Per Jesper Sjöström

Highly nonrandom features of synaptic connectivity in local cortical circuits.

How different is local cortical circuitry from a random network? To answer this question, we probed synaptic connections with several hundred simultaneous quadruple whole-cell recordings from layer 5 pyramidal neurons in the rat visual cortex. Analysis of this dataset revealed several nonrandom features in synaptic connectivity. We confirmed previous reports that bidirectional connections are more common than expected in a random network. We found that several highly clustered three-neuron connectivity patterns are overrepresented, suggesting that connections tend to cluster together. We also analyzed synaptic connection strength as defined by the peak excitatory postsynaptic potential amplitude. We found that the distribution of synaptic connection strength differs significantly from the Poisson distribution and can be fitted by a lognormal distribution. Such a distribution has a heavier tail and implies that synaptic weight is concentrated among few synaptic connections. In addition, the strengths of synaptic connections sharing pre- or postsynaptic neurons are correlated, implying that strong connections are even more clustered than the weak ones. Therefore, the local cortical network structure can be viewed as a skeleton of stronger connections in a sea of weaker ones. Such a skeleton is likely to play an important role in network dynamics and should be investigated further.

Neocortical LTD via Coincident Activation of Presynaptic NMDA and Cannabinoid Receptors

There is a consensus that NMDA receptors (NMDARs) detect coincident pre- and postsynaptic activity during induction of long-term potentiation (LTP), but their role in timing-dependent long-term depression (tLTD) is unclear. We examine tLTD in neocortical layer 5 (L5) pyramidal pairs and find that tLTD is expressed presynaptically, implying retrograde signaling. CB1 agonists produce depression that mimics and occludes tLTD. This agonist-induced LTD requires presynaptic activity and NMDAR activation, but not postsynaptic Ca(2+) influx. Further experiments demonstrate the existence of presynaptic NMDARs that underlie the presynaptic activity dependence. Finally, manipulating cannabinoid breakdown alters the temporal window for tLTD. In conclusion, tLTD requires simultaneous activation of presynaptic NMDA and CB1 receptors. This novel form of coincidence detection may explain the temporal window of tLTD and may also impart synapse specificity to cannabinoid retrograde signaling.

A Cooperative Switch Determines the Sign of Synaptic Plasticity in Distal Dendrites of Neocortical Pyramidal Neurons

Pyramidal neurons in the cerebral cortex span multiple cortical layers. How the excitable properties of pyramidal neuron dendrites allow these neurons to both integrate activity and store associations between different layers is not well understood, but is thought to rely in part on dendritic backpropagation of action potentials. Here we demonstrate that the sign of synaptic plasticity in neocortical pyramidal neurons is regulated by the spread of the backpropagating action potential to the synapse. This creates a progressive gradient between LTP and LTD as the distance of the synaptic contacts from the soma increases. At distal synapses, cooperative synaptic input or dendritic depolarization can switch plasticity between LTD and LTP by boosting backpropagation of action potentials. This activity-dependent switch provides a mechanism for associative learning across different neocortical layers that process distinct types of information.

Spike timing, calcium signals and synaptic plasticity

Plasticity at central synapses depends critically on the timing of presynaptic and postsynaptic action potentials. Key initial steps in synaptic plasticity involve the back-propagation of action potentials into the dendritic tree and calcium influx that depends nonlinearly on the action potential and synaptic input. These initial steps are now better understood. In addition, recent studies of processes as diverse as gene expression and channel inactivation suggest that responses to calcium transients depend not only their amplitude, but on their time course and on the location of their origin.

A History of Spike-Timing-Dependent Plasticity

How learning and memory is achieved in the brain is a central question in neuroscience. Key to today’s research into information storage in the brain is the concept of synaptic plasticity, a notion that has been heavily influenced by Hebbs (1949) postulate. Hebb conjectured that repeatedly and persistently co-active cells should increase connective strength among populations of interconnected neurons as a means of storing a memory trace, also known as an engram. Hebb certainly was not the first to make such a conjecture, as we show in this history. Nevertheless, literally thousands of studies into the classical frequency-dependent paradigm of cellular learning rules were directly inspired by the Hebbian postulate. But in more recent years, a novel concept in cellular learning has emerged, where temporal order instead of frequency is emphasized. This new learning paradigm – known as spike-timing-dependent plasticity (STDP) – has rapidly gained tremendous interest, perhaps because of its combination of elegant simplicity, biological plausibility, and computational power. But what are the roots of today’s STDP concept? Here, we discuss several centuries of diverse thinking, beginning with philosophers such as Aristotle, Locke, and Ribot, traversing, e.g., Lugaro’s plasticità and Rosenblatt’s perceptron, and culminating with the discovery of STDP. We highlight interactions between theoretical and experimental fields, showing how discoveries sometimes occurred in parallel, seemingly without much knowledge of the other field, and sometimes via concrete back-and-forth communication. We point out where the future directions may lie, which includes interneuron STDP, the functional impact of STDP, its mechanisms and its neuromodulatory regulation, and the linking of STDP to the developmental formation and continuous plasticity of neuronal networks.

A proportional but slower NMDA potentiation follows AMPA potentiation in LTP

Most excitatory glutamatergic synapses contain both AMPA and NMDA receptors, but whether these receptors are regulated together or independently during synaptic plasticity has been controversial. Although long-term potentiation (LTP) is thought to selectively enhance AMPA currents and alter the NMDA-to-AMPA ratio, this ratio is well conserved across synapses onto the same neuron. This suggests that the NMDA-to-AMPA ratio is only transiently perturbed by LTP. To test this, we induced LTP at rat neocortical synapses and recorded mixed AMPA-NMDA currents. We observed rapid LTP of AMPA currents, as well as delayed potentiation of NMDA currents that required previous AMPA potentiation. The delayed potentiation of NMDA currents restored the original NMDA-to-AMPA ratio within 2 h of LTP induction. These data suggest that recruitment of AMPA receptors to synapses eventually induces a proportional increase in NMDA current. This may ensure that LTP does not alter the relative contributions of these two receptors to synaptic transmission and information processing.

Novel presynaptic mechanisms for coincidence detection in synaptic plasticity

Long-term plasticity typically relies on postsynaptic NMDA receptors to detect the coincidence of pre- and postsynaptic activity. Recent studies, however, have revealed forms of plasticity that depend on coincidence detection by presynaptic NMDA receptors. In the amygdala, cortical afferent associative presynaptic long-term potentiation (LTP) requires activation of presynaptic NMDA receptors by simultaneous thalamic and cortical afferents. Surprisingly, both types of afferent can also undergo postsynaptically induced NMDA-receptor-dependent LTP. In the neocortex, spike-timing-dependent long-term depression (LTD) requires simultaneous activation of presynaptic NMDA autoreceptors and retrograde signalling by endocannabinoids. In cerebellar LTD, presynaptic NMDA receptor activation suggests that similar presynaptic mechanisms may exist. Recent studies also indicate the existence of presynaptic coincidence detection that is independent of NMDA receptors, suggesting that such mechanisms have a widespread role in plasticity.

Spike-timing-dependent plasticity: a comprehensive overview

Reference EPFL-ARTICLE-183375doi:10.3389/fnsyn.2012.00002View record in PubMed Record created on 2013-01-28, modified on 2017-05-12

Multiple forms of long-term plasticity at unitary neocortical layer 5 synapses

Long-term potentiation and depression (LTP and LTD) are cellular plasticity phenomena expressed at a variety of central synapses, and are thought to contribute to learning and developmental changes in circuitry. Recurrent neocortical layer-5 synapses are thought to express a presynaptic form of LTP that influences the short-term plasticity of the synapse. Here we show that changes in synaptic strength elicited by pairing high frequency pre- and postsynaptic firing at this synapse result from a mixture of presynaptic and postsynaptic forms of plasticity, as assessed by the analysis of changes in coefficient of variation, short-term plasticity, and NMDA:AMPA current ratios. Pharmacological dissection of this plasticity revealed that block of presynaptic LTD with an endocannabinoid inhibitor enhanced LTP, while the apparently presynaptic component of LTP could be prevented by induction in the presence of blockers of nitric oxide. These data suggest that correlated high-frequency firing at layer-5 synapses simultaneously induces a mixture of presynaptic LTD, presynaptic LTP, and postsynaptic LTP.

Optimal Information Storage in Noisy Synapses under Resource Constraints

Experimental investigations have revealed that synapses possess interesting and, in some cases, unexpected properties. We propose a theoretical framework that accounts for three of these properties: typical central synapses are noisy, the distribution of synaptic weights among central synapses is wide, and synaptic connectivity between neurons is sparse. We also comment on the possibility that synaptic weights may vary in discrete steps. Our approach is based on maximizing information storage capacity of neural tissue under resource constraints. Based on previous experimental and theoretical work, we use volume as a limited resource and utilize the empirical relationship between volume and synaptic weight. Solutions of our constrained optimization problems are not only consistent with existing experimental measurements but also make nontrivial predictions.