1038 Biophysical Journal Volume 108 March

Size: px
Start display at page:

Download "1038 Biophysical Journal Volume 108 March"

Transcription

1 1038 Biophysical Journal Volume 108 March Article Effect of the Initial Synaptic State on the Probability to Induce Long-Term Potentiation and Depression Michele Migliore, 1, * Giada De Simone, 1 and Rosanna Migliore 1 1 Institute of Biophysics, National Research Council, Palermo, Italy ABSTRACT Long-term potentiation (LTP) and long-term depression (LTD) are the two major forms of long-lasting synaptic plasticity in the mammalian neurons, and are directly related to higher brain functions such as learning and memory. Experimentally, they are characterized by a change in the strength of a synaptic connection induced by repetitive and properly patterned stimulation protocols. Although many important details of the molecular events leading to LTP and LTD are known, experimenters often report problems in using standard induction protocols to obtain consistent results, especially for LTD in vivo. We hypothesize that a possible source of confusion in interpreting the results, from any given experiment on synaptic plasticity, can be the intrinsic limitation of the experimental techniques, which cannot take into account the actual state and peak conductance of the synapses before the conditioning protocol. In this article, we investigate the possibility that the same experimental protocol may result in different consequences (e.g., LTD instead of LTP), according to the initial conditions of the stimulated synapses, and can generate confusing results. Using biophysical models of synaptic plasticity and hippocampal CA1 pyramidal neurons, we study how, why, and to what extent the phenomena observed at the soma after induction of LTP/LTD reflects the actual (local) synaptic state. The model and the results suggest a physiologically plausible explanation for why LTD induction is experimentally difficult to obtain. They also suggest experimentally testable predictions on the stimulation protocols that may be more effective. INTRODUCTION The two major forms of long-lasting synaptic plasticity in the mammalian neurons, long-term potentiation (LTP) and long-term depression (LTD), are characterized by changes in the strength of a synaptic connection induced by a repetitive and properly patterned electrical activity. Discovered ~40 years ago (1,2), for both phenomena there is now compelling evidence that they represent a basic step to understand neuronal development, circuit reorganization, and learning and memory mechanisms (3 6). The underlying molecular mechanisms are starting to be unraveled (7) and are under intense experimental and theoretical scrutiny, especially in the CA1 region of the hippocampus. Experimental studies on LTP and LTD are usually performed using in vitro or in vivo preparations (e.g., Buschler et al. (8) and Goh and Manahan- Vaughan (9)), exploiting specific stimulation patterns of synaptic inputs to induce long-lasting changes in the synaptic strength. In almost all cases, the overall amount of LTP or LTD is measured at the soma, using specific and precisely defined experimental conditioning protocols that have been found to be particularly effective in inducing synaptic plasticity, such as constant frequency or q-burst stimulation (10 14). Submitted September 16, 2014, and accepted for publication December 10, *Correspondence: michele.migliore@cnr.it Editor: Edward Stuenkel. A possible major limitation with this approach is that, even when it is carried out using more-sophisticated electrophysiological recording techniques (15,16), it cannot take into account the actual state of a given synapse before the conditioning protocol. From a general point of view, it cannot be excluded that the same experimental protocol delivered to different synapses may result in opposite consequences (e.g., LTD instead of LTP), according to the state of the synapse at the time of conditioning. The interpretation of the overall results measured at the soma may thus be misleading, especially for LTD, which is well known to be difficult to induce (9,17) for unclear reasons. A number of questions on the interplay among stimulation patterns, preconditioning synaptic strength, and dendritic location, thus remain open. The main aim of this work is to gain insight into the relation between the stimulation patterns inducing local (dendritic) synaptic plasticity and the excitatory postsynaptic potential (EPSP) heights observed at the soma. Using biophysical models of synaptic plasticity and hippocampal CA1 pyramidal neurons, we studied how, why, and to what extent EPSPs observed at the soma after induction of LTP/LTD reflects the actual (local) synaptic state. The model and the results suggest a physiologically plausible explanation of why LTD induction is experimentally difficult, and they offer experimentally testable predictions on the stimulation protocols that may be more effective. Ó 2015 by the Biophysical Society /15/03/1038/9 $2.00

2 LTP and LTD in CA1 Pyramidal Neurons 1039 MATERIALS AND METHODS Simulations were carried out using the NEURON simulation environment (Ver. 7.3 (18)). In most cases, an IBM Blue Gene/Q (Armonk, NY) supercomputer (the FERMI system at Cineca, Bologna, Italy) was used to run simulations in parallel. A typical set of 18,564, 90-s long, simulations required ~8 h using 2048 processors. Model and simulation files will be available for public download on the MODELDB section of the SENSE- LAB suite (Accession No ; The SenseLab Project, med.yale.edu/modeldb/). A morphologically realistic three-dimensional model of a hippocampal CA1 pyramidal neuron (Fig. 1 A, cell 5,038,804, originally downloaded from the public archive, was used in all cases. The model neuron included uniform passive properties (t m ¼ 28 ms, R m ¼ 28 ku cm 2, and R a ¼ 150 U cm), and a set of active properties (voltage-dependent ionic channels, kinetic and distribution) identical to those described in a previous article (Migliore et al. (19), MODELDB accession No ). Briefly, the model included a sodium (g NA ) and a delayed rectifier potassium (g KDR ) conductance, uniformly distributed throughout the dendrites, whereas g KA and I h linearly increased with the distance from soma. The neuron model has already been validated against several experimental findings on electrophysiological and synaptic integration properties of CA1 neurons (e.g., Migliore (20) and Gasparini et al. (21)). One example is shown in the inset of Fig. 1 A, where we show the model s reproduction of the classic experimental finding on distance-independent synaptic integration in CA1 pyramidal neurons (22,23). The traces show somatic membrane potential in response to a short train of five synaptic stimuli delivered at 20 Hz to either a distal (304 mm from the soma, A B C FIGURE 1 (A) The three-dimensional reconstruction of the CA1 hippocampal pyramidal neuron used for all simulations (top). The traces are somatic membrane potential during a train of stimulations of a distal (red) or proximal (green) synaptic input. (B) Schematic representation of the model synapse; in all cases we used: U SE ¼ 0.36, t in ¼ 3 ms, t rec ¼ 50 ms, A SE ¼ 250 pa, R in ¼ 10 MU, f ¼ mv 1, d i ¼ 400 ms 1, l P ¼ 10 3 ms 1, l D ¼ ms 1, n P ¼ ms 1, n D ¼ 0.07 ms 1, h ¼ ms 1, g ¼ 0.2 ms 1, b ¼ ms 1, t m ¼ 40 ms, g 2 ¼ 43 ms, M i ¼ mv/ms, A P ¼ 2mV 2, and A D ¼ 0.5 mv 2.(C) Dendritic (black line) and somatic (green line) membrane potential in response to test or conditioning stimuli; stimulation patterns correspond to the experimental TBS (left) or constant frequency protocols (right) for LTP and LTD. To see this figure in color, go online.

3 1040 Migliore et al. distal) or a proximal (23 mm from the soma, proximal) dendritic compartment. As in the experiments (22), the amount of temporal summation occurring at the soma was independent from the synaptic input location. To be more closely related to what can happen in vivo, and in contrast to the widely used experimental practice (in vitro) to pharmacologically block the generation of action potentials, our model cell was in all cases a fully active neuron. Synaptic weights eliciting dendritic or somatic action potentials during test pulses were excluded from the analysis. The model synapse To investigate synaptic plasticity, we started from a model previously proposed for LTP and LTD induction (24,25), adapting its scheme as shown in Fig. 1 B to also take into account experimental findings on heterosynaptic LTP (26) and depotentiation (7). The presynaptic part was modeled using the phenomenological model of neocortical synapses discussed by Tsodyks and Markram (27) and Abbott et al. (28), described by the equations dy dx ¼ z t rec IU SE x; ¼ z t in þ IU SE x; and z ¼ 1 x y; which reproduce the stereotypical synaptic response dynamics between pyramidal neurons under physiological conditions. The variables x, y, and z are the fraction of resources in the recovered, active, and inactive states, respectively. The parameters used for the presynaptic mechanisms (see legend of Fig. 1) reproduced experimental findings on pyramidal CA1 neurons under control conditions (22). When an input stimulus I is delivered, the active fraction y of the presynaptic resources generates a synaptic current, I syn ¼ A SE w S y, whose effects are described by the postsynaptic equations dv S dg ¼ g t in ; ¼ v S 1 þ R in A SE g þ f ðd P N P d D N D Þ ; t m t m dn i dc ¼ gv S hc þ bðv m þ 65Þ; ¼ n i C ðl i þ gd i ÞN i þ M ini 2 ; A i þ Ni 2 where i h P, D, and g ¼ w S y. These equations derive from the synaptic transmission scheme illustrated in Fig. 1 B. A synaptic current, I syn, generates an effective postsynaptic membrane potential v s (and an effective post synaptic current, I s ¼ g 2 v s ), which is further modulated by autocatalytic processes, N P and N D, representing all those postsynaptic mechanisms that could be involved with LTP and LTD induction and maintenance, such as protein autophosphorylation (29 31). The details and rationale for this kind of implementation are discussed elsewhere (24,25,32). Briefly, the signal generated by a presynaptic event produces a postsynaptic depolarization, with an eventual contribution from additional local sources (e.g., depolarization spread from nearby dendrites). Under the appropriate presynaptic stimulation pattern, this process activates N P and N D, which modulate the overall postsynaptic response, increasing (LTP) or decreasing (LTD) the amount of signal generated at each stimulus. The operation of the model can be better understood by considering that this set of coupled nonlinear differential equations implements a bistable switch for N P and N D, independently controlled by the postsynaptic depolarization and each one characterized by two steady-state conditions (ground and high). The high state for N P and N D is responsible for the induction and maintenance of LTP and/or LTD. In agreement with experimental suggestions (33,34), following the appropriate conditioning protocol a given synapse will change its state in an all-or-none manner. The model has already been validated against experimental findings (32). This model has suggested experimentally testable predictions (24,25). In this work we have also taken into account experimental findings on heterosynaptic plasticity, by adding a term (b(v m þ 65), in the equations above) that explicitly depends on the local membrane depolarization (V m ) from rest (see below). It should be noted that this model does not take into account the relatively slow subcellular processes (such as protein turnover) underlying LTP/LTD expression and modulation after induction, because they are out of the scope of this work. In our case, once a synapse reaches a stable LTP/LTD state, it does not spontaneously decay to ground. RESULTS Stimulation protocols Experimentally, a widely used protocol is the q-burst stimulation (TBS), mimicking the endogenous q-frequency EEG activity seen in the rat hippocampus during exploratory learning tasks (35,36). It consists of trains of short bursts separated by interburst intervals. With TBS protocols, LTP is typically induced by four pulses at Hz repeated at 200-ms intervals, and LTD by two pulses at Hz repeated at 1-s intervals. Typical examples of LTP and LTD induction in our model are shown in Fig. 1 C (left), where we plot the somatic and dendritic membrane potential in response to two test stimuli delivered to a single synapse before and after conditioning stimulation patterns corresponding to the experimental TBS protocol for LTP and LTD. Corresponding traces for the two variables N P and N D are shown in Fig. S1 in the Supporting Material. Another typical experimental protocol that we used in this article is tetanic stimulation at constant frequency. In this case, LTP is typically induced with a high-frequency stimulation (e.g., 50 or Hz (10,13,37)), whereas LTD can be induced by a train of stimuli at low frequency (typically in the range of 1 5 Hz (9,10,38)). We will refer to these protocols as HFS or LFS, respectively (10,11,37,39). Typical simulations findings for both cases are shown in Fig. 1 C (right). In this work, we considered only experimental protocols involving presynaptic stimulation alone. Other protocols used to study synaptic plasticity induced by paired pre- and postsynaptic activity (such as spike-time-dependent plasticity) were not considered. Model validation against experimental findings We start by validating the model against experimental findings on LTP and LTD. For this purpose we first activated individual synapses, randomly located in the proximal apical trunk (<110 mm from soma), with a test pulse delivered

4 LTP and LTD in CA1 Pyramidal Neurons 1041 every 50 ms. In this way we can assess a control baseline for the local (dendritic) and somatic EPSP (Fig. 2 A, top left, symbols for t < 750 ms). A 5-s conditioning stimulation was then independently delivered to each synapse, according to the relative experimental protocols for TBS LTP or LTD induction. Finally, another set of test stimuli were delivered to measure the amount of synaptic plasticity observed locally and at the soma (Fig. 2 A, top middle, symbols for 6000 < t < 7000 ms). The results show, on average, 165% potentiation and 35% depression, in qualitative agreement with experimental findings (13,40). We also tested the ability of the model to erase LTP after its induction, an effect called depotentiation and experimentally found in CA1 neurons (41,42). To this purpose, all the synapses that were previously potentiated with the TBS LTP protocol received an additional 40-s conditioning period at a constant frequency of 1 Hz (42). As shown in Fig. 2 A (top-right plot), LTP was erased and all synapses returned to their control state. We found that a depotentiation protocol was also able to erase LTD, switching the synapses to their control state (Fig. 2 A, bottom plot). Finally, we tested our model for heterosynaptic LTP, which occurs in unconditioned synapses spatially close to synapses conditioned with a TBS LTP protocol. Experimentally, it has been observed in CA1 neuron excitatory synapses up to z70 mm from the stimulation location (43). We selected couples of synapses (n ¼ 3 5 7) located on oblique dendrites at different relative distance, and conditioned one of them with an LTP protocol. As in the experiments, heterosynaptic LTP was observed up to z70 mm from the site of stimulation (Fig. 2 B). It should be stressed that, to the best of our knowledge, our model is A B FIGURE 2 (A) Recordings from a representative simulation in which (top) homosynaptic long-term depression (LTP) and (bottom) homosynaptic long-term potentiation (LTD) were induced by a q-burst stimulation (TBS) of synapses placed on the apical trunk within 110 mm from the soma. Each point represents the percent change of the somatic (solid squares) and local (open circles) peak EPSP amplitude evoked by test pulses delivered to one random synapse before (t < 750 ms) and after (6000 < t < 7000 ms) a LTP or LTD conditioning period. In both cases, a depotentiation protocol was applied at t ¼ 7000 ms for 40 s. (B) Heterosynaptic LTP, occurring in synapses that were not directly stimulated, as a function of the relative distance from a stimulated synapse. To see this figure in color, go online.

5 1042 Migliore et al. the only one able to reproduce such a wide set of experimental protocols and findings. These results show that our model is able to take into account the most relevant experimental findings on LTP and LTD, validating its use to gain more information on the relation between what is observed at the soma and what is really occurring at the synaptic locations. The effect of induction protocols and role of the initial synaptic state To test the efficacy of TBS protocols in inducing LTP and LTD in synapses with a relatively uniform distribution of peak conductance, we run a set of simulations stimulating random groups of 15 synapses uniformly distributed over the dendritic tree. Their weights (peak conductances) were drawn from a normal distribution (with average values in the range ns and a variance of 0.01 ns 2 ), and assumed to be in their ground control state (i.e., neither potentiated nor depressed, N P ¼ N D ¼ 0). For each average value, we ran 10 simulations, randomly redistributing location and peak conductance of all synapses. The average (mean 5 SE) EPSP change measured at the soma is shown in Fig. 3 (solid symbols). It clearly show the robustness of the LTP protocol (solid red squares) for synaptic weights larger than z0.25 ns, whereas the TBS LTD protocol (solid blue circles) was effective only for a relatively small range of peak conductance (~ ns). Note that both protocols lead to LTP for a peak synaptic conductance larger than 1.2 ns. The qualitative results did not change using a more localized (e.g., proximal/distal) dendritic distribution of the synapses (not shown). We also hypothesize that the actual initial synaptic state can affect the amount of observed LTP or LTD. To test this hypothesis, we repeated the simulations assuming that the initial synaptic weights were the result of a previously % of somatic peak EPSP TBS LTD from ground state TBS LTD from potentiated state peak conductance (ns) TBS LTP from ground state TBS LTP from potentiated state FIGURE 3 Peak EPSP amplitude, measured at the soma during a test pulse after TBS LTP (red squares) or LTD (blue circles) protocol on 15 synapses uniformly distributed over the entire neuron, as a function of the peak synaptic conductance. Synapses started from their ground state (solid symbols) or in a potentiated state (open symbols). To see this figure in color, go online. induced LTP on all synapses. The results (Fig. 3, open symbols) show how big the discrepancy can be between the expected and actual result measured at the soma. Whereas a LTD protocol would be observed as LTD for a much wider range of synaptic strength (Fig. 3, open blue symbols), there can combinations that would result in an outcome that appears to be the opposite of what is expected for example, an LTP protocol would be observed as LTD or no effects (Fig. 3, open red symbols). These findings suggest that the induction of LTD can be difficult because it strongly depends on the actual peak conductance and initial state of the synapse at the time of the experiment: weak synapses can be unaffected by the stimulation protocol, whereas those with higher strength can be potentiated. Although some information is available on the strength and distribution of synaptic currents in CA1 neurons (e.g., Ito and Schuman (44)), the actual state of individual synapses before any given experiment is carried out cannot be identified. Induction protocols may thus be applied to synapses that are already in the appropriate state, which will not be affected by the conditioning pattern. This can generate misleading results. An overall picture of what can be observed at the soma, after a conditioning period, can be obtained by considering groups of synapses under control conditions (i.e., in their ground state) uniformly distributed over the entire neuron but with a wider distribution of peak conductance, as it would occur in a real experiment. For this purpose, we ran a series of simulations using groups of 15 randomly distributed synapses conditioned with a TBS LTD (left part of Fig. 4 A) or a TBS LTP protocol (right part of Fig. 4 A). In each case, the synapses were initialized to their ground state, with weights randomly drawn from a uniform distribution within increasing ranges, in 0.4 ns bins. The probability distribution of somatic EPSP change was calculated from 10 trials carried out for each range and stimulation protocol (randomly redistributing both the synaptic location and the peak conductance). The normalized distributions of the final peak conductance change (relative to control) are reported in Fig. 4 A. Each distribution (colored lines in Fig. 4 A) indicates the probability to observe LTD (left side) or LTP (right side) for the different range of peak conductance that was considered. The model confirmed that LTP is an extremely robust process, resulting in % increase in synaptic strength for the entire range tested ( ns). In contrast, the overall effect elicited by a TBS LTD protocol will drastically differ according to the initial synaptic weight. The maximum effectiveness of a TBS LTD protocol would be obtained only for initial synaptic weights within a rather narrow range ( ns, in our case). This effect depends on the final state of the individual synapses that, according to their initial weight, may end up being in different states, as schematically represented in Fig. 4 B, where we show two typical results for the

6 LTP and LTD in CA1 Pyramidal Neurons 1043 A LTD TBS protocol from ground state LTP ns ns ns ns ns % of somatic peak EPSP probability B C ns ns LTP protocol LTD protocol LTP LTD no change peak conductance (ns) FIGURE 4 (A) Probability distribution function to obtain LTP (right) or LTD (left) for different synaptic weight ranges. (B) Two typical cases illustrating the state of all synapses for two synaptic weight ranges after a TBS LTD conditioning period. (C) Schematic representation of the overall result that can be obtained by TBS LTP or LTD protocols for different values of the initial synaptic peak conductance. (Colored lines) Different ranges tested in the simulations. To see this figure in color, go online. LTD protocol and different synaptic weight range. The overall picture, for synapses starting from a ground state, is depicted in Fig. 4 C: for a LTD protocol, initial synaptic weights that are too low or too high would result in a significant proportion of synapses being unaffected or potentiated, respectively. Taken together, these results suggest that, when a group of synapses is subjected to a LTP protocol, the overall effect observed at the soma could effectively reflect the proportion of potentiated synapses. Instead, LTD protocols will result in an overall effect that cannot be directly interpreted in the same way. The reason for this apparently inconsistent result can be understood by considering the dynamics of the variable C in the equations for the synaptic transmission; it is one of the main determinants for the activation of the autocatalytic processes and could correspond to the intracellular calcium concentration, which is experimentally known to have this role in synaptic plasticity (reviewed in Lisman and McIntyre (45)). In Fig. 5 A we summarize the possible results that can be obtained by conditioning a single synapse in its ground state with a TBS LTD protocol: no change, LTD, or LTP if the initial peak conductance is low or intermediate, high, or very high, respectively. The local dendritic membrane potential for each case is shown in Fig. 5 B, whereas the corresponding time course of the variables C, N p, and N d is shown in Fig. 5 C. For a low or intermediate initial peak conductance (Fig. 5, B and C, green traces), the current generated by the synaptic input during the conditioning stimulation is not able to raise the level of C enough to activate N p and/or N d. At the end of the conditioning period they all return to the ground state, and this is observed as a no-change in the amplitude of the test pulse. For a higher initial peak conductance (Fig. 5, blue traces), C, N p, and N d reach a higher level. However, although this is sufficient to switch N d to its high state, it is not enough for N p (compare blue traces in Fig. 5 C, middle and bottom plots). The end result will be observed as a depression of the test pulse. For even higher (but still subthreshold) initial synaptic peak conductance (Fig. 5, red traces), C raises to a level sufficient to switch both N p, and N d to their high state; this will be observed as a potentiation. These results explain why the interpretation of experimental findings, on the amount of LTD elicited by standard protocols, can be misled by the initial conditions of the stimulated synapses. Finally, to test these results with another of the widely used induction protocols, we explored LTP and LTD induced by a tetanic stimulation at a constant HFS or LFS (see Fig. 1 C). For this purpose, we carried out a series of simulations on a synapse at 150 mm from the soma, using a wide range of initial peak conductance ( ns) and a range of stimulation frequency at ~4 Hz for LTD, the most common constant frequency protocol experimentally used to induce LTD (Goh and Manahan-Vaughan (9) and references therein), and >50 Hz for LTP. The results for LTP were rather robust and consistent with those found for the TBS protocol (Fig. 3): LTP was consistently induced over practically the entire range of frequency tested (not shown). Also consistently with what we found for a TBS LTD protocol, our model predicts that a synapse starting from its ground state (Fig. 6 A) would exhibit LTD after a LFS conditioning only for a rather limited region of the parameter space (blue area in Fig. 6 A). The initial synaptic state also was particularly important for a correct interpretation of the results after a conditioning period: the model suggests that synapses already in their potentiated state will exhibit LTD for selected ranges of peak conductance and conditioning frequency (Fig. 6 B), whereas initially depressed synapses would most likely appear as strongly potentiated after an LFS period (Fig. 6 C). Taken together, these results suggest that LTD induction using LFS protocols may be experimentally difficult to achieve because synaptic parameters (in our

7 1044 Migliore et al. A control (from ground state) POST PRE TBS LTD CONDITIONING no change LTD LTP peak cond. low or med high very high B C C (% change) 3 mv Np Nd time (sec) 5 sec FIGURE 5 (A) Schematic representation of the results obtained by conditioning a single synapse in its ground state with a TBS LTD protocol as a function of the peak conductance. (B) Corresponding local dendritic potential for a low or intermediate (green line), high (blue line), and very high (red line) peak conductance. (C) Time course of the variables C, N p, and N d for each case. To see this figure in color, go online. case, initial state and peak conductance) need to start within a small subset of the physiological range. DISCUSSION The main aim of this article was to investigate the relation between the actual state of synapses subjected to typical experimental protocols for LTP and LTD induction, and what is observed at the soma. Our model pointed out that the outcome of an experiment, testing the amount of synaptic LTP/LTD plasticity that can be induced, strongly depends (at least) on the initial synaptic state and peak conductance. We think that this is an important issue because, no matter how sophisticated these experimental techniques might be, the actual state and peak conductance of individual synapses before any given conditioning period cannot be easily measured. Following the results obtained in this work we suggest that, to obtain results as consistent as possible among different preparations and experimental conditions, it would be a good experimental practice to use a stimulation paradigm able to preset the synapses in a preconditioning state that would give, in principle, the best results. Our model suggests that this preconditioning state should be LTP before a LTD protocol, and depotentiated before a LTP protocol. The rationale for these choices can be understood by considering the overall model findings, discussed below. Before an LTD conditioning protocol, one would like to avoid synapses that are either depressed or in the ground state, because a number of them will change to a potentiated state, confusing the overall result. This could be obtained by

8 LTP and LTD in CA1 Pyramidal Neurons 1045 constant Low Frequency Stimulation (LFS) A frequency (Hz) B frequency (Hz) C frequency (Hz) from ground state peak conductance (ns) from potentiated state peak conductance (ns) from depressed state peak conductance (ns) % of control preconditioning the synapses with an LTP protocol. In this case, our model predicts that starting from a potentiated state an LTD protocol will result in LTD for the widest range of initial peak conductance. Even if at the end of the conditioning period individual synapses can be in the ground or depressed state, according to their initial strength, the overall result in most cases will be a depression, giving the experimenter a more precise and consistent idea of how much that particular synaptic pathway can be depressed. For synapses undergoing a LTP protocol, in principle one can expect that the best preconditioning state would be a FIGURE 6 Results for LFS conditioning of a synapse located 150 mm from the soma; (A) synapse initially in the ground state; (B) synapse initially in the potentiated state; (C) synapse initially in the depressed state. To see this figure in color, go online. 40 depressed state. The problem with this approach is that LTD protocols are not able to consistently switch synapses in their depressed state. Because of the role also played by the initial peak conductance, an attempt to depress a random group of synapses would result in a mix of synaptic states that would confuse the interpretation of the experimental findings. The model predicts that the best preconditioning option in this case would be a depotentiation, because of its ability to bring a synapse to its ground state, no matter the initial state (Fig. 2 A); a LTP protocol will thus result in LTP in most cases. Finally, the model explains why LTD induction may be more critical to be obtained, with respect to LTP, at least in vivo. The reason is directly related to the dynamics of critical postsynaptic processes. A LTP protocol is rather efficient in generating enough signal to activate the autocatalytic processes responsible for LTP, and it does not have a ceiling: a stronger signal would simply saturate the overall amount of observed LTP. A LTD protocol instead is more critical, because it would generate LTD only when the combination of the involved pre- and postsynaptic quantities reach values that are just enough to activate the autocatalytic process for LTD (but not that for LTP, which has a higher threshold). Although in vitro these conditions can be more easily obtained by controlling many electrophysiological parameters (background activity, bathing solutions, stimulation locations, etc.), in vivo it may be much more difficult. SUPPORTING MATERIAL One figure is available at S (15) ACKNOWLEDGMENTS We thank the Cineca consortium (Bologna, Italy) for granting access to their IBM BlueGene/Q FERMI system. The research leading to these results has received funding from the European Union Seventh Framework Program (grant No. FP7/ ) under grant No (Human Brain Project). REFERENCES 1. Bliss, T. V., and T. Lomo Long-lasting potentiation of synaptic transmission in the dentate area of the anaesthetized rabbit following stimulation of the perforant path. J. Physiol. 232: Dunwiddie, T., and G. Lynch Long-term potentiation and depression of synaptic responses in the rat hippocampus: localization and frequency dependency. J. Physiol. 276: Whitlock, J. R., A. J. Heynen,., M. F. Bear Learning induces long-term potentiation in the hippocampus. Science. 313: Bear, M. F A synaptic basis for memory storage in the cerebral cortex. Proc. Natl. Acad. Sci. USA. 93: Martin, S. J., P. D. Grimwood, and R. G. Morris Synaptic plasticity and memory: an evaluation of the hypothesis. Annu. Rev. Neurosci. 23:

9 1046 Migliore et al. 6. Nabavi, S., R. Fox,., R. Malinow Engineering a memory with LTD and LTP. Nature. 511: Collingridge, G. L., S. Peineau,., Y. T. Wang Long-term depression in the CNS. Nat. Rev. Neurosci. 11: Buschler, A., J. J. Goh, and D. Manahan-Vaughan Frequency dependency of NMDA receptor-dependent synaptic plasticity in the hippocampal CA1 region of freely behaving mice. Hippocampus. 22: Goh, J. J., and D. Manahan-Vaughan Synaptic depression in the CA1 region of freely behaving mice is highly dependent on afferent stimulation parameters. Front. Integr. Neurosci. 7: Dudek, S. M., and M. F. Bear Homosynaptic long-term depression in area CA1 of hippocampus and effects of n-methyl-d-aspartate receptor blockade. Proc. Natl. Acad. Sci. USA. 89: Bliss, T. V., and G. L. Collingridge A synaptic model of memory: long-term potentiation in the hippocampus. Nature. 361: Capocchi, G., M. Zampolini, and J. Larson Theta-burst stimulation is optimal for induction of LTP at both apical and basal dendritic synapses on hippocampal CA1 neurons. Brain Res. 591: Hernandez, R. V., M. M. Navarro,., R. G. LeBaron Differences in the magnitude of long-term potentiation produced by q-burst and high frequency stimulation protocols matched in stimulus number. Brain Res. Brain Res. Protoc. 15: Hsu, J. C., S. J. Cheng,., Y. W. Lin Bidirectional synaptic plasticity induced by conditioned stimulations with different number of pulse at hippocampal CA1 synapses: roles of n-methyl-d-aspartate and metabotropic glutamate receptors. Synapse. 65: Matsuzaki, M., G. C. Ellis-Davies,., H. Kasai Dendritic spine geometry is critical for AMPA receptor expression in hippocampal CA1 pyramidal neurons. Nat. Neurosci. 4: Davie, J. T., M. H. Kole,., M. Häusser Dendritic patch-clamp recording. Nat. Protoc. 1: Manahan-Vaughan, D Long-term depression in freely moving rats is dependent upon strain variation, induction protocol and behavioral state. Cereb. Cortex. 10: Hines, M. L., and N. T. Carnevale The NEURON simulation environment. Neural Comput. 9: Migliore, M., M. Ferrante, and G. A. Ascoli Signal propagation in oblique dendrites of CA1 pyramidal cells. J. Neurophysiol. 94: Migliore, M On the integration of subthreshold inputs from perforant path and Schaffer collaterals in hippocampal CA1 pyramidal neurons. J. Comput. Neurosci. 14: Gasparini, S., M. Migliore, and J. C. Magee On the initiation and propagation of dendritic spikes in CA1 pyramidal neurons. J. Neurosci. 24: Magee, J. C Dendritic I h normalizes temporal summation in hippocampal CA1 neurons. Nat. Neurosci. 2: Magee, J. C Dendritic integration of excitatory synaptic input. Nat. Rev. Neurosci. 1: Migliore, M., and P. Lansky Long-term potentiation and depression induced by a stochastic conditioning of a model synapse. Biophys. J. 77: Migliore, M., and P. Lansky Computational model of the effects of stochastic conditioning on the induction of long-term potentiation and depression. Biol. Cybern. 81: Engert, F., and T. Bonhoeffer Dendritic spine changes associated with hippocampal long-term synaptic plasticity. Nature. 399: Tsodyks, M. V., and H. Markram The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability. Proc. Natl. Acad. Sci. USA. 94: Abbott, L. F., J. A. Varela,., S. B. Nelson Synaptic depression and cortical gain control. Science. 275: Otmakhov, N., L. C. Griffith, and J. E. Lisman Postsynaptic inhibitors of calcium/calmodulin-dependent protein kinase type II block induction but not maintenance of pairing-induced long-term potentiation. J. Neurosci. 17: Lisman, J., H. Schulman, and H. Cline The molecular basis of CaMKII function in synaptic and behavioral memory. Nat. Rev. Neurosci. 3: Kotaleski, J. H., and K. T. Blackwell Modeling the molecular mechanisms of synaptic plasticity using systems biology approaches. Nat. Rev. Neurosci. 11: Migliore, M., F. Alicata, and G. F. Ayala A model for long-term potentiation and depression. J. Comput. Neurosci. 2: Petersen, C. C., R. C. Malenka,., J. J. Hopfield All-or-none potentiation at CA3-CA1 synapses. Proc. Natl. Acad. Sci. USA. 95: O Connor, D. H., G. M. Wittenberg, and S. S. Wang Graded bidirectional synaptic plasticity is composed of switch-like unitary events. Proc. Natl. Acad. Sci. USA. 102: Otto, T., H. Eichenbaum,., C. G. Wible Learning-related patterns of CA1 spike trains parallel stimulation parameters optimal for inducing hippocampal long-term potentiation. Hippocampus. 1: Buzsáki, G Theta-oscillations in the hippocampus. Neuron. 33: Huang, Y. Y., P. V. Nguyen,., E. R. Kandel Long-lasting forms of synaptic potentiation in the mammalian hippocampus. Learn. Mem. 3: Mulkey, R. M., and R. C. Malenka Mechanisms underlying induction of homosynaptic long-term depression in area CA1 of the hippocampus. Neuron. 9: Brown, T. H., P. F. Chapman,., C. L. Keenan Long-term synaptic potentiation. Science. 242: Ito, M Long-term depression. Annu. Rev. Neurosci. 12: Barrionuevo, G., F. Schottler, and G. Lynch The effects of repetitive low frequency stimulation on control and potentiated synaptic responses in the hippocampus. Life Sci. 27: Staubli, U., and G. Lynch Stable depression of potentiated synaptic responses in the hippocampus with 1 5 Hz stimulation. Brain Res. 513: Engert, F., and T. Bonhoeffer Synapse specificity of long-term potentiation breaks down at short distances. Nature. 388: Ito, H. T., and E. M. Schuman Distance-dependent homeostatic synaptic scaling mediated by A-type potassium channels. Front. Cell. Neurosci. 3: Lisman, J. E., and C. C. McIntyre Synaptic plasticity: a molecular memory switch. Curr. Biol. 11:R788 R791.

10 Supporting Material Effect of the initial synaptic state on the probability to induce Long-Term Potentiation and Depression Michele Migliore, Giada De Simone, Rosanna Migliore Supporting Figure: Figure S1: Dynamical behavior of the two variables N P and N D, during the simulations shown in Fig.1C for induction of LTP (top) or LTD (bottom) using experimental protocols.

SUPPLEMENTARY INFORMATION. Supplementary Figure 1

SUPPLEMENTARY INFORMATION. Supplementary Figure 1 SUPPLEMENTARY INFORMATION Supplementary Figure 1 The supralinear events evoked in CA3 pyramidal cells fulfill the criteria for NMDA spikes, exhibiting a threshold, sensitivity to NMDAR blockade, and all-or-none

More information

Basics of Computational Neuroscience: Neurons and Synapses to Networks

Basics of Computational Neuroscience: Neurons and Synapses to Networks Basics of Computational Neuroscience: Neurons and Synapses to Networks Bruce Graham Mathematics School of Natural Sciences University of Stirling Scotland, U.K. Useful Book Authors: David Sterratt, Bruce

More information

Part 11: Mechanisms of Learning

Part 11: Mechanisms of Learning Neurophysiology and Information: Theory of Brain Function Christopher Fiorillo BiS 527, Spring 2012 042 350 4326, fiorillo@kaist.ac.kr Part 11: Mechanisms of Learning Reading: Bear, Connors, and Paradiso,

More information

Cellular mechanisms of information transfer: neuronal and synaptic plasticity

Cellular mechanisms of information transfer: neuronal and synaptic plasticity Cellular mechanisms of information transfer: neuronal and synaptic plasticity Ivan Pavlov (UCL Institute of Neurology, UK) Anton Chizhov (Ioffe Physical Technical Institute) Pavel Zykin (St.-Petersburg

More information

Memory Systems II How Stored: Engram and LTP. Reading: BCP Chapter 25

Memory Systems II How Stored: Engram and LTP. Reading: BCP Chapter 25 Memory Systems II How Stored: Engram and LTP Reading: BCP Chapter 25 Memory Systems Learning is the acquisition of new knowledge or skills. Memory is the retention of learned information. Many different

More information

BIPN 140 Problem Set 6

BIPN 140 Problem Set 6 BIPN 140 Problem Set 6 1) Hippocampus is a cortical structure in the medial portion of the temporal lobe (medial temporal lobe in primates. a) What is the main function of the hippocampus? The hippocampus

More information

BIPN 140 Problem Set 6

BIPN 140 Problem Set 6 BIPN 140 Problem Set 6 1) The hippocampus is a cortical structure in the medial portion of the temporal lobe (medial temporal lobe in primates. a) What is the main function of the hippocampus? The hippocampus

More information

Signal detection in networks of spiking neurons with dynamical synapses

Signal detection in networks of spiking neurons with dynamical synapses Published in AIP Proceedings 887, 83-88, 7. Signal detection in networks of spiking neurons with dynamical synapses Jorge F. Mejías and Joaquín J. Torres Dept. of Electromagnetism and Physics of the Matter

More information

Supplementary Figure 1. Basic properties of compound EPSPs at

Supplementary Figure 1. Basic properties of compound EPSPs at Supplementary Figure 1. Basic properties of compound EPSPs at hippocampal CA3 CA3 cell synapses. (a) EPSPs were evoked by extracellular stimulation of the recurrent collaterals and pharmacologically isolated

More information

Synaptic plasticityhippocampus. Neur 8790 Topics in Neuroscience: Neuroplasticity. Outline. Synaptic plasticity hypothesis

Synaptic plasticityhippocampus. Neur 8790 Topics in Neuroscience: Neuroplasticity. Outline. Synaptic plasticity hypothesis Synaptic plasticityhippocampus Neur 8790 Topics in Neuroscience: Neuroplasticity Outline Synaptic plasticity hypothesis Long term potentiation in the hippocampus How it s measured What it looks like Mechanisms

More information

Synaptic plasticity and hippocampal memory

Synaptic plasticity and hippocampal memory Synaptic plasticity and hippocampal memory Tobias Bast School of Psychology, University of Nottingham tobias.bast@nottingham.ac.uk Synaptic plasticity as the neurophysiological substrate of learning Hebb

More information

Synaptic Transmission: Ionic and Metabotropic

Synaptic Transmission: Ionic and Metabotropic Synaptic Transmission: Ionic and Metabotropic D. Purves et al. Neuroscience (Sinauer Assoc.) Chapters 5, 6, 7. C. Koch. Biophysics of Computation (Oxford) Chapter 4. J.G. Nicholls et al. From Neuron to

More information

DOMINIQUE DEBANNE*, BEAT H. GÄHWILER, AND SCOTT M. THOMPSON MATERIALS AND METHODS

DOMINIQUE DEBANNE*, BEAT H. GÄHWILER, AND SCOTT M. THOMPSON MATERIALS AND METHODS Proc. Natl. Acad. Sci. USA Vol. 93, pp. 11225 11230, October 1996 Neurobiology Cooperative interactions in the induction of long-term potentiation and depression of synaptic excitation between hippocampal

More information

Synaptic Plasticity and the NMDA Receptor

Synaptic Plasticity and the NMDA Receptor Synaptic Plasticity and the NMDA Receptor Lecture 4.2 David S. Touretzky November, 2015 Long Term Synaptic Plasticity Long Term Potentiation (LTP) Reversal of LTP Long Term Depression (LTD) Reversal of

More information

BIPN140 Lecture 12: Synaptic Plasticity (II)

BIPN140 Lecture 12: Synaptic Plasticity (II) BIPN140 Lecture 12: Synaptic Plasticity (II) 1. Early v.s. Late LTP 2. Long-Term Depression 3. Molecular Mechanisms of Long-Term Depression: NMDA-R dependent 4. Molecular Mechanisms of Long-Term Depression:

More information

A form of long-lasting, learning-related synaptic plasticity in the hippocampus induced by heterosynaptic low-frequency pairing

A form of long-lasting, learning-related synaptic plasticity in the hippocampus induced by heterosynaptic low-frequency pairing A form of long-lasting, learning-related synaptic plasticity in the hippocampus induced by heterosynaptic low-frequency pairing Yan-You Huang, Christopher Pittenger*, and Eric R. Kandel Center for Neurobiology

More information

Synaptic plasticity. Activity-dependent changes in synaptic strength. Changes in innervation patterns. New synapses or deterioration of synapses.

Synaptic plasticity. Activity-dependent changes in synaptic strength. Changes in innervation patterns. New synapses or deterioration of synapses. Synaptic plasticity Activity-dependent changes in synaptic strength. Changes in innervation patterns. New synapses or deterioration of synapses. Repair/changes in the nervous system after damage. MRC Centre

More information

Supplementary Information

Supplementary Information Hyperpolarization-activated cation channels inhibit EPSPs by interactions with M-type K + channels Meena S. George, L.F. Abbott, Steven A. Siegelbaum Supplementary Information Part 1: Supplementary Figures

More information

File name: Supplementary Information Description: Supplementary Figures, Supplementary Table and Supplementary References

File name: Supplementary Information Description: Supplementary Figures, Supplementary Table and Supplementary References File name: Supplementary Information Description: Supplementary Figures, Supplementary Table and Supplementary References File name: Supplementary Data 1 Description: Summary datasheets showing the spatial

More information

STRUCTURAL ELEMENTS OF THE NERVOUS SYSTEM

STRUCTURAL ELEMENTS OF THE NERVOUS SYSTEM STRUCTURAL ELEMENTS OF THE NERVOUS SYSTEM STRUCTURE AND MAINTENANCE OF NEURONS (a) (b) Dendrites Cell body Initial segment collateral terminals (a) Diagrammatic representation of a neuron. The break in

More information

A Model of Spike-Timing Dependent Plasticity: One or Two Coincidence Detectors?

A Model of Spike-Timing Dependent Plasticity: One or Two Coincidence Detectors? RAPID COMMUNICATION J Neurophysiol 88: 507 513, 2002; 10.1152/jn.00909.2001. A Model of Spike-Timing Dependent Plasticity: One or Two Coincidence Detectors? UMA R. KARMARKAR AND DEAN V. BUONOMANO Departments

More information

Synapses and synaptic plasticity. Lubica Benuskova Lecture 8 How neurons communicate How do we learn and remember

Synapses and synaptic plasticity. Lubica Benuskova Lecture 8 How neurons communicate How do we learn and remember Synapses and synaptic plasticity Lubica Benuskova Lecture 8 How neurons communicate How do we learn and remember 1 Brain is comprised of networks of neurons connected and communicating via synapses ~10

More information

Cellular Neurobiology BIPN140

Cellular Neurobiology BIPN140 Cellular Neurobiology BIPN140 1st Midterm Exam Ready for Pickup By the elevator on the 3 rd Floor of Pacific Hall (waiver) Exam Depot Window at the north entrance to Pacific Hall (no waiver) Mon-Fri, 10:00

More information

Transitions between dierent synchronous ring modes using synaptic depression

Transitions between dierent synchronous ring modes using synaptic depression Neurocomputing 44 46 (2002) 61 67 www.elsevier.com/locate/neucom Transitions between dierent synchronous ring modes using synaptic depression Victoria Booth, Amitabha Bose Department of Mathematical Sciences,

More information

Synaptic Plasticity and Memory

Synaptic Plasticity and Memory Synaptic Plasticity and Memory Properties and synaptic mechanisms underlying the induction of long-term potentiation (LTP) The role of calcium/calmodulin-dependent kinase II (CamKII) in the induction,

More information

A Role for Synaptic Inputs at Distal Dendrites: Instructive Signals for Hippocampal Long-Term Plasticity

A Role for Synaptic Inputs at Distal Dendrites: Instructive Signals for Hippocampal Long-Term Plasticity Article A Role for Synaptic Inputs at Distal Dendrites: Instructive Signals for Hippocampal Long-Term Plasticity Joshua T. Dudman, 1 David Tsay, 1 and Steven A. Siegelbaum 1,2,3, * 1 Department of Neuroscience

More information

Dendritic Signal Integration

Dendritic Signal Integration Dendritic Signal Integration 445 Dendritic Signal Integration N Spruston, Northwestern University, Evanston, IL, USA ã 2009 Elsevier Ltd. All rights reserved. Overview: Questions Most neurons have elaborately

More information

Long-term depression and recognition of parallel "bre patterns in a multi-compartmental model of a cerebellar Purkinje cell

Long-term depression and recognition of parallel bre patterns in a multi-compartmental model of a cerebellar Purkinje cell Neurocomputing 38}40 (2001) 383}388 Long-term depression and recognition of parallel "bre patterns in a multi-compartmental model of a cerebellar Purkinje cell Volker Steuber*, Erik De Schutter Laboratory

More information

Differential Effect of TEA on Long-Term Synaptic Modification in Hippocampal CA1 and Dentate Gyrus in vitro

Differential Effect of TEA on Long-Term Synaptic Modification in Hippocampal CA1 and Dentate Gyrus in vitro Neurobiology of Learning and Memory 76, 375 387 (2001) doi:10.1006/nlme.2001.4032, available online at http://www.idealibrary.com on Differential Effect of TEA on Long-Term Synaptic Modification in Hippocampal

More information

Input-speci"c adaptation in complex cells through synaptic depression

Input-specic adaptation in complex cells through synaptic depression 0 0 0 0 Neurocomputing }0 (00) } Input-speci"c adaptation in complex cells through synaptic depression Frances S. Chance*, L.F. Abbott Volen Center for Complex Systems and Department of Biology, Brandeis

More information

Synfire chains with conductance-based neurons: internal timing and coordination with timed input

Synfire chains with conductance-based neurons: internal timing and coordination with timed input Neurocomputing 5 (5) 9 5 www.elsevier.com/locate/neucom Synfire chains with conductance-based neurons: internal timing and coordination with timed input Friedrich T. Sommer a,, Thomas Wennekers b a Redwood

More information

SUPPLEMENTARY INFORMATION

SUPPLEMENTARY INFORMATION Supplementary Figure 1. Normal AMPAR-mediated fepsp input-output curve in CA3-Psen cdko mice. Input-output curves, which are plotted initial slopes of the evoked fepsp as function of the amplitude of the

More information

previously shown (10), however, this manipulation by itself does not reliably result in the development of a large

previously shown (10), however, this manipulation by itself does not reliably result in the development of a large Proc. Nati. Acad. Sci. USA Vol. 85, pp. 9346-9350, December 1988 Neurobiology Long-term potentiation differentially affects two components of synaptic responses in hippocampus (plasticity/n-methyl-d-aspartate/d-2-amino-5-phosphonovglerate/facilitation)

More information

Sample Lab Report 1 from 1. Measuring and Manipulating Passive Membrane Properties

Sample Lab Report 1 from  1. Measuring and Manipulating Passive Membrane Properties Sample Lab Report 1 from http://www.bio365l.net 1 Abstract Measuring and Manipulating Passive Membrane Properties Biological membranes exhibit the properties of capacitance and resistance, which allow

More information

Abstract. 1 Introduction

Abstract. 1 Introduction Biophysical model of a single synaptic connection: transmission properties are determined by the cooperation of pre- and postsynaptic mechanisms Julia Trommershäuser and Annette Zippelius Institut für

More information

Multi compartment model of synaptic plasticity

Multi compartment model of synaptic plasticity Multi compartment model of synaptic plasticity E. Paxon Frady We introduce a biophysical model of a neuronal network that can accurately replicate many classical plasticity experiments. The model uses

More information

Axon initial segment position changes CA1 pyramidal neuron excitability

Axon initial segment position changes CA1 pyramidal neuron excitability Axon initial segment position changes CA1 pyramidal neuron excitability Cristina Nigro and Jason Pipkin UCSD Neurosciences Graduate Program Abstract The axon initial segment (AIS) is the portion of the

More information

Neurons! John A. White Dept. of Bioengineering

Neurons! John A. White Dept. of Bioengineering Neurons! John A. White Dept. of Bioengineering john.white@utah.edu What makes neurons different from cardiomyocytes? Morphological polarity Transport systems Shape and function of action potentials Neuronal

More information

Cellular Bioelectricity

Cellular Bioelectricity ELEC ENG 3BB3: Cellular Bioelectricity Notes for Lecture 24 Thursday, March 6, 2014 8. NEURAL ELECTROPHYSIOLOGY We will look at: Structure of the nervous system Sensory transducers and neurons Neural coding

More information

Dendritic mechanisms controlling spike-timing-dependent synaptic plasticity

Dendritic mechanisms controlling spike-timing-dependent synaptic plasticity Review TRENDS in Neurosciences Vol.30 No.9 Dendritic mechanisms controlling spike-timing-dependent synaptic plasticity Björn M. Kampa 1, Johannes J. Letzkus 2 and Greg J. Stuart 2 1 Brain Research Institute,

More information

Learning Rules for Spike Timing-Dependent Plasticity Depend on Dendritic Synapse Location

Learning Rules for Spike Timing-Dependent Plasticity Depend on Dendritic Synapse Location 10420 The Journal of Neuroscience, October 11, 2006 26(41):10420 10429 Cellular/Molecular Learning Rules for Spike Timing-Dependent Plasticity Depend on Dendritic Synapse Location Johannes J. Letzkus,

More information

Computational cognitive neuroscience: 2. Neuron. Lubica Beňušková Centre for Cognitive Science, FMFI Comenius University in Bratislava

Computational cognitive neuroscience: 2. Neuron. Lubica Beňušková Centre for Cognitive Science, FMFI Comenius University in Bratislava 1 Computational cognitive neuroscience: 2. Neuron Lubica Beňušková Centre for Cognitive Science, FMFI Comenius University in Bratislava 2 Neurons communicate via electric signals In neurons it is important

More information

Synapses throughout the brain are bidirectionally modifiable.

Synapses throughout the brain are bidirectionally modifiable. A unified model of NMDA receptor-dependent bidirectional synaptic plasticity Harel Z. Shouval*, Mark F. Bear*, and Leon N Cooper* *Institute for Brain and Neural Systems, Departments of Physics and Neuroscience,

More information

Astrocyte signaling controls spike timing-dependent depression at neocortical synapses

Astrocyte signaling controls spike timing-dependent depression at neocortical synapses Supplementary Information Astrocyte signaling controls spike timing-dependent depression at neocortical synapses Rogier Min and Thomas Nevian Department of Physiology, University of Berne, Bern, Switzerland

More information

Predictive Features of Persistent Activity Emergence in Regular Spiking and Intrinsic Bursting Model Neurons

Predictive Features of Persistent Activity Emergence in Regular Spiking and Intrinsic Bursting Model Neurons Emergence in Regular Spiking and Intrinsic Bursting Model Neurons Kyriaki Sidiropoulou, Panayiota Poirazi* Institute of Molecular Biology and Biotechnology (IMBB), Foundation for Research and Technology-Hellas

More information

Topical Review. Dendritic potassium channels in hippocampal pyramidal neurons

Topical Review. Dendritic potassium channels in hippocampal pyramidal neurons Keywords: 0642 Journal of Physiology (2000), 525.1, pp. 75 81 75 Topical Review Dendritic potassium channels in hippocampal pyramidal neurons Daniel Johnston, Dax A. Hoffman, Jeffrey C. Magee, Nicholas

More information

Intro. Comp. NeuroSci. Ch. 9 October 4, The threshold and channel memory

Intro. Comp. NeuroSci. Ch. 9 October 4, The threshold and channel memory 9.7.4 The threshold and channel memory The action potential has a threshold. In figure the area around threshold is expanded (rectangle). A current injection that does not reach the threshold does not

More information

Chapter 6 subtitles postsynaptic integration

Chapter 6 subtitles postsynaptic integration CELLULAR NEUROPHYSIOLOGY CONSTANCE HAMMOND Chapter 6 subtitles postsynaptic integration INTRODUCTION (1:56) This sixth and final chapter deals with the summation of presynaptic currents. Glutamate and

More information

Cellular Neurobiology / BIPN 140

Cellular Neurobiology / BIPN 140 SECOND MIDTERM EXAMINATION Fall, 2015 GENERAL INSTRUCTIONS 1. Please write your name on ALL 6 pages. 2. Please answer each question IN THE SPACE ALLOTTED. 1) /10 pts 2) /10 pts 3) /15 pts 4) /15 pts 5)

More information

Dynamic Stochastic Synapses as Computational Units

Dynamic Stochastic Synapses as Computational Units Dynamic Stochastic Synapses as Computational Units Wolfgang Maass Institute for Theoretical Computer Science Technische Universitat Graz A-B01O Graz Austria. email: maass@igi.tu-graz.ac.at Anthony M. Zador

More information

VS : Systemische Physiologie - Animalische Physiologie für Bioinformatiker. Neuronenmodelle III. Modelle synaptischer Kurz- und Langzeitplastizität

VS : Systemische Physiologie - Animalische Physiologie für Bioinformatiker. Neuronenmodelle III. Modelle synaptischer Kurz- und Langzeitplastizität Bachelor Program Bioinformatics, FU Berlin VS : Systemische Physiologie - Animalische Physiologie für Bioinformatiker Synaptische Übertragung Neuronenmodelle III Modelle synaptischer Kurz- und Langzeitplastizität

More information

Rolls,E.T. (2016) Cerebral Cortex: Principles of Operation. Oxford University Press.

Rolls,E.T. (2016) Cerebral Cortex: Principles of Operation. Oxford University Press. Digital Signal Processing and the Brain Is the brain a digital signal processor? Digital vs continuous signals Digital signals involve streams of binary encoded numbers The brain uses digital, all or none,

More information

Resonant synchronization of heterogeneous inhibitory networks

Resonant synchronization of heterogeneous inhibitory networks Cerebellar oscillations: Anesthetized rats Transgenic animals Recurrent model Review of literature: γ Network resonance Life simulations Resonance frequency Conclusion Resonant synchronization of heterogeneous

More information

Bursting dynamics in the brain. Jaeseung Jeong, Department of Biosystems, KAIST

Bursting dynamics in the brain. Jaeseung Jeong, Department of Biosystems, KAIST Bursting dynamics in the brain Jaeseung Jeong, Department of Biosystems, KAIST Tonic and phasic activity A neuron is said to exhibit a tonic activity when it fires a series of single action potentials

More information

Requirements for LTP Induction by Pairing in Hippocampal CA1 Pyramidal Cells

Requirements for LTP Induction by Pairing in Hippocampal CA1 Pyramidal Cells Requirements for LTP Induction by Pairing in Hippocampal CA1 Pyramidal Cells HUAN-XIN CHEN, NIKOLAI OTMAKHOV, AND JOHN LISMAN Volen Center for Complex Systems, Biology Department, Brandeis University,

More information

Neuroscience 201A (2016) - Problems in Synaptic Physiology

Neuroscience 201A (2016) - Problems in Synaptic Physiology Question 1: The record below in A shows an EPSC recorded from a cerebellar granule cell following stimulation (at the gap in the record) of a mossy fiber input. These responses are, then, evoked by stimulation.

More information

Biophysical model of AMPA receptor trafficking and its regulation during LTP/LTD

Biophysical model of AMPA receptor trafficking and its regulation during LTP/LTD Biophysical model of AMPA receptor trafficking and its regulation during LTP/LTD Berton A. Earnshaw and Paul C. Bressloff Department of Mathematics, University of Utah Salt Lake City, Utah 84112 Biophysical

More information

Beyond Vanilla LTP. Spike-timing-dependent-plasticity or STDP

Beyond Vanilla LTP. Spike-timing-dependent-plasticity or STDP Beyond Vanilla LTP Spike-timing-dependent-plasticity or STDP Hebbian learning rule asn W MN,aSN MN Δw ij = μ x j (v i - φ) learning threshold under which LTD can occur Stimulation electrode Recording electrode

More information

Reading Neuronal Synchrony with Depressing Synapses

Reading Neuronal Synchrony with Depressing Synapses NOTE Communicated by Laurence Abbott Reading Neuronal Synchrony with Depressing Synapses W. Senn Department of Neurobiology, Hebrew University, Jerusalem 4, Israel, Department of Physiology, University

More information

1) Drop off in the Bi 150 box outside Baxter 331 or to the head TA (jcolas).

1) Drop off in the Bi 150 box outside Baxter 331 or  to the head TA (jcolas). Bi/CNS/NB 150 Problem Set 3 Due: Tuesday, Oct. 27, at 4:30 pm Instructions: 1) Drop off in the Bi 150 box outside Baxter 331 or e-mail to the head TA (jcolas). 2) Submit with this cover page. 3) Use a

More information

CASE 49. What type of memory is available for conscious retrieval? Which part of the brain stores semantic (factual) memories?

CASE 49. What type of memory is available for conscious retrieval? Which part of the brain stores semantic (factual) memories? CASE 49 A 43-year-old woman is brought to her primary care physician by her family because of concerns about her forgetfulness. The patient has a history of Down syndrome but no other medical problems.

More information

Modeling Depolarization Induced Suppression of Inhibition in Pyramidal Neurons

Modeling Depolarization Induced Suppression of Inhibition in Pyramidal Neurons Modeling Depolarization Induced Suppression of Inhibition in Pyramidal Neurons Peter Osseward, Uri Magaram Department of Neuroscience University of California, San Diego La Jolla, CA 92092 possewar@ucsd.edu

More information

Model of Neural Circuit Comparing Static and Adaptive Synapses

Model of Neural Circuit Comparing Static and Adaptive Synapses Prague Medical Report / Vol. 105 (2004) No. 4, p. 369 380 369) Model of Neural Circuit Comparing Static and Adaptive Synapses Kuriščák E. 1, Maršálek P. 2,3 1 Department of Physiology of the First Faculty

More information

Shunting Inhibition Does Not Have a Divisive Effect on Firing Rates

Shunting Inhibition Does Not Have a Divisive Effect on Firing Rates Communicated by Anthony Zador Shunting Inhibition Does Not Have a Divisive Effect on Firing Rates Gary R. Holt Christof Koch Computation and Neural Systems Program, California Institute of Technology,

More information

STDP in a Bistable Synapse Model Based on CaMKII and Associated Signaling Pathways

STDP in a Bistable Synapse Model Based on CaMKII and Associated Signaling Pathways Based on CaMKII and Associated Signaling Pathways Michael Graupner 1,2,3*, Nicolas Brunel 1,2 1 Université Paris Descartes, Laboratoire de Neurophysique et Physiologie, Paris, France, 2 CNRS, UMR 8119,

More information

Neuromorphic computing

Neuromorphic computing Neuromorphic computing Robotics M.Sc. programme in Computer Science lorenzo.vannucci@santannapisa.it April 19th, 2018 Outline 1. Introduction 2. Fundamentals of neuroscience 3. Simulating the brain 4.

More information

Linking Neuronal Ensembles by Associative Synaptic Plasticity

Linking Neuronal Ensembles by Associative Synaptic Plasticity Linking Neuronal Ensembles by Associative Synaptic Plasticity Qi Yuan 1,2, Jeffry S. Isaacson 2, Massimo Scanziani 1,2,3 * 1 Department of Neurobiology, Center for Neural Circuits and Behavior, University

More information

Nature Methods: doi: /nmeth Supplementary Figure 1. Activity in turtle dorsal cortex is sparse.

Nature Methods: doi: /nmeth Supplementary Figure 1. Activity in turtle dorsal cortex is sparse. Supplementary Figure 1 Activity in turtle dorsal cortex is sparse. a. Probability distribution of firing rates across the population (notice log scale) in our data. The range of firing rates is wide but

More information

Dopamine modulation of prefrontal delay activity - Reverberatory activity and sharpness of tuning curves

Dopamine modulation of prefrontal delay activity - Reverberatory activity and sharpness of tuning curves Dopamine modulation of prefrontal delay activity - Reverberatory activity and sharpness of tuning curves Gabriele Scheler+ and Jean-Marc Fellous* +Sloan Center for Theoretical Neurobiology *Computational

More information

The Journal of Neuroscience, August 15, 1997, 17(16):

The Journal of Neuroscience, August 15, 1997, 17(16): The Journal of Neuroscience, August 15, 1997, 17(16):6470 6477 Stimulation on the Positive Phase of Hippocampal Theta Rhythm Induces Long-Term Potentiation That Can Be Depotentiated by Stimulation on the

More information

How Synapses Integrate Information and Change

How Synapses Integrate Information and Change How Synapses Integrate Information and Change Rachel Stewart class of 2016 http://neuroscience.uth.tmc.edu/s1/chapter06.html http://neuroscience.uth.tmc.edu/s1/chapter07.html Chris Cohan, Ph.D. Dept. of

More information

Lecture 22: A little Neurobiology

Lecture 22: A little Neurobiology BIO 5099: Molecular Biology for Computer Scientists (et al) Lecture 22: A little Neurobiology http://compbio.uchsc.edu/hunter/bio5099 Larry.Hunter@uchsc.edu Nervous system development Part of the ectoderm

More information

Long-Term Plasticity Is Proportional to Theta-Activity

Long-Term Plasticity Is Proportional to Theta-Activity Long-Term Plasticity Is Proportional to Theta-Activity Marian Tsanov 1,2, Denise Manahan-Vaughan 1,2 * 1 Department of Experimental Neurophysiology, Medical Faculty, Ruhr University Bochum, Bochum, Germany,

More information

Title: Plasticity of intrinsic excitability in mature granule cells of the dentate gyrus

Title: Plasticity of intrinsic excitability in mature granule cells of the dentate gyrus Title: Plasticity of intrinsic excitability in mature granule cells of the dentate gyrus Authors: Jeffrey Lopez-Rojas a1, Martin Heine b1 and Michael R. Kreutz ac1 a Research Group Neuroplasticity, b Research

More information

Bidirectional modifications in synaptic efficacy, exemplified

Bidirectional modifications in synaptic efficacy, exemplified Capture of a protein synthesis-dependent component of long-term depression Beth S. Kauderer* and Eric R. Kandel* Howard Hughes Medical Institute and *Center for Neurobiology and Behavior, College of Physicians

More information

SUPPLEMENTARY INFORMATION

SUPPLEMENTARY INFORMATION doi: 1.138/nature6416 Supplementary Notes Spine Ca 2+ signals produced by glutamate uncaging We imaged uncaging-evoked [Ca 2+ ] transients in neurons loaded with a green Ca 2+ - sensitive indicator (G;

More information

Using An Expanded Morris-Lecar Model to Determine Neuronal Dynamics In the Event of Traumatic Brain Injury

Using An Expanded Morris-Lecar Model to Determine Neuronal Dynamics In the Event of Traumatic Brain Injury Using An Expanded Morris-Lecar Model to Determine Neuronal Dynamics In the Event of Traumatic Brain Injury Ryan W. Tam rwtam@ucsd.edu Department of Bioengineering University of California San Diego La

More information

CYTOARCHITECTURE OF CEREBRAL CORTEX

CYTOARCHITECTURE OF CEREBRAL CORTEX BASICS OF NEUROBIOLOGY CYTOARCHITECTURE OF CEREBRAL CORTEX ZSOLT LIPOSITS 1 CELLULAR COMPOSITION OF THE CEREBRAL CORTEX THE CEREBRAL CORTEX CONSISTS OF THE ARCHICORTEX (HIPPOCAMPAL FORMA- TION), PALEOCORTEX

More information

Synchrony Generation in Recurrent Networks with Frequency-Dependent Synapses

Synchrony Generation in Recurrent Networks with Frequency-Dependent Synapses The Journal of Neuroscience, 2000, Vol. 20 RC50 1of5 Synchrony Generation in Recurrent Networks with Frequency-Dependent Synapses Misha Tsodyks, Asher Uziel, and Henry Markram Department of Neurobiology,

More information

Ube3a is required for experience-dependent maturation of the neocortex

Ube3a is required for experience-dependent maturation of the neocortex Ube3a is required for experience-dependent maturation of the neocortex Koji Yashiro, Thorfinn T. Riday, Kathryn H. Condon, Adam C. Roberts, Danilo R. Bernardo, Rohit Prakash, Richard J. Weinberg, Michael

More information

THE EFFECT OF TETANIC STIMULATION ON FUNCTIONAL CONNECTIVITY

THE EFFECT OF TETANIC STIMULATION ON FUNCTIONAL CONNECTIVITY REPORT THE EFFECT OF TETANIC STIMULATION ON FUNCTIONAL CONNECTIVITY Tim Witteveen FACULTY OF ELECTRICAL ENGINEERING, MATHEMATICS AND COMPUTER SCIENCE BIOMEDICAL SIGNALS AND SYSTEMS EXAMINATION COMMITTEE

More information

Information Processing During Transient Responses in the Crayfish Visual System

Information Processing During Transient Responses in the Crayfish Visual System Information Processing During Transient Responses in the Crayfish Visual System Christopher J. Rozell, Don. H. Johnson and Raymon M. Glantz Department of Electrical & Computer Engineering Department of

More information

JESSY JOHN* and ROHIT MANCHANDA

JESSY JOHN* and ROHIT MANCHANDA Modulation of synaptic potentials and cell excitability by dendritic K IR and K A s channels in nucleus accumbens medium spiny neurons: A computational study JESSY JOHN* and ROHIT MANCHANDA Biomedical

More information

Fear conditioning induces associative long-term potentiation in the amygdala

Fear conditioning induces associative long-term potentiation in the amygdala 11 December 1997 Nature 390, 604-607 (1997) Macmillan Publishers Ltd. Fear conditioning induces associative long-term potentiation in the amygdala MICHAEL T. ROGAN, URSULA V. STÄUBLI & JOSEPH E. LEDOUX

More information

5-Nervous system II: Physiology of Neurons

5-Nervous system II: Physiology of Neurons 5-Nervous system II: Physiology of Neurons AXON ION GRADIENTS ACTION POTENTIAL (axon conduction) GRADED POTENTIAL (cell-cell communication at synapse) SYNAPSE STRUCTURE & FUNCTION NEURAL INTEGRATION CNS

More information

Structure of a Neuron:

Structure of a Neuron: Structure of a Neuron: At the dendrite the incoming signals arrive (incoming currents) At the soma current are finally integrated. At the axon hillock action potential are generated if the potential crosses

More information

Dendritic Mechanisms of Phase Precession in Hippocampal CA1 Pyramidal Neurons

Dendritic Mechanisms of Phase Precession in Hippocampal CA1 Pyramidal Neurons RAPID COMMUNICATION Dendritic Mechanisms of Phase Precession in Hippocampal CA1 Pyramidal Neurons JEFFREY C. MAGEE Neuroscience Center, Louisiana State University Medical Center, New Orleans, Louisiana

More information

GABA B Receptor-Mediated Presynaptic Inhibition Has History-Dependent Effects on Synaptic Transmission during Physiologically Relevant Spike Trains

GABA B Receptor-Mediated Presynaptic Inhibition Has History-Dependent Effects on Synaptic Transmission during Physiologically Relevant Spike Trains The Journal of Neuroscience, June 15, 2003 23(12):4809 4814 4809 Brief Communication GABA B Receptor-Mediated Presynaptic Inhibition Has History-Dependent Effects on Synaptic Transmission during Physiologically

More information

A New Principle for Information Storage in an Enzymatic Pathway Model

A New Principle for Information Storage in an Enzymatic Pathway Model A New Principle for Information Storage in an Enzymatic Pathway Model Bruno Delord 1*, Hugues Berry 2, Emmanuel Guigon 1, Stéphane Genet 1 1 Institut National de la Santé et de la Recherche Médicale (INSERM),

More information

TREATMENT-SPECIFIC ABNORMAL SYNAPTIC PLASTICITY IN EARLY PARKINSON S DISEASE

TREATMENT-SPECIFIC ABNORMAL SYNAPTIC PLASTICITY IN EARLY PARKINSON S DISEASE TREATMENT-SPECIFIC ABNORMAL SYNAPTIC PLASTICITY IN EARLY PARKINSON S DISEASE Angel Lago-Rodriguez 1, Binith Cheeran 2 and Miguel Fernández-Del-Olmo 3 1. Prism Lab, Behavioural Brain Sciences, School of

More information

How Synapses Integrate Information and Change

How Synapses Integrate Information and Change How Synapses Integrate Information and Change Rachel Stewart class of 2016 https://nba.uth.tmc.edu/neuroscience/s1/chapter06.html https://nba.uth.tmc.edu/neuroscience/s1/chapter07.html Chris Cohan, Ph.D.

More information

Bidirectional NMDA receptor plasticity controls CA3 output and heterosynaptic metaplasticity

Bidirectional NMDA receptor plasticity controls CA3 output and heterosynaptic metaplasticity Bidirectional NMDA receptor plasticity controls CA output and heterosynaptic metaplasticity David L. Hunt, Nagore Puente, Pedro Grandes, Pablo E. Castillo a NMDAR EPSC (pa) - - -8-6 -4 - st 5 nd 5 b NMDAR

More information

Increased Susceptibility to Induction of Long-Term Depression and Long-Term Potentiation Reversal during Aging

Increased Susceptibility to Induction of Long-Term Depression and Long-Term Potentiation Reversal during Aging The Journal of Neuroscience, September 1, 1996, 16(17):5382 5392 Increased Susceptibility to Induction of Long-Term Depression and Long-Term Potentiation Reversal during Aging Christopher M. Norris, Donna

More information

The Role of Mitral Cells in State Dependent Olfactory Responses. Trygve Bakken & Gunnar Poplawski

The Role of Mitral Cells in State Dependent Olfactory Responses. Trygve Bakken & Gunnar Poplawski The Role of Mitral Cells in State Dependent Olfactory Responses Trygve akken & Gunnar Poplawski GGN 260 Neurodynamics Winter 2008 bstract Many behavioral studies have shown a reduced responsiveness to

More information

Inhibition: Effects of Timing, Time Scales and Gap Junctions

Inhibition: Effects of Timing, Time Scales and Gap Junctions Inhibition: Effects of Timing, Time Scales and Gap Junctions I. Auditory brain stem neurons and subthreshold integ n. Fast, precise (feed forward) inhibition shapes ITD tuning. Facilitating effects of

More information

Spike Timing-Dependent Plasticity: From Synapse to Perception

Spike Timing-Dependent Plasticity: From Synapse to Perception Spike Timing-Dependent Plasticity: From Synapse to Perception Yang Dan and Mu-Ming Poo Physiol Rev 86:1033-1048, 2006. doi:10.1152/physrev.00030.2005 You might find this additional information useful...

More information

Dep. Control Time (min)

Dep. Control Time (min) aa Control Dep. RP 1s 1 mv 2s 1 mv b % potentiation of IPSP 2 15 1 5 Dep. * 1 2 3 4 Time (min) Supplementary Figure 1. Rebound potentiation of IPSPs in PCs. a, IPSPs recorded with a K + gluconate pipette

More information

NS200: In vitro electrophysiology section September 11th, 2013

NS200: In vitro electrophysiology section September 11th, 2013 NS200: In vitro electrophysiology section September 11th, 2013 Quynh Anh Nguyen, 4 th Year Nicoll Lab quynhanh.nguyen@ucsf.edu N276 Genentech Hall, Mission Bay Outline Part I: Theory Review of circuit

More information

Timing and the cerebellum (and the VOR) Neurophysiology of systems 2010

Timing and the cerebellum (and the VOR) Neurophysiology of systems 2010 Timing and the cerebellum (and the VOR) Neurophysiology of systems 2010 Asymmetry in learning in the reverse direction Full recovery from UP using DOWN: initial return to naïve values within 10 minutes,

More information