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M Tsodyks

Publications and source records attributed to M Tsodyks.

13 recordsLinked to original sources
PubMed2000

Synchrony generation in recurrent networks with frequency-dependent synapses.

Throughout the neocortex, groups of neurons have been found to fire synchronously on the time scale of several milliseconds. This near coincident firing of neurons could coordinate the multifaceted information of different features of a stimulus. The mechanisms of generating such synchrony are not clear. We simulated the activity of a population of excitatory and inhibitory neurons randomly interconnected into a recurrent network via synapses that display temporal dynamics in their transmission; surprisingly, we found a behavior of the network where action potential activity spontaneously self-organized to produce highly synchronous bursts involving virtually the entire network. These population bursts were also triggered by stimuli to the network in an all-or-none manner. We found that the particular intensities of the external stimulus to specific neurons were crucial to evoke population bursts. This topographic sensitivity therefore depends on the spectrum of basal discharge rates across the population and not on the anatomical individuality of the neurons, because this was random. These results suggest that networks in which neurons are even randomly interconnected via frequency-dependent synapses could exhibit a novel form of reflex response that is sensitive to the nature of the stimulus as well as the background spontaneous activity.

Action Potentials↗
PubMed1999

Linking spontaneous activity of single cortical neurons and the underlying functional architecture.

The relation between the activity of a single neocortical neuron and the dynamics of the network in which it is embedded was explored by single-unit recordings and real-time optical imaging. The firing rate of a spontaneously active single neuron strongly depends on the instantaneous spatial pattern of ongoing population activity in a large cortical area. Very similar spatial patterns of population activity were observed both when the neuron fired spontaneously and when it was driven by its optimal stimulus. The evoked patterns could be used to reconstruct the spontaneous activity of single neurons.

Action Potentials↗
PubMed1999

Relation between retinotopical and orientation maps in visual cortex.

A recent study of cat visual cortex reported abrupt changes in the positions of the receptive fields of adjacent neurons whose preferred orientations strongly differed (Das & Gilbert, 1997). Using a simple cortical model, we show that this covariation of discontinuities in maps of orientation preference and local distortions in maps of visual space reflects collective effects of the lateral cortical feedback.

Animals↗
PubMed1999

Attractor neural network models of spatial maps in hippocampus.

Hippocampal pyramidal neurons in rats are selectively activated at specific locations in an environment (O'Keefe and Dostrovsky, Brain Res 1971;34:171-175). Different cells are active in different places, therefore providing a faithful representation of the environment in which every spatial location is mapped to a particular population state of activity of place cells (Wilson and McNaughton, Science 1993;261:1055-1058; Zhang et al., J Neurosci 1998;79:1017-1044). We describe a theory of the hippocampus, according to which the map results from the cooperative dynamics of network, in which the strength of synaptic interaction between the neurons depends on the distance between their place fields. This synaptic structure guarantees that the network possesses a quasi-continuous set of stable states (attractors) that are localized in the space of neuronal variables reflecting their synaptic interactions, rather than their physical location in the hippocampus. As a consequence of the stable states, the network can exhibit place selective activity even without relying on input from external sensory cues.

Animals↗
PubMed1998

Reading neuronal synchrony with depressing synapses.

A recent experiment showed that neurons in the primary auditory cortex of the monkey do not change their mean firing rate during an ongoing tone stimulus. The only change was an enhanced correlation among the individual spike trains during the tone. We show that there is an easy way to extract this coherence information in the cortical cell population by projecting the spike trains through depressing synapses onto a postsynaptic neuron.

Acoustic Stimulation↗
PubMed1998

Neural networks with dynamic synapses.

Transmission across neocortical synapses depends on the frequency of presynaptic activity (Thomson & Deuchars, 1994). Interpyramidal synapses in layer V exhibit fast depression of synaptic transmission, while other types of synapses exhibit facilitation of transmission. To study the role of dynamic synapses in network computation, we propose a unified phenomenological model that allows computation of the postsynaptic current generated by both types of synapses when driven by an arbitrary pattern of action potential (AP) activity in a presynaptic population. Using this formalism, we analyze different regimes of synaptic transmission and demonstrate that dynamic synapses transmit different aspects of the presynaptic activity depending on the average presynaptic frequency. The model also allows for derivation of mean-field equations, which govern the activity of large, interconnected networks. We show that the dynamics of synaptic transmission results in complex sets of regular and irregular regimes of network activity.

Action Potentials↗
PubMed1998

Differential signaling via the same axon of neocortical pyramidal neurons.

The nature of information stemming from a single neuron and conveyed simultaneously to several hundred target neurons is not known. Triple and quadruple neuron recordings revealed that each synaptic connection established by neocortical pyramidal neurons is potentially unique. Specifically, synaptic connections onto the same morphological class differed in the numbers and dendritic locations of synaptic contacts, their absolute synaptic strengths, as well as their rates of synaptic depression and recovery from depression. The same axon of a pyramidal neuron innervating another pyramidal neuron and an interneuron mediated frequency-dependent depression and facilitation, respectively, during high frequency discharges of presynaptic action potentials, suggesting that the different natures of the target neurons underlie qualitative differences in synaptic properties. Facilitating-type synaptic connections established by three pyramidal neurons of the same class onto a single interneuron, were all qualitatively similar with a combination of facilitation and depression mechanisms. The time courses of facilitation and depression, however, differed for these convergent connections, suggesting that different pre-postsynaptic interactions underlie quantitative differences in synaptic properties. Mathematical analysis of the transfer functions of frequency-dependent synapses revealed supra-linear, linear, and sub-linear signaling regimes in which mixtures of presynaptic rates, integrals of rates, and derivatives of rates are transferred to targets depending on the precise values of the synaptic parameters and the history of presynaptic action potential activity. Heterogeneity of synaptic transfer functions therefore allows multiple synaptic representations of the same presynaptic action potential train and suggests that these synaptic representations are regulated in a complex manner. It is therefore proposed that differential signaling is a key mechanism in neocortical information processing, which can be regulated by selective synaptic modifications.

Animals↗
PubMed1997

Excitatory-inhibitory network in the visual cortex: psychophysical evidence.

At early stages in visual processing cells respond to local stimuli with specific features such as orientation and spatial frequency. Although the receptive fields of these cells have been thought to be local and independent, recent physiological and psychophysical evidence has accumulated, indicating that the cells participate in a rich network of local connections. Thus, these local processing units can integrate information over much larger parts of the visual field; the pattern of their response to a stimulus apparently depends on the context presented. To explore the pattern of lateral interactions in human visual cortex under different context conditions we used a novel chain lateral masking detection paradigm, in which human observers performed a detection task in the presence of different length chains of high-contrast-flanked Gabor signals. The results indicated a nonmonotonic relation of the detection threshold with the number of flankers. Remote flankers had a stronger effect on target detection when the space between them was filled with other flankers, indicating that the detection threshold is caused by dynamics of large neuronal populations in the neocortex, with a major interplay between excitation and inhibition. We considered a model of the primary visual cortex as a network consisting of excitatory and inhibitory cell populations, with both short- and long-range interactions. The model exhibited a behavior similar to the experimental results throughout a range of parameters. Experimental and modeling results indicated that long-range connections play an important role in visual perception, possibly mediating the effects of context.

Feedback↗
PubMed1996

Redistribution of synaptic efficacy between neocortical pyramidal neurons.

Experience-dependent potentiation and depression of synaptic strength has been proposed to subserve learning and memory by changing the gain of signals conveyed between neurons. Here we examine synaptic plasticity between individual neocortical layer-5 pyramidal neurons. We show that an increase in the synaptic response, induced by pairing action-potential activity in pre- and postsynaptic neurons, was only observed when synaptic input occurred at low frequencies. This frequency-dependent increase in synaptic responses arises because of a redistribution of the available synaptic efficacy and not because of an increase in the efficacy. Redistribution of synaptic efficacy could represent a mechanism to change the content, rather than the gain, of signals conveyed between neurons.

Action Potentials↗
PubMed1996

Redistribution of synaptic efficacy: a mechanism to generate infinite synaptic input diversity from a homogeneous population of neurons without changing absolute synaptic efficacies.

Changing the reliability of neurotransmitter release results in a change in the efficacy of low frequency synaptic transmission and in the rate of high frequency synaptic depression thus it can not cause an uniform change in strength of synapses and instead results in a change in the dynamics of synaptic transmission referred to as 'redistribution of synaptic efficacy' (RSE). Since the change in synaptic transmission associated with RSE depends on the history of action potential activity it is concluded that RSE serves as a mechanism to generate a potentially infinite diversity of synaptic input.

Action Potentials↗
PubMedDate not supplied

Information processing with frequency-dependent synaptic connections.

The efficacy of synaptic transmission between two neurons changes as a function of the history of previous activations of the synaptic connection. This history dependence can be characterized by examining the dependence of transmission on the frequency of stimulation. In this framework synaptic plasticity can also be examined in terms of changes in the frequency dependence of transmission and not merely in terms of synaptic strength which constitutes only a linear scaling mechanism. Recent work shows that the frequency dependence of transmission determines the content of information transmitted between neurons and that synaptic modifications can change the content of information transmitted. Multipatch-clamp recordings revealed that the frequency dependence of transmission is potentially unique for each synaptic connection made by a single axon and that the class of pre-postsynaptic neuron determines the class of frequency dependence (activity independent), while the unique activity relationship between any two neurons could determine the precise values of the parameters within a specific class (activity dependent). The content of information transmitted between neurons is also formalized to provide synaptic transfer functions which can be used to determine the role of the synaptic connection within a network of neurons. It is proposed that deriving synaptic transfer functions is crucial in order to understand the link between synaptic transmission and information processing within networks of neurons and to understand the link between synaptic plasticity and learning and memory.

Algorithms↗
PubMedDate not supplied

Potential for multiple mechanisms, phenomena and algorithms for synaptic plasticity at single synapses.

Recent experimental evidence indicates that in the neocortex, the manner in which each synapse releases neurotransmitter in response to trains of presynaptic action potentials is potentially unique. These unique transmission characteristics arise because of a large heterogeneity in various synaptic properties that determine frequency dependence of transmission such as those governing the rates of synaptic depression and facilitation. A theoretical analysis was therefore undertaken to explore the phenomenologies of changes in the values of these synaptic parameters. The results illustrate how the change in any one of several synaptic parameters produces a distinctive effect on synaptic transmission and how these distinctive effects can point to the most likely biophysical mechanisms. These results could therefore be useful in studies of synaptic plasticity in order to obtain a full characterization of the phenomenologies of synaptic modifications and to isolate potential biophysical mechanisms. Based on this theoretical analysis and experimental data, it is proposed that there exists multiple mechanisms, phenomena and algorithms for synaptic plasticity at single synapses. Finally, it is shown that the impact of changing the values of synaptic parameters depends on the values of the other parameters. This may indicate that the various mechanisms, phenomena and algorithms are interlinked in a 'synaptic plasticity code'.

Algorithms↗