Project description:Postsynaptic release of Ca(2+) from intracellular stores is an important means of cellular signaling that mediates numerous forms of synaptic plasticity. Previous studies have identified a postsynaptic intracellular Ca(2+) requirement for a form of short-term plasticity, post-tetanic potentiation (PTP) at sensory neuron (SN)-motor neuron synapses in Aplysia. Here, we show that postsynaptic IP(3)-mediated Ca(2+) release in response to a presynaptic tetanus in an SN that induces PTP can confer transient plasticity onto a neighboring SN synapse receiving subthreshold activation. This heterosynaptic sharing of plasticity represents a dynamic, short-term synaptic enhancement of synaptic inputs onto a common postsynaptic target. Heterosynaptic sharing is blocked by postsynaptic disruption of Ca(2+)- and IP(3)-mediated signaling, and, conversely, it is mimicked by postsynaptic injection of nonhydrolyzable IP(3), and by photolysis of caged IP(3) in the MN. The molecular mechanism for heterosynaptic sharing involves metabotropic glutamate receptors and Homer-dependent interactions, indicating that Homer can facilitate the integration of Ca(2+)-dependent plasticity at neighboring postsynaptic sites and provides a postsynaptic mechanism for the spread of plasticity induced by presynaptic activation. Our results support a model in which postsynaptic summation of IP(3) signals from suprathreshold and subthreshold inputs results in molecular coincidence detection that gives rise to a novel form of heterosynaptic plasticity.
Project description:In the brain, the postsynaptic response of a neuron to time-varying inputs is determined by the interaction of presynaptic spike times with the short-term dynamics of each synapse. For a neuron driven by stochastic synapses, synaptic depression results in a quite different postsynaptic response to a large population input depending on how correlated in time the spikes across individual synapses are. Here we show using both simulations and mathematical analysis that not only the rate but the phase of the postsynaptic response to a rhythmic population input varies as a function of synaptic dynamics and synaptic configuration. Resultant phase leads may compensate for transmission delays and be predictive of rhythmic changes. This could be particularly important for sensory processing and motor rhythm generation in the nervous system.
Project description:Episodic memory has a dynamic nature: when we recall past episodes, we retrieve not only their content, but also their temporal structure. The phenomenon of replay, in the hippocampus of mammals, offers a remarkable example of this temporal dynamics. However, most quantitative models of memory treat memories as static configurations, neglecting the temporal unfolding of the retrieval process. Here, we introduce a continuous attractor network model with a memory-dependent asymmetric component in the synaptic connectivity, which spontaneously breaks the equilibrium of the memory configurations and produces dynamic retrieval. The detailed analysis of the model with analytical calculations and numerical simulations shows that it can robustly retrieve multiple dynamical memories, and that this feature is largely independent of the details of its implementation. By calculating the storage capacity, we show that the dynamic component does not impair memory capacity, and can even enhance it in certain regimes.
Project description:Continuous attractor models of working-memory store continuous-valued information in continuous state-spaces, but are sensitive to noise processes that degrade memory retention. Short-term synaptic plasticity of recurrent synapses has previously been shown to affect continuous attractor systems: short-term facilitation can stabilize memory retention, while short-term depression possibly increases continuous attractor volatility. Here, we present a comprehensive description of the combined effect of both short-term facilitation and depression on noise-induced memory degradation in one-dimensional continuous attractor models. Our theoretical description, applicable to rate models as well as spiking networks close to a stationary state, accurately describes the slow dynamics of stored memory positions as a combination of two processes: (i) diffusion due to variability caused by spikes; and (ii) drift due to random connectivity and neuronal heterogeneity. We find that facilitation decreases both diffusion and directed drifts, while short-term depression tends to increase both. Using mutual information, we evaluate the combined impact of short-term facilitation and depression on the ability of networks to retain stable working memory. Finally, our theory predicts the sensitivity of continuous working memory to distractor inputs and provides conditions for stability of memory.
Project description:Although it is well known that long-term synaptic plasticity can be expressed both pre- and postsynaptically, the functional consequences of this arrangement have remained elusive. We show that spike-timing-dependent plasticity with both pre- and postsynaptic expression develops receptive fields with reduced variability and improved discriminability compared to postsynaptic plasticity alone. These long-term modifications in receptive field statistics match recent sensory perception experiments. Moreover, learning with this form of plasticity leaves a hidden postsynaptic memory trace that enables fast relearning of previously stored information, providing a cellular substrate for memory savings. Our results reveal essential roles for presynaptic plasticity that are missed when only postsynaptic expression of long-term plasticity is considered, and suggest an experience-dependent distribution of pre- and postsynaptic strength changes.
Project description:Synaptic efficacy is subjected to activity-dependent changes on short- and long time scales. While short-term changes decay over minutes, long-term modifications last from hours up to a lifetime and are thought to constitute the basis of learning and memory. Both plasticity mechanisms have been studied extensively but how their interaction shapes synaptic dynamics is little known. To investigate how both short- and long-term plasticity together control the induction of synaptic depression and potentiation, we used numerical simulations and mathematical analysis of a calcium-based model, where pre- and postsynaptic activity induces calcium transients driving synaptic long-term plasticity. We found that the model implementing known synaptic short-term dynamics in the calcium transients can be successfully fitted to long-term plasticity data obtained in visual- and somatosensory cortex. Interestingly, the impact of spike-timing and firing rate changes on plasticity occurs in the prevalent firing rate range, which is different in both cortical areas considered here. Our findings suggest that short- and long-term plasticity are together tuned to adapt plasticity to area-specific activity statistics such as firing rates.
Project description: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.
Project description:Functional magnetic resonance imaging (fMRI) studies have provided ample evidence for the involvement of the lateral occipital cortex (LO), fusiform gyrus (FG), and intraparietal sulcus (IPS) in visuo-haptic object integration. Here we applied 30 min of sham (non-effective) or real offline 1 Hz repetitive transcranial magnetic stimulation (rTMS) to perturb neural processing in left LO immediately before subjects performed a visuo-haptic delayed-match-to-sample task during fMRI. In this task, subjects had to match sample (S1) and target (S2) objects presented sequentially within or across vision and/or haptics in both directions (visual-haptic or haptic-visual) and decide whether or not S1 and S2 were the same objects. Real rTMS transiently decreased activity at the site of stimulation and remote regions such as the right LO and bilateral FG during haptic S1 processing. Without affecting behavior, the same stimulation gave rise to relative increases in activation during S2 processing in the right LO, left FG, bilateral IPS, and other regions previously associated with object recognition. Critically, the modality of S2 determined which regions were recruited after rTMS. Relative to sham rTMS, real rTMS induced increased activations during crossmodal congruent matching in the left FG for haptic S2 and the temporal pole for visual S2. In addition, we found stronger activations for incongruent than congruent matching in the right anterior parahippocampus and middle frontal gyrus for crossmodal matching of haptic S2 and in the left FG and bilateral IPS for unimodal matching of visual S2, only after real but not sham rTMS. The results imply that a focal perturbation of the left LO triggers modality-specific interactions between the stimulated left LO and other key regions of object processing possibly to maintain unimpaired object recognition. This suggests that visual and haptic processing engage partially distinct brain networks during visuo-haptic object matching.
Project description:While there is evidence that the visual cortex retains a potential for plasticity in adulthood, less is known about the subcortical stages of visual processing. Here, we asked whether short-term ocular dominance plasticity affects the human visual thalamus. We addressed this question in normally sighted adult humans, using ultra-high field (7T) magnetic resonance imaging combined with the paradigm of short-term monocular deprivation. With this approach, we previously demonstrated transient shifts of perceptual eye dominance and ocular dominance in visual cortex (Binda et al., 2018). Here, we report evidence for short-term plasticity in the ventral division of the pulvinar (vPulv), where the deprived eye representation was enhanced over the nondeprived eye. This vPulv plasticity was similar as previously seen in visual cortex and it was correlated with the ocular dominance shift measured behaviorally. In contrast, there was no effect of monocular deprivation in two adjacent thalamic regions: dorsal pulvinar and Lateral Geniculate Nucleus. We conclude that the visual thalamus retains potential for short-term plasticity in adulthood; the plasticity effect differs across thalamic subregions, possibly reflecting differences in their corticofugal connectivity.
Project description:Simple models of short term synaptic plasticity that incorporate facilitation and/or depression have been created in abundance for different synapse types and circumstances. The analysis of these models has included computing mutual information between a stochastic input spike train and some sort of representation of the postsynaptic response. While this approach has proven useful in many contexts, for the purpose of determining the type of process underlying a stochastic output train, it ignores the ordering of the responses, leaving an important characterizing feature on the table. In this paper we use a broader class of information measures on output only, and specifically construct hidden Markov models (HMMs) (known as epsilon machines or causal state models) to differentiate between synapse type, and classify the complexity of the process. We find that the machines allow us to differentiate between processes in a way not possible by considering distributions alone. We are also able to understand these differences in terms of the dynamics of the model used to create the output response, bringing the analysis full circle. Hence this technique provides a complimentary description of the synaptic filtering process, and potentially expands the interpretation of future experimental results.