pattern of inhibitory synaptic input from the heartbeat CPG to segmental motor neurons that drive heartbeat in the medicinal leech and the resultant coordination of CPG interneurons and motor neurons (Norris et al. 2006; Norris et al. 2007a; b). To begin elucidating the mechanisms of coordination, we explore intersegmental and side-to-side coordination in an ensemble model of all heart motor neurons and their known synaptic inputs and electrical coupling. Model motor neuron intrinsic properties were kept simple enabling us to determine the extent to which input and electrical coupling acting together can account for observed coordination in the living system in the absence of a substantive contribution from the motor neurons themselves. The living system produces an asymmetric motor pattern: motor neurons on one side fire nearly in synchrony (synchronous), while on the other they fire in a rear-to-front progression (peristaltic). The model reproduces the general trends of intersegmental and side-to-side phase relations among motor neurons, but the match with the living system is not quantitatively accurate. Thus realistic (experimentally determined) inputs do not produce similarly realistic output in our model suggesting that motor neuron intrinsic properties may contribute to their coordination. By varying parameters that determine electrical coupling, conduction delays, intraburst synaptic plasticity, and motor neuron excitability, we show that the most important determinant of intersegmental and side-to-side phase relations in the model was the spatiotemporal pattern of synaptic inputs, yet phasing was influenced significantly by electrical coupling.

Competing for memory: hippocampal LTP under regimes of reduced protein synthesis.

R Fonseca — 2008-06-20T16:16:13-00:00

Neuron, Vol. 44, No. 6. (16 December 2004), pp. 1011-1020.

The persistence of synaptic potentiation in the hippocampus is known to depend on transcription and protein synthesis. We report here that, under regimes of reduced protein synthesis, competition between synapses for the relevant intracellular proteins can be demonstrated. Under such circumstances, the induction of additional protein synthesis-dependent long-term potentiation for a given set of postsynaptic neurons occurs at the expense of the maintenance of prior potentiation on an independent pathway. This new phenomenon, which we call "competitive maintenance," has important functional consequences, and it may be explained in terms of dynamic interactions between synapses and "plasticity factors" over extended periods of time.

Synaptic tagging and long-term potentiation.

U Frey — 2006-08-01T20:13:42-00:00

Nature, Vol. 385, No. 6616. (6 February 1997), pp. 533-536.

Repeated stimulation of hippocampal neurons can induce an immediate and prolonged increase in synaptic strength that is called long-term potentiation (LTP)-the primary cellular model of memory in the mammalian brain. An early phase of LTP (lasting less than three hours) can be dissociated from late-phase LTP by using inhibitors of transcription and translation, Because protein synthesis occurs mainly in the cell body, whereas LTP is input-specific, the question arises of how the synapse specificity of late LTP is achieved without elaborate intracellular protein trafficking. We propose that LTP initiates the creation of a short-lasting protein-synthesis-independent 'synaptic tag' at the potentiated synapse which sequesters the relevant protein(s) to establish late LTP. In support of this idea, we now show that weak tetanic stimulation, which ordinarily leads only to early LTP, or repeated tetanization in the presence of protein-synthesis inhibitors, each results in protein-synthesis-dependent late LTP, provided repeated tetanization has already been applied at another input to the same population of neurons. The synaptic tag decays in less than three hours. These findings indicate that the persistence of LTP depends not only on local events during its induction, but also on the prior activity of the neuron.

Long-term potentiation: outstanding questions and attempted synthesis.

J Lisman — 2008-06-20T15:40:13-00:00

Philosophical transactions of the Royal Society of London. Series B, Biological sciences, Vol. 358, No. 1432. (29 April 2003), pp. 829-842.

This article attempts an overview of the mechanism of NMDAR-dependent long-term potentiation (LTP) and its role in hippocampal networks. Efforts are made to integrate information, often in speculative ways, and to identify unresolved issues about the induction, expression and molecular storage processes. The pre/post debate about LTP expression has been particularly difficult to resolve. The following hypothesis attempts to reconcile the available physiological evidence as well as anatomical evidence that LTP increases synapse size. It is proposed that synapses are composed of a variable number of trans-synaptic modules, each having presynaptic release sites and a postsynaptic structure that can be AMPAfied by the addition of a hyperslot assembly that anchors 10-20 AMPA channels. According to a newly developed view of transmission, the quantal response is generated by AMPA channels near the site of vesicle release and so will depend on whether the module where release occurs has been AMPAfied. LTP expression may involve two structurally mediated processes: (i) the AMPAfication of existing modules by addition of hyperslot assemblies: this is a purely postsynaptic process and produces an increase in the probability of an AMPA response, with no change in the NMDA component; and (ii) the addition of new modules: this is a structurally coordinated pre/post process that leads to LTP-induced synapse enlargement and potentiation of the NMDA component owing to an increase in the number of release sites (the number of NMDA channels is assumed to be fixed). The protocol used for LTP induction appears to affect the proportion of these two processes; pairing protocols that involve low-frequency presynaptic stimulation induce only AMPAfication, making LTP purely postsynaptic, whereas high-frequency stimulation evokes both processes, giving rise to a presynaptic component. This model is capable of reconciling much of the seemingly contradictory evidence in the pre/post debate. The structural nature of the postulated changes is relevant to a second debate: whether a CaMKII switch or protein-dependent structural change is the molecular memory mechanism. A possible reconciliation is that a reversible CaMKII switch controls the construction of modules and hyperslot assemblies from newly synthesized proteins.

Synaptic homeostasis and input selectivity follow from a calcium-dependent plasticity model

Luk Yeung — 2008-03-25T15:19:22-00:00

Proceedings of the National Academy of Sciences, Vol. 101, No. 41. (12 October 2004), pp. 14943-14948.

Modifications in the strengths of synapses are thought to underlie memory, learning, and development of cortical circuits. Many cellular mechanisms of synaptic plasticity have been investigated in which differential elevations of postsynaptic calcium concentrations play a key role in determining the direction and magnitude of synaptic changes. We have previously described a model of plasticity that uses calcium currents mediated by N-methyl-D-aspartate receptors as the associative signal for Hebbian learning. However, this model is not completely stable. Here, we propose a mechanism of stabilization through homeostatic regulation of intracellular calcium levels. With this model, synapses are stable and exhibit properties such as those observed in metaplasticity and synaptic scaling. In addition, the model displays synaptic competition, allowing structures to emerge in the synaptic space that reflect the statistical properties of the inputs. Therefore, the combination of a fast calcium-dependent learning and a slow stabilization mechanism can account for both the formation of selective receptive fields and the maintenance of neural circuits in a state of equilibrium. 10.1073/pnas.0405555101

Decoding temporal information: A model based on short-term synaptic plasticity.

DV Buonomano — 2005-11-10T20:33:52-00:00

J Neurosci, Vol. 20, No. 3. (1 February 2000), pp. 1129-1141.

In the current paper it is proposed that short-term plasticity and dynamic changes in the balance of excitatory-inhibitory interactions may underlie the decoding of temporal information, that is, the generation of temporally selective neurons. Our initial approach was to simulate excitatory-inhibitory disynaptic circuits. Such circuits were composed of a single excitatory and inhibitory neuron and incorporated short-term plasticity of EPSPs and IPSPs and slow IPSPs. We first showed that it is possible to tune cells to respond selectively to different intervals by changing the synaptic weights of different synapses in parallel. In other words, temporal tuning can rely on long-term changes in synaptic strength and does not require changes in the time constants of the temporal properties. When the units studied in disynaptic circuits were incorporated into a larger single-layer network, the units exhibited a broad range of temporal selectivity ranging from no interval tuning to interval-selective tuning. The variability in temporal tuning relied on the variability of synaptic strengths. The network as a whole contained a robust population code for a wide range of intervals. Importantly, the same network was able to discriminate simple temporal sequences. These results argue that neural circuits are intrinsically able to process temporal information on the time scale of tens to hundreds of milliseconds and that specialized mechanisms, such as delay lines or oscillators, may not be necessary.

Neuronal oscillations in cortical networks.

G Buzsáki — 2005-10-05T00:55:25-00:00

Science, Vol. 304, No. 5679. (25 June 2004), pp. 1926-1929.

Clocks tick, bridges and skyscrapers vibrate, neuronal networks oscillate. Are neuronal oscillations an inevitable by-product, similar to bridge vibrations, or an essential part of the brain's design? Mammalian cortical neurons form behavior-dependent oscillating networks of various sizes, which span five orders of magnitude in frequency. These oscillations are phylogenetically preserved, suggesting that they are functionally relevant. Recent findings indicate that network oscillations bias input selection, temporally link neurons into assemblies, and facilitate synaptic plasticity, mechanisms that cooperatively support temporal representation and long-term consolidation of information.

Cascade models of synaptically stored memories.

S Fusi — 2006-08-01T07:18:07-00:00

Neuron, Vol. 45, No. 4. (17 February 2005), pp. 599-611.

Storing memories of ongoing, everyday experiences requires a high degree of plasticity, but retaining these memories demands protection against changes induced by further activity and experience. Models in which memories are stored through switch-like transitions in synaptic efficacy are good at storing but bad at retaining memories if these transitions are likely, and they are poor at storage but good at retention if they are unlikely. We construct and study a model in which each synapse has a cascade of states with different levels of plasticity, connected by metaplastic transitions. This cascade model combines high levels of memory storage with long retention times and significantly outperforms alternative models. As a result, we suggest that memory storage requires synapses with multiple states exhibiting dynamics over a wide range of timescales, and we suggest experimental tests of this hypothesis.

Adaptive surround modulation in cortical area MT.

X Huang — 2008-06-15T15:30:49-00:00

Neuron, Vol. 53, No. 5. (1 March 2007), pp. 761-770.

Visual motion perception relies on two opposing operations: integration and segmentation. Integration overcomes motion ambiguity in the visual image by spatial pooling of motion signals, whereas segmentation identifies differences between adjacent moving objects. For visual motion area MT, previous investigations have reported that stimuli in the receptive field surround, which do not elicit a response when presented alone, can nevertheless modulate responses to stimuli in the receptive field center. The directional tuning of this "surround modulation" has been found to be mainly antagonistic and hence consistent with segmentation. Here, we report that surround modulation in area MT can be either antagonistic or integrative depending upon the visual stimulus. Both types of modulation were delayed relative to response onset. Our results suggest that the dominance of antagonistic modulation in previous MT studies was due to stimulus choice and that segmentation and integration are achieved, in part, via adaptive surround modulation.

The Relationship between Nature of Social Change, Age, and Position of New Neurons and Their Survival in Adult Zebra Finch Brain

Einat Adar — 2008-06-06T17:54:58-00:00

J. Neurosci., Vol. 28, No. 20. (14 May 2008), pp. 5394-5400.

Some kinds of neurons are spontaneously recruited in the intact, healthy adult brain, but the variables that affect their survival are not always clear. We show that in caudal nidopallium of adult male zebra finches, the rostrocaudal position of newly recruited neurons, their age (1 vs 3 months), and the nature of social change (complex vs simple) after the neurons were born affect their survival. Greater social complexity promoted the survival of younger new neurons, and the demise of older ones; a less marked social change promoted the survival of older new neurons. These effects were position dependent. We suggest that functional correlations between new neuron recruitment/survival and its inferred benefit to the animal might be better perceived when taking into account the position of cells, their age at the time of life style changes, and the nature and magnitude of the life style change. 10.1523/JNEUROSCI.5706-07.2008

Temporal characteristics of audiovisual information processing.

GF Alpert — 2008-05-27T19:43:23-00:00

The Journal of neuroscience : the official journal of the Society for Neuroscience, Vol. 28, No. 20. (14 May 2008), pp. 5344-5349.

In complex natural environments, auditory and visual information often have to be processed simultaneously. Previous functional magnetic resonance imaging (fMRI) studies focused on the spatial localization of brain areas involved in audiovisual (AV) information processing, but the temporal characteristics of AV information flow in these regions remained unclear. In this study, we used fMRI and a novel information-theoretic approach to study the flow of AV sensory information. Subjects passively perceived sounds and images of objects presented either alone or simultaneously. Applying the measure of mutual information, we computed for each voxel the latency in which the blood oxygenation level-dependent signal had the highest information content about the preceding stimulus. The results indicate that, after AV stimulation, the earliest informative activity occurs in right Heschl's gyrus, left primary visual cortex, and the posterior portion of the superior temporal gyrus, which is known as a region involved in object-related AV integration. Informative activity in the anterior portion of superior temporal gyrus, middle temporal gyrus, right occipital cortex, and inferior frontal cortex was found at a later latency. Moreover, AV presentation resulted in shorter latencies in multiple cortical areas compared with isolated auditory or visual presentation. The results provide evidence for bottom-up processing from primary sensory areas into higher association areas during AV integration in humans and suggest that AV presentation shortens processing time in early sensory cortices.

Low-Frequency Local Field Potentials and Spikes in Primary Visual Cortex Convey Independent Visual Information

Andrei Belitski — 2008-06-02T10:17:41-00:00

J. Neurosci., Vol. 28, No. 22. (28 May 2008), pp. 5696-5709.

Local field potentials (LFPs) reflect subthreshold integrative processes that complement spike train measures. However, little is yet known about the differences between how LFPs and spikes encode rich naturalistic sensory stimuli. We addressed this question by recording LFPs and spikes from the primary visual cortex of anesthetized macaques while presenting a color movie. We then determined how the power of LFPs and spikes at different frequencies represents the visual features in the movie. We found that the most informative LFP frequency ranges were 1-8 and 60-100 Hz. LFPs in the range of 12-40 Hz carried little information about the stimulus, and may primarily reflect neuromodulatory inputs. Spike power was informative only at frequencies <12 Hz. We further quantified "signal correlations" (correlations in the trial-averaged power response to different stimuli) and "noise correlations" (trial-by-trial correlations in the fluctuations around the average) of LFPs and spikes recorded from the same electrode. We found positive signal correlation between high-gamma LFPs (60-100 Hz) and spikes, as well as strong positive signal correlation within high-gamma LFPs, suggesting that high-gamma LFPs and spikes are generated within the same network. LFPs <24 Hz shared strong positive noise correlations, indicating that they are influenced by a common source, such as a diffuse neuromodulatory input. LFPs <40 Hz showed very little signal and noise correlations with LFPs >40 Hz and with spikes, suggesting that low-frequency LFPs reflect neural processes that in natural conditions are fully decoupled from those giving rise to spikes and to high-gamma LFPs. 10.1523/JNEUROSCI.0009-08.2008

Effects of Synaptic Synchrony on the Neuronal Input-Output Relationship

Xiaoshen Li — 2008-06-06T17:26:17-00:00

Neural Comp., Vol. 20, No. 7. (1 July 2008), pp. 1717-1731.

The firing rate of individual neurons depends on the firing frequency of their distributed synaptic inputs, with linear and nonlinear relations subserving different computational functions. This letter explores the relationship between the degree of synchrony among excitatory synapses and the linearity of the response using detailed compartmental models of cortical pyramidal cells. Synchronous input resulted in a linear input-output relationship, while asynchronous stimulation yielded sub- and supraproportional outputs at low and high frequencies, respectively. The dependence of input-output linearity on synchrony was sigmoidal and considerably robust with respect to dendritic location, stimulus irregularity, and alteration of active and synaptic properties. Moreover, synchrony affected firing rate differently at lower and higher input frequencies. A reduced integrate-and-fire model suggested a mechanism explaining these results based on spatiotemporal integration, with fundamental implications relating synchrony to memory encoding.

Hierarchical structure and the prediction of missing links in networks

Aaron Clauset — 2008-04-30T19:31:59-00:00

Nature, Vol. 453, No. 7191., pp. 98-101.

Dendritic mechanisms controlling spike-timing-dependent synaptic plasticity.

BM Kampa — 2008-06-06T14:57:55-00:00

Trends in neurosciences, Vol. 30, No. 9. (September 2007), pp. 456-463.

The ability of neurons to modulate the strength of their synaptic connections has been shown to depend on the relative timing of pre- and postsynaptic action potentials. This form of synaptic plasticity, called spike-timing-dependent plasticity (STDP), has become an attractive model for learning at the single-cell level. Yet, despite its popularity in experimental and theoretical neuroscience, the influence of dendritic mechanisms in the induction of STDP has been largely overlooked. Several recent studies have investigated how active dendritic properties and synapse location within the dendritic tree influence STDP. These studies suggest the existence of learning rules that depend on firing mode and subcellular input location, adding unanticipated complexity to STDP. Here, we propose a new look at STDP that is focused on processing at the postsynaptic site in the dendrites, rather than on spike-timing at the cell body.

Spike-timing-dependent synaptic plasticity depends on dendritic location

Robert Froemke — 2005-03-10T03:18:11-00:00

Nature, Vol. 434, No. 7030. (10 March 2005), pp. 221-225.

Learning rules for spike timing-dependent plasticity depend on dendritic synapse location.

JJ Letzkus — 2006-10-31T22:03:37-00:00

J Neurosci, Vol. 26, No. 41. (11 October 2006), pp. 10420-10429.

Previous studies focusing on the temporal rules governing changes in synaptic strength during spike timing-dependent synaptic plasticity (STDP) have paid little attention to the fact that synaptic inputs are distributed across complex dendritic trees. During STDP, propagation of action potentials (APs) back to the site of synaptic input is thought to trigger plasticity. However, in pyramidal neurons, backpropagation of single APs is decremental, whereas high-frequency bursts lead to generation of distal dendritic calcium spikes. This raises the question whether STDP learning rules depend on synapse location and firing mode. Here, we investigate this issue at synapses between layer 2/3 and layer 5 pyramidal neurons in somatosensory cortex. We find that low-frequency pairing of single APs at positive times leads to a distance-dependent shift to long-term depression (LTD) at distal inputs. At proximal sites, this LTD could be converted to long-term potentiation (LTP) by dendritic depolarizations suprathreshold for BAC-firing or by high-frequency AP bursts. During AP bursts, we observed a progressive, distance-dependent shift in the timing requirements for induction of LTP and LTD, such that distal synapses display novel timing rules: they potentiate when inputs are activated after burst onset (negative timing) but depress when activated before burst onset (positive timing). These findings could be explained by distance-dependent differences in the underlying dendritic voltage waveforms driving NMDA receptor activation during STDP induction. Our results suggest that synapse location within the dendritic tree is a crucial determinant of STDP, and that synapses undergo plasticity according to local rather than global learning rules.

How the shape of pre- and postsynaptic signals can influence STDP: a biophysical model.

A Saudargiene — 2006-06-30T05:17:24-00:00

Neural Comput, Vol. 16, No. 3. (March 2004), pp. 595-625.

Spike-timing-dependent plasticity (STDP) is described by long-term potentiation (LTP), when a presynaptic event precedes a postsynaptic event, and by long-term depression (LTD), when the temporal order is reversed. In this article, we present a biophysical model of STDP based on a differential Hebbian learning rule (ISO learning). This rule correlates presynaptically the NMDA channel conductance with the derivative of the membrane potential at the synapse as the postsynaptic signal. The model is able to reproduce the generic STDP weight change characteristic. We find that (1) The actual shape of the weight change curve strongly depends on the NMDA channel characteristics and on the shape of the membrane potential at the synapse. (2) The typical antisymmetrical STDP curve (LTD and LTP) can become similar to a standard Hebbian characteristic (LTP only) without having to change the learning rule. This occurs if the membrane depolarization has a shallow onset and is long lasting. (3) It is known that the membrane potential varies along the dendrite as a result of the active or passive backpropagation of somatic spikes or because of local dendritic processes. As a consequence, our model predicts that learning properties will be different at different locations on the dendritic tree. In conclusion, such site-specific synaptic plasticity would provide a neuron with powerful learning capabilities.

Local learning rules: predicted influence of dendritic location on synaptic modification in spike-timing-dependent plasticity.

A Saudargiene — 2006-08-01T06:22:00-00:00

Biol Cybern, Vol. 92, No. 2. (February 2005), pp. 128-138.

Recent indirect experimental evidence suggests that synaptic plasticity changes along the dendrites of a neuron. Here we present a synaptic plasticity rule which is controlled by the properties of the pre- and postsynaptic signals. Using recorded membrane traces of back-propagating and dendritic spikes we demonstrate that LTP and LTD will depend specifically on the shape of the postsynaptic depolarization at a given dendritic site. We find that asymmetrical spike-timing-dependent plasticity (STDP) can be replaced by temporally symmetrical plasticity within physiologically relevant time windows if the postsynaptic depolarization rises shallow. Presynaptically the rule depends on the NMDA channel characteristic, and the model predicts that an increase in Mg(2+) will attenuate the STDP curve without changing its shape. Furthermore, the model suggests that the profile of LTD should be governed by the postsynaptic signal while that of LTP mainly depends on the presynaptic signal shape.

Synaptic modifications depend on synapse location and activity: a biophysical model of STDP.

A Saudargiene — 2008-04-28T22:50:21-00:00

Bio Systems, Vol. 79, No. 1-3. (r 2005), pp. 3-10.

In spike-timing-dependent plasticity (STDP) the synapses are potentiated or depressed depending on the temporal order and temporal difference of the pre- and post-synaptic signals. We present a biophysical model of STDP which assumes that not only the timing, but also the shapes of these signals influence the synaptic modifications. The model is based on a Hebbian learning rule which correlates the NMDA synaptic conductance with the post-synaptic signal at synaptic location as the pre- and post-synaptic quantities. As compared to a previous paper [Saudargiene, A., Porr, B., Worgotter, F., 2004. How the shape of pre- and post-synaptic signals can influence stdp: a biophysical model. Neural Comp.], here we show that this rule reproduces the generic STDP weight change curve by using real neuronal input signals and combinations of more than two (pre- and post-synaptic) spikes. We demonstrate that the shape of the STDP curve strongly depends on the shape of the depolarising membrane potentials, which induces learning. As these potentials vary at different locations of the dendritic tree, model predicts that synaptic changes are location dependent. The model is extended to account for the patterns of more than two spikes of the pre- and post-synaptic cells. The results show that STDP weight change curve is also activity dependent.

Perceptual learning and top-down influences in primary visual cortex

Wu Li — 2008-04-22T13:37:28-00:00

Nat Neurosci, Vol. 7, No. 6. (June 2004), pp. 651-657.

A biophysical model of synaptic plasticity and metaplasticity can account for the dynamics of the backward shift of hippocampal place fields.

Xintian Yu — 2008-06-06T14:21:06-00:00

Journal of neurophysiology (4 June 2008)

Hippocampal place cells in the rat undergo experience-dependent changes when the rat runs stereotyped routes. One such change, the backward shift of the place field center mass (COM), has been linked by previous modeling efforts to spike timing-dependent plasticity (STDP). However, these models did not account for the termination of the place field shift and they were based on an abstract implementation of STDP that ignores many of the features found in cortical plasticity. Here, instead of the abstract STDP model, we use a calcium-dependent plasticity (CaDP) learning rule that can account for many of the observed properties of cortical plasticity. We use the CaDP learning rule in combination with a model of metaplasticity to simulate place field dynamics. Without any major changes to the parameters of the original model, the present simulations account both for the initial rapid place field shift as well as for the subsequent slowing down of this shift. These results suggest that the CaDP model captures the essence of a general cortical mechanism of synaptic plasticity, which may underlie numerous forms of synaptic plasticity observed both in vivo and in vitro.

Subthreshold membrane-potential resonances shape spike-train patterns in the entorhinal cortex.

Tatiana A Engel — 2008-06-06T14:14:52-00:00

Journal of neurophysiology (21 May 2008)

Many neurons exhibit subthreshold membrane-potential resonances, such that the largest voltage responses occur at preferred stimulation frequencies. As subthreshold resonances are known to infuence the rhythmic activity at the network level, it is vital to understand how they affect spike generation on the single-cell level. We therefore investigated resonant as well as nonresonant neurons of rat entorhinal cortex. A minimal resonate-and-fire type model based on measured physiological parameters captures fundamental properties of neuronal firing statistics surprisingly well and helps to shed light on the mechanisms that shape spike patterns: (i) subthreshold resonance together with a spike-induced reset of subthreshold oscillations leads to spike clustering, (ii) spike-induced dynamics infuence the fne structure of interspike interval (ISI) distributions and are responsible for ISI correlations appearing at higher fring rates (3 Hz). Both mechanisms are likely to account for the specifc discharge characteristics of various cell types.

Activity-dependent development of axonal and dendritic delays, or, why synaptic transmission should be unreliable.

W Senn — 2008-06-04T10:54:36-00:00

Neural computation, Vol. 14, No. 3. (March 2002), pp. 583-619.

Systematic temporal relations between single neuronal activities or population activities are ubiquitous in the brain. No experimental evidence, however, exists for a direct modification of neuronal delays during Hebbian-type stimulation protocols. We show that in fact an explicit delay adaptation is not needed if one assumes that the synaptic strengths are modified according to the recently observed temporally asymmetric learning rule with the downregulating branch dominating the upregulating branch. During development, slow, unbiased fluctuations in the transmission time, together with temporally correlated network activity, may control neural growth and implicitly induce drifts in the axonal delays and dendritic latencies. These delays and latencies become optimally tuned in the sense that the synaptic response tends to peak in the soma of the postsynaptic cell if this is most likely to fire. The nature of the selection process requires unreliable synapses in order to give successful synapses an evolutionary advantage over the others. The width of the learning function also determines the preferred dendritic delay and the preferred width of the postsynaptic response. Hence, it may implicitly determine whether a synaptic connection provides a precisely timed or a broadly tuned "contextual" signal.

Optimality model of unsupervised spike-timing-dependent plasticity: synaptic memory and weight distribution.

T Toyoizumi — 2008-06-04T10:48:15-00:00

Neural computation, Vol. 19, No. 3. (March 2007), pp. 639-671.

We studied the hypothesis that synaptic dynamics is controlled by three basic principles: (1) synapses adapt their weights so that neurons can effectively transmit information, (2) homeostatic processes stabilize the mean firing rate of the postsynaptic neuron, and (3) weak synapses adapt more slowly than strong ones, while maintenance of strong synapses is costly. Our results show that a synaptic update rule derived from these principles shares features, with spike-timing-dependent plasticity, is sensitive to correlations in the input and is useful for synaptic memory. Moreover, input selectivity (sharply tuned receptive fields) of postsynaptic neurons develops only if stimuli with strong features are presented. Sharply tuned neurons can coexist with unselective ones, and the distribution of synaptic weights can be unimodal or bimodal. The formulation of synaptic dynamics through an optimality criterion provides a simple graphical argument for the stability of synapses, necessary for synaptic memory.

Self-tuning of neural circuits through short-term synaptic plasticity.

D Sussillo — 2008-06-04T10:46:05-00:00

Journal of neurophysiology, Vol. 97, No. 6. (June 2007), pp. 4079-4095.

Numerous experimental data show that cortical networks of neurons are not silent in the absence of external inputs, but rather maintain a low spontaneous firing activity. This aspect of cortical networks is likely to be important for their computational function, but is hard to reproduce in models of cortical circuits of neurons because the low-activity regime is inherently unstable. Here we show-through theoretical analysis and extensive computer simulations-that short-term synaptic plasticity endows models of cortical circuits with a remarkable stability in the low-activity regime. This short-term plasticity works as a homeostatic mechanism that stabilizes the overall activity level in spite of drastic changes in external inputs and internal circuit properties, while preserving reliable transient responses to signals. The contribution of synaptic dynamics to this stability can be predicted on the basis of general principles from control theory.

Type I membranes, phase resetting curves, and synchrony.

B Ermentrout — 2008-05-28T18:39:56-00:00

Neural computation, Vol. 8, No. 5. (1 July 1996), pp. 979-1001.

Type I membrane oscillators such as the Connor model (Connor et al. 1977) and the Morris-Lecar model (Morris and Lecar 1981) admit very low frequency oscillations near the critical applied current. Hansel et al. (1995) have numerically shown that synchrony is difficult to achieve with these models and that the phase resetting curve is strictly positive. We use singular perturbation methods and averaging to show that this is a general property of Type I membrane models. We show in a limited sense that so called Type II resetting occurs with models that obtain rhythmicity via a Hopf bifurcation. We also show the differences between synapses that act rapidly and those that act slowly and derive a canonical form for the phase interactions.

The influence of limit cycle topology on the phase resetting curve.

SA Oprisan — 2008-05-28T18:37:48-00:00

Neural computation, Vol. 14, No. 5. (May 2002), pp. 1027-1057.

Understanding the phenomenology of phase resetting is an essential step toward developing a formalism for the analysis of circuits composed of bursting neurons that receive multiple, and sometimes overlapping, inputs. If we are to use phase-resetting methods to analyze these circuits, we can either generate phase-resetting curves (PRCs) for all possible inputs and combinations of inputs, or we can develop an understanding of how to construct PRCs for arbitrary perturbations of a given neuron. The latter strategy is the goal of this study. We present a geometrical derivation of phase resetting of neural limit cycle oscillators in response to short current pulses. A geometrical phase is defined as the distance traveled along the limit cycle in the appropriate phase space. The perturbations in current are treated as displacements in the direction corresponding to membrane voltage. We show that for type I oscillators, the direction of a perturbation in current is nearly tangent to the limit cycle; hence, the projection of the displacement in voltage onto the limit cycle is sufficient to give the geometrical phase resetting. In order to obtain the phase resetting in terms of elapsed time or temporal phase, a mapping between geometrical and temporal phase is obtained empirically and used to make the conversion. This mapping is shown to be an invariant of the dynamics. Perturbations in current applied to type II oscillators produce significant normal displacements from the limit cycle, so the difference in angular velocity at displaced points compared to the angular velocity on the limit cycle must be taken into account. Empirical attempts to correct for differences in angular velocity (amplitude versus phase effects in terms of a circular coordinate system) during relaxation back to the limit cycle achieved some success in the construction of phase-resetting curves for type II model oscillators. The ultimate goal of this work is the extension of these techniques to biological circuits comprising type II neural oscillators, which appear frequently in identified central pattern-generating circuits.

Dynamics from a time series: can we extract the phase resetting curve from a time series?

SA Oprisan — 2008-05-28T18:35:41-00:00

Biophysical journal, Vol. 84, No. 5. (May 2003), pp. 2919-2928.

Recordings of the membrane potential from a bursting neuron were used to reconstruct the phase curve for that neuron for a limited set of perturbations. These perturbations were inhibitory synaptic conductance pulses able to shift the membrane potential below the most hyperpolarized level attained in the free running mode. The extraction of the phase resetting curve from such a one-dimensional time series requires reconstruction of the periodic activity in the form of a limit cycle attractor. Resetting was found to have two components. In the first component, if the pulse was applied during a burst, the burst was truncated, and the time until the next burst was shortened in a manner predicted by movement normal to the limit cycle. By movement normal to the limit cycle, we mean a switch between two well-defined solution branches of a relaxation-like oscillator in a hysteretic manner enabled by the existence of a singular dominant slow process (variable). In the second component, the onset of the burst was delayed until the end of the hyperpolarizing pulse. Thus, for the pulse amplitudes we studied, resetting was independent of amplitude but increased linearly with pulse duration. The predicted and the experimental phase resetting curves for a pyloric dilator neuron show satisfactory agreement. The method was applied to only one pulse per cycle, but our results suggest it could easily be generalized to accommodate multiple inputs.

A stochastic method to predict the consequence of arbitrary forms of spike-timing-dependent plasticity.

H Câteau — 2008-05-28T17:51:26-00:00

Neural computation, Vol. 15, No. 3. (March 2003), pp. 597-620.

Synapses in various neural preparations exhibit spike-timing-dependent plasticity (STDP) with a variety of learning window functions. The window functions determine the magnitude and the polarity of synaptic change according to the time difference of pre- and postsynaptic spikes. Numerical experiments revealed that STDP learning with a single-exponential window function resulted in a bimodal distribution of synaptic conductances as a consequence of competition between synapses. A slightly modified window function, however, resulted in a unimodal distribution rather than a bimodal distribution. Since various window functions have been observed in neural preparations, we develop a rigorous mathematical method to calculate the conductance distribution for any given window function. Our method is based on the Fokker-Planck equation to determine the conductance distribution and on the Ornstein-Uhlenbeck process to characterize the membrane potential fluctuations. Demonstrating that our method reproduces the known quantitative results of STDP learning, we apply the method to the type of STDP learning found recently in the CA1 region of the rat hippocampus. We find that this learning can result in nearly optimized competition between synapses. Meanwhile, we find that the type of STDP learning found in the cerebellum-like structure of electric fish can result in all-or-none synapses: either all the synaptic conductances are maximized, or none of them becomes significantly large. Our method also determines the window function that optimizes synaptic competition.

Equilibrium Properties of Temporally Asymmetric Hebbian Plasticity

Jonathan Rubin — 2008-05-28T17:37:47-00:00

Physical Review Letters, Vol. 86, No. 2. (January 2001), 364.

Temporal compression mediated by short-term synaptic plasticity

Christian Leibold — 2008-03-17T10:49:54-00:00

Proceedings of the National Academy of Sciences (12 March 2008), 0708711105.

Time scales of cortical neuronal dynamics range from few milliseconds to hundreds of milliseconds. In contrast, behavior occurs on the time scale of seconds or longer. How can behavioral time then be neuronally represented in cortical networks? Here, using electrophysiology and modeling, we offer a hypothesis on how to bridge the gap between behavioral and cellular time scales. The core idea is to use a long time constant of decay of synaptic facilitation to translate slow behaviorally induced temporal correlations into a distribution of synaptic response amplitudes. These amplitudes can then be transferred to a sequence of action potentials in a population of neurons. These sequences provide temporal correlations on a millisecond time scale that are able to induce persistent synaptic changes. As a proof of concept, we provide simulations of a neuron that learns to discriminate temporal patterns on a time scale of seconds by synaptic learning rules with a millisecond memory buffer. We find that the conversion from synaptic amplitudes to millisecond correlations can be strongly facilitated by subthreshold oscillations both in terms of information transmission and success of learning. 10.1073/pnas.0708711105

Modeling synaptic plasticity in conjuction with the timing of pre- and postsynaptic action potentials.

WM Kistler — 2008-04-28T22:07:58-00:00

Neural computation, Vol. 12, No. 2. (February 2000), pp. 385-405.

We present a spiking neuron model that allows for an analytic calculation of the correlations between pre- and postsynaptic spikes. The neuron model is a generalization of the integrate-and-fire model and equipped with a probabilistic spike-triggering mechanism. We show that under certain biologically plausible conditions, pre- and postsynaptic spike trains can be described simultaneously as an inhomogeneous Poisson process. Inspired by experimental findings, we develop a model for synaptic long-term plasticity that relies on the relative timing of pre- and post-synaptic action potentials. Being given an input statistics, we compute the stationary synaptic weights that result from the temporal correlations between the pre- and postsynaptic spikes. By means of both analytic calculations and computer simulations, we show that such a mechanism of synaptic plasticity is able to strengthen those input synapses that convey precisely timed spikes at the expense of synapses that deliver spikes with a broad temporal distribution. This may be of vital importance for any kind of information processing based on spiking neurons and temporal coding.

Synaptic modification by correlated activity: Hebb's postulate revisited.

G Bi — 2006-08-04T05:48:15-00:00

Annu Rev Neurosci, Vol. 24 (2001), pp. 139-166.

Correlated spiking of pre- and postsynaptic neurons can result in strengthening or weakening of synapses, depending on the temporal order of spiking. Recent findings indicate that there are narrow and cell type-specific temporal windows for such synaptic modification and that the generally accepted input- (or synapse-) specific rule for modification appears not to be strictly adhered to. Spike timing-dependent modifications, together with selective spread of synaptic changes, provide a set of cellular mechanisms that are likely to be important for the development and functioning of neural networks. When an axon of cell A is near enough to excite cell B or repeatedly or consistently takes part in firing it, some growth or metabolic change takes place in one or both cells such that A's efficiency, as one of the cells firing B, is increased.

Spike-timing-dependent plasticity for neurons with recurrent connections

Burkitt — 2007-06-07T22:18:05-00:00

Biological Cybernetics, Vol. 96, No. 5. (May 2007), pp. 533-546.

Formation of temporal-feature maps by axonal propagation of synaptic learning.

R Kempter — 2006-02-12T06:59:05-00:00

Proc Natl Acad Sci U S A, Vol. 98, No. 7. (27 March 2001), pp. 4166-4171.

Computational maps are of central importance to a neuronal representation of the outside world. In a map, neighboring neurons respond to similar sensory features. A well studied example is the computational map of interaural time differences (ITDs), which is essential to sound localization in a variety of species and allows resolution of ITDs of the order of 10 micros. Nevertheless, it is unclear how such an orderly representation of temporal features arises. We address this problem by modeling the ontogenetic development of an ITD map in the laminar nucleus of the barn owl. We show how the owl's ITD map can emerge from a combined action of homosynaptic spike-based Hebbian learning and its propagation along the presynaptic axon. In spike-based Hebbian learning, synaptic strengths are modified according to the timing of pre- and postsynaptic action potentials. In unspecific axonal learning, a synapse's modification gives rise to a factor that propagates along the presynaptic axon and affects the properties of synapses at neighboring neurons. Our results indicate that both Hebbian learning and its presynaptic propagation are necessary for map formation in the laminar nucleus, but the latter can be orders of magnitude weaker than the former. We argue that the algorithm is important for the formation of computational maps, when, in particular, time plays a key role.

Intrinsic stabilization of output rates by spike-based Hebbian learning.

R Kempter — 2008-05-28T16:33:45-00:00

Neural computation, Vol. 13, No. 12. (December 2001), pp. 2709-2741.

We study analytically a model of long-term synaptic plasticity where synaptic changes are triggered by presynaptic spikes, postsynaptic spikes, and the time differences between presynaptic and postsynaptic spikes. The changes due to correlated input and output spikes are quantified by means of a learning window. We show that plasticity can lead to an intrinsic stabilization of the mean firing rate of the postsynaptic neuron. Subtractive normalization of the synaptic weights (summed over all presynaptic inputs converging on a postsynaptic neuron) follows if, in addition, the mean input rates and the mean input correlations are identical at all synapses. If the integral over the learning window is positive, firing-rate stabilization requires a non-Hebbian component, whereas such a component is not needed if the integral of the learning window is negative. A negative integral corresponds to anti-Hebbian learning in a model with slowly varying firing rates. For spike-based learning, a strict distinction between Hebbian and anti-Hebbian rules is questionable since learning is driven by correlations on the timescale of the learning window. The correlations between presynaptic and postsynaptic firing are evaluated for a piecewise-linear Poisson model and for a noisy spiking neuron model with refractoriness. While a negative integral over the learning window leads to intrinsic rate stabilization, the positive part of the learning window picks up spatial and temporal correlations in the input.

Hebbian learning and spiking neurons

Richard Kempter — 2008-05-28T16:31:40-00:00

Physical Review E, Vol. 59, No. 4. (1 April 1999), 4498.

Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type.

GQ Bi — 2006-08-04T04:50:58-00:00

J Neurosci, Vol. 18, No. 24. (15 December 1998), pp. 10464-10472.

In cultures of dissociated rat hippocampal neurons, persistent potentiation and depression of glutamatergic synapses were induced by correlated spiking of presynaptic and postsynaptic neurons. The relative timing between the presynaptic and postsynaptic spiking determined the direction and the extent of synaptic changes. Repetitive postsynaptic spiking within a time window of 20 msec after presynaptic activation resulted in long-term potentiation (LTP), whereas postsynaptic spiking within a window of 20 msec before the repetitive presynaptic activation led to long-term depression (LTD). Significant LTP occurred only at synapses with relatively low initial strength, whereas the extent of LTD did not show obvious dependence on the initial synaptic strength. Both LTP and LTD depended on the activation of NMDA receptors and were absent in cases in which the postsynaptic neurons were GABAergic in nature. Blockade of L-type calcium channels with nimodipine abolished the induction of LTD and reduced the extent of LTP. These results underscore the importance of precise spike timing, synaptic strength, and postsynaptic cell type in the activity-induced modification of central synapses and suggest that Hebb's rule may need to incorporate a quantitative consideration of spike timing that reflects the narrow and asymmetric window for the induction of synaptic modification.

Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs.

H Markram — 2006-09-22T09:03:57-00:00

Science, Vol. 275, No. 5297. (10 January 1997), pp. 213-215.

Activity-driven modifications in synaptic connections between neurons in the neocortex may occur during development and learning. In dual whole-cell voltage recordings from pyramidal neurons, the coincidence of postsynaptic action potentials (APs) and unitary excitatory postsynaptic potentials (EPSPs) was found to induce changes in EPSPs. Their average amplitudes were differentially up- or down-regulated, depending on the precise timing of postsynaptic APs relative to EPSPs. These observations suggest that APs propagating back into dendrites serve to modify single active synaptic connections, depending on the pattern of electrical activity in the pre- and postsynaptic neurons.

Spike-timing-dependent plasticity of neocortical excitatory synapses on inhibitory interneurons depends on target cell type.

JT Lu — 2007-09-15T08:43:46-00:00

J Neurosci, Vol. 27, No. 36. (5 September 2007), pp. 9711-9720.

Repetitive correlated spiking can induce long-term potentiation (LTP) and long-term depression (LTD) of many excitatory synapses on glutamatergic neurons, in a manner that depends on the timing of presynaptic and postsynaptic spiking. However, it is mostly unknown whether and how such spike-timing-dependent plasticity (STDP) operates at neocortical excitatory synapses on inhibitory interneurons, which have diverse physiological and morphological characteristics. In this study, we found that these synapses exhibit target-cell-dependent STDP. In layer 2/3 of the somatosensory cortex, the pyramidal cell (PC) forms divergent synapses on fast spiking (FS) and low-threshold spiking (LTS) interneurons that exhibit short-term synaptic depression and facilitation in response to high-frequency stimulation, respectively. At PC-LTS synapses, repetitive correlated spiking induced LTP or LTD, depending on the timing of presynaptic and postsynaptic spiking. However, regardless of the timing and frequency of spiking, correlated activity induced only LTD at PC-FS synapses. This target-cell-specific STDP was not caused by the difference in the short-term plasticity between these two types of synapses. Activation of postsynaptic NMDA subtype of glutamate receptors (NMDARs) was required for LTP induction at PC-LTS synapses, whereas activation of metabotropic glutamate receptors was required for LTD induction at both PC-LTS and PC-FS synapses. Additional analysis of synaptic currents suggests that LTP and LTD of PC-LTS synapses, but not LTD of PC-FS synapses, involves presynaptic modifications. Such dependence of both the induction and expression of STDP on the type of postsynaptic interneurons may contribute to differential processing and storage of information in cortical local circuits.

Spike Timing-Dependent Plasticity: From Synapse to Perception

Yang Dan — 2006-07-03T10:46:44-00:00

Physiol. Rev., Vol. 86, No. 3. (1 July 2006), pp. 1033-1048.

Information in the nervous system may be carried by both the rate and timing of neuronal spikes. Recent findings of spike timing-dependent plasticity (STDP) have fueled the interest in the potential roles of spike timing in processing and storage of information in neural circuits. Induction of long-term potentiation (LTP) and long-term depression (LTD) in a variety of in vitro and in vivo systems has been shown to depend on the temporal order of pre- and postsynaptic spiking. Spike timing-dependent modification of neuronal excitability and dendritic integration was also observed. Such STDP at the synaptic and cellular level is likely to play important roles in activity-induced functional changes in neuronal receptive fields and human perception. 10.1152/physrev.00030.2005

Spike timing-dependent plasticity of neural circuits.

Y Dan — 2006-07-03T08:03:06-00:00

Neuron, Vol. 44, No. 1. (30 September 2004), pp. 23-30.

Recent findings of spike timing-dependent plasticity (STDP) have stimulated much interest among experimentalists and theorists. Beyond the traditional correlation-based Hebbian plasticity, STDP opens up new avenues for understanding information coding and circuit plasticity that depend on the precise timing of neuronal spikes. Here we summarize experimental characterization of STDP at various synapses, the underlying cellular mechanisms, and the associated changes in neuronal excitability and dendritic integration. We also describe STDP in the context of complex spike patterns and its dependence on the dendritic location of the synapse. Finally, we discuss timing-dependent modification of neuronal receptive fields and human visual perception and the computational significance of STDP as a synaptic learning rule.

Phenomenological models of synaptic plasticity based on spike timing

Morrison — 2008-05-26T18:06:54-00:00

Biological Cybernetics, Vol. 98, No. 6. (June 2008), pp. 459-478.

Inhibition, not excitation, is the key to multimodal sensory integration

Friedel — 2008-05-26T18:06:53-00:00

Biological Cybernetics, Vol. 98, No. 6. (June 2008), pp. 597-618.

Statistical Analysis of Circular Data

Nicholas Fisher — 2005-11-03T02:31:12-00:00

(12 October 1995)

A unified, up-to-date account of circular data-handling techniques, useful throughout science.