Abstract:The magnitude and direction of synaptic plasticity can be determined by the precise timing of presynaptic and postsynaptic action potentials on a millisecond timescale. In vivo, however, neural activity has structure on longer timescales. Here we show that plasticity at the CA3-CA1 synapse depends strongly on parameters other than millisecond spike timing. As a result, the notion that a single spiketiming-dependent plasticity (STDP) rule alone can fully describe the mapping between neural activity and synapse … Show more
“…Recent experiments conducted in acute hippocampal slices, which closely approximate the conditions present in vivo, have demonstrated that the plasticity of CA3-CA1 synapses is jointly dependent upon the temporal offset of pre-and post-synaptic firing, number of post-synaptic spikes fired, frequency of spike pairings, and duration of stimulation [28][29][30]. Firstly, we aim to ascertain whether the Calcium control hypothesis -which has been demonstrated to successfully reproduce earlier STDP data obtained in culture (Figure 1a), as well as that induced by other activity patterns -can be revised to account for this joint dependency [18,[41][42][43][44][45][46][47][48].…”
Section: Induction Of Synaptic Plasticity By Spike-timing Stimulationmentioning
confidence: 99%
“…The experimental data we aim to replicate can be characterised by considering the effects of two different stimulation protocols -pairing 100 single preand post-synaptic spikes (hereafter referred to as 'spike pairing') at low frequencies (0.1-5Hz), which generates a depression-only learning rule ( Figure 1b); or pairing a single pre-synaptic spike with two post-synaptic spikes (hereafter referred to as 'triplet pairing'), which generates a triphasic bidirectional learning rule after 100 pairings at a frequency of 5Hz (Figure 1c), an unsaturated potentiation-only rule after 30 pairings at 5Hz (Figure 1d), or mild depression after 100 pairings at a frequency of 0.5Hz (data not shown). Each of these data sets can be fitted by a learning rule composed of Gaussian (or a sum of Gaussian) curves centred at short, positive temporal offsets [29]. [26] and (b-d) acute slice preparations [29].…”
Section: Induction Of Synaptic Plasticity By Spike-timing Stimulationmentioning
confidence: 99%
“…However, more recent examinations using acute hippocampal slices have been unable to induce bidirectional plasticity with pairs of single preand post-synaptic action potentials under standard recording conditions [ Figure 1b; 28-30, 33, 34]. These results suggest a more complex picture, where synaptic plasticity is dependent not just on relative spike timing, but also on the frequency, duration and nature of spike pairings -with a triphasic STDP curve obtained at CA3-CA1 synapses only when pairings are delivered at approximately theta frequency (>5Hz) and involve multiple post-synaptic spikes [20,29,32; Figure 1c]. Similar results have been obtained at excitatory connections between cortical pyramidal neurons [35].…”
Section: Introductionmentioning
confidence: 96%
“…Initially, empirical observations of synaptic plasticity were mediated by tetanic stimulation protocols, with high frequency stimulation (HFS; typically 1s of 100Hz afferent firing) used to induce LTP and low frequency stimulation (LFS; typically 15mins of 1Hz afferent firing) used to induce LTD [4,5]. In more recent years, it has also been established that temporally correlated pairs or triplets of pre-and post-synaptic action potentials delivered at low frequencies can induce bidirectional spike-timing dependent plasticity (STDP) depending, among other parameters, on their exact temporal offset over a range of ~100ms [26][27][28][29][30]. STDP has been examined in a variety of cortical regions and species, and its discovery has both accompanied and accelerated a move in computational neuroscience from rate to temporally coded models of cognitive processing [31,32].…”
Section: Introductionmentioning
confidence: 99%
“…Other experimental data indicates that potentiation and depression events are switch-like transitions between binary conductance states, mediated by kinase and phosphotase pathways that are co-activated and competitive [36][37][38][39][40]. The kinetics of kinase and phosphotase activation also differ significantly, as LTP can be rapidly induced by appropriate patterns of activity while LTD requires prolonged stimulation [15,17,29]. Computational modelling of synaptic plasticity has demonstrated that the dynamics of Calcium influx through the NMDA receptor (NMDAr) is sufficient to account for empirical data obtained using multiple stimulation protocols, integrating an array of experimental results within a single theoretical framework [18,[41][42][43][44][45][46][47][48].…”
Synaptic plasticity is believed to represent the neural correlate of mammalian learning and memory function. It has been demonstrated that changes in synaptic conductance can be induced by approximately synchronous pairings of pre-and post-synaptic action potentials delivered at low frequencies. It has also been established that NMDAr-dependent Calcium influx into dendritic spines represents the critical signal for plasticity induction, and can account for this spike-timing dependent plasticity (STDP) as well as experimental data obtained using other stimulation protocols. However, subsequent empirical studies have delineated a more complex relationship between spike-timing, firing rate, stimulus duration and post-synaptic bursting in dictating changes in the conductance of hippocampal excitatory synapses. It has yet to be established whether the Calcium control hypothesis can account for this more recent data. Here, we present a detailed biophysical model of single dendritic spines on a CA1 pyramidal neuron, describe the NMDAr-dependent Calcium influx generated by different stimulation protocols, and present a parsimonious model of Calcium driven kinase and phosphotase dynamics that dictate transitions between binary synaptic weight states. We demonstrate the manner in which this model can account for various experimental observations of synaptic plasticity and be used to make predictions regarding the dynamics of depolarisation and NMDAr activation generated by STDP protocols as well as the synaptic weight change induced under other experimental conditions. We then discuss how this parsimonious, unified computational model of synaptic plasticity might be utilised to appraise the activity-dependent refinement of neural circuitry induced by more realistic firing patterns.
IntroductionSynaptic plasticity -the process of activity dependent change in synaptic conductance -is widely believed to represent the neural correlate of mammalian learning and memory function [1][2][3]. Since the first experimental demonstrations of long-term potentiation (LTP) and depression (LTD), a wealth of empirical data regarding the induction, expression and maintenance of synaptic plasticity in different cortical regions has been obtained [4][5][6][7][8]. In spite of the heterogeneity of plasticity mechanisms observed throughout the brain, changes in the strength of excitatory synapses afferent on CA1 pyramidal neurons in the hippocampus represent the best studied form in the mammalian cortex [9][10][11][12]. At these synapses, Calcium influx into dendritic spines represents the critical signal for synaptic plasticity induction [13][14][15][16][17][18][19][20]. Large, transient elevations in intracellular [Ca 2+ ] generate LTP via the preferential activation of kinase pathways while modest, sustained elevations in intracellular [Ca 2+ ] generate LTD via the preferential activation of phosphotase pathways [21][22][23][24][25]. Initially, empirical observations of synaptic plasticity were mediated by tetanic stimulation protoc...
“…Recent experiments conducted in acute hippocampal slices, which closely approximate the conditions present in vivo, have demonstrated that the plasticity of CA3-CA1 synapses is jointly dependent upon the temporal offset of pre-and post-synaptic firing, number of post-synaptic spikes fired, frequency of spike pairings, and duration of stimulation [28][29][30]. Firstly, we aim to ascertain whether the Calcium control hypothesis -which has been demonstrated to successfully reproduce earlier STDP data obtained in culture (Figure 1a), as well as that induced by other activity patterns -can be revised to account for this joint dependency [18,[41][42][43][44][45][46][47][48].…”
Section: Induction Of Synaptic Plasticity By Spike-timing Stimulationmentioning
confidence: 99%
“…The experimental data we aim to replicate can be characterised by considering the effects of two different stimulation protocols -pairing 100 single preand post-synaptic spikes (hereafter referred to as 'spike pairing') at low frequencies (0.1-5Hz), which generates a depression-only learning rule ( Figure 1b); or pairing a single pre-synaptic spike with two post-synaptic spikes (hereafter referred to as 'triplet pairing'), which generates a triphasic bidirectional learning rule after 100 pairings at a frequency of 5Hz (Figure 1c), an unsaturated potentiation-only rule after 30 pairings at 5Hz (Figure 1d), or mild depression after 100 pairings at a frequency of 0.5Hz (data not shown). Each of these data sets can be fitted by a learning rule composed of Gaussian (or a sum of Gaussian) curves centred at short, positive temporal offsets [29]. [26] and (b-d) acute slice preparations [29].…”
Section: Induction Of Synaptic Plasticity By Spike-timing Stimulationmentioning
confidence: 99%
“…However, more recent examinations using acute hippocampal slices have been unable to induce bidirectional plasticity with pairs of single preand post-synaptic action potentials under standard recording conditions [ Figure 1b; 28-30, 33, 34]. These results suggest a more complex picture, where synaptic plasticity is dependent not just on relative spike timing, but also on the frequency, duration and nature of spike pairings -with a triphasic STDP curve obtained at CA3-CA1 synapses only when pairings are delivered at approximately theta frequency (>5Hz) and involve multiple post-synaptic spikes [20,29,32; Figure 1c]. Similar results have been obtained at excitatory connections between cortical pyramidal neurons [35].…”
Section: Introductionmentioning
confidence: 96%
“…Initially, empirical observations of synaptic plasticity were mediated by tetanic stimulation protocols, with high frequency stimulation (HFS; typically 1s of 100Hz afferent firing) used to induce LTP and low frequency stimulation (LFS; typically 15mins of 1Hz afferent firing) used to induce LTD [4,5]. In more recent years, it has also been established that temporally correlated pairs or triplets of pre-and post-synaptic action potentials delivered at low frequencies can induce bidirectional spike-timing dependent plasticity (STDP) depending, among other parameters, on their exact temporal offset over a range of ~100ms [26][27][28][29][30]. STDP has been examined in a variety of cortical regions and species, and its discovery has both accompanied and accelerated a move in computational neuroscience from rate to temporally coded models of cognitive processing [31,32].…”
Section: Introductionmentioning
confidence: 99%
“…Other experimental data indicates that potentiation and depression events are switch-like transitions between binary conductance states, mediated by kinase and phosphotase pathways that are co-activated and competitive [36][37][38][39][40]. The kinetics of kinase and phosphotase activation also differ significantly, as LTP can be rapidly induced by appropriate patterns of activity while LTD requires prolonged stimulation [15,17,29]. Computational modelling of synaptic plasticity has demonstrated that the dynamics of Calcium influx through the NMDA receptor (NMDAr) is sufficient to account for empirical data obtained using multiple stimulation protocols, integrating an array of experimental results within a single theoretical framework [18,[41][42][43][44][45][46][47][48].…”
Synaptic plasticity is believed to represent the neural correlate of mammalian learning and memory function. It has been demonstrated that changes in synaptic conductance can be induced by approximately synchronous pairings of pre-and post-synaptic action potentials delivered at low frequencies. It has also been established that NMDAr-dependent Calcium influx into dendritic spines represents the critical signal for plasticity induction, and can account for this spike-timing dependent plasticity (STDP) as well as experimental data obtained using other stimulation protocols. However, subsequent empirical studies have delineated a more complex relationship between spike-timing, firing rate, stimulus duration and post-synaptic bursting in dictating changes in the conductance of hippocampal excitatory synapses. It has yet to be established whether the Calcium control hypothesis can account for this more recent data. Here, we present a detailed biophysical model of single dendritic spines on a CA1 pyramidal neuron, describe the NMDAr-dependent Calcium influx generated by different stimulation protocols, and present a parsimonious model of Calcium driven kinase and phosphotase dynamics that dictate transitions between binary synaptic weight states. We demonstrate the manner in which this model can account for various experimental observations of synaptic plasticity and be used to make predictions regarding the dynamics of depolarisation and NMDAr activation generated by STDP protocols as well as the synaptic weight change induced under other experimental conditions. We then discuss how this parsimonious, unified computational model of synaptic plasticity might be utilised to appraise the activity-dependent refinement of neural circuitry induced by more realistic firing patterns.
IntroductionSynaptic plasticity -the process of activity dependent change in synaptic conductance -is widely believed to represent the neural correlate of mammalian learning and memory function [1][2][3]. Since the first experimental demonstrations of long-term potentiation (LTP) and depression (LTD), a wealth of empirical data regarding the induction, expression and maintenance of synaptic plasticity in different cortical regions has been obtained [4][5][6][7][8]. In spite of the heterogeneity of plasticity mechanisms observed throughout the brain, changes in the strength of excitatory synapses afferent on CA1 pyramidal neurons in the hippocampus represent the best studied form in the mammalian cortex [9][10][11][12]. At these synapses, Calcium influx into dendritic spines represents the critical signal for synaptic plasticity induction [13][14][15][16][17][18][19][20]. Large, transient elevations in intracellular [Ca 2+ ] generate LTP via the preferential activation of kinase pathways while modest, sustained elevations in intracellular [Ca 2+ ] generate LTD via the preferential activation of phosphotase pathways [21][22][23][24][25]. Initially, empirical observations of synaptic plasticity were mediated by tetanic stimulation protoc...
Flexible neuromorphic electronics that emulate biological neuronal systems constitute a promising candidate for next‐generation wearable computing, soft robotics, and neuroprosthetics. For realization, with the achievement of simple synaptic behaviors in a single device, the construction of artificial synapses with various functions of sensing and responding and integrated systems to mimic complicated computing, sensing, and responding in biological systems is a prerequisite. Artificial synapses that have learning ability can perceive and react to events in the real world; these abilities expand the neuromorphic applications toward health monitoring and cybernetic devices in the future Internet of Things. To demonstrate the flexible neuromorphic systems successfully, it is essential to develop artificial synapses and nerves replicating the functionalities of the biological counterparts and satisfying the requirements for constructing the elements and the integrated systems such as flexibility, low power consumption, high‐density integration, and biocompatibility. Here, the progress of flexible neuromorphic electronics is addressed, from basic backgrounds including synaptic characteristics, device structures, and mechanisms of artificial synapses and nerves, to applications for computing, soft robotics, and neuroprosthetics. Finally, future research directions toward wearable artificial neuromorphic systems are suggested for this emerging area.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.