Bursts as a unit of neural information: making unreliable synapses reliable

Lisman, John

doi:10.1016/s0166-2236(96)10070-9

Cited by 1,225 publications

(937 citation statements)

References 49 publications

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“…Our results show that information from a bursting neuron must be decoded non-linearly while a linear decoder could suffice for a tonically firing one. The decoding scheme proposed in Lisman (1997) is non-linear in nature and our results are consistent with the hypothesis that bursts could reduce synaptic unreliability.…”

Section: Bursting Neurons Are Non-linearsupporting

confidence: 88%

“…While it has been shown that both tonically firing and bursting neurons can encode information (Reinagel et al, 1999), one possible function of bursting is to detect specific stimulus features and some results do support this hypothesis Sherman, 2001;Kepecs et al, 2002). On the other hand, bursting behavior might simply be used to improve synaptic reliability (Lisman, 1997).…”

Section: Introductionmentioning

confidence: 99%

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To Burst or Not to Burst?

2004

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It is well known that some neurons tend to fire packets of action potentials followed by periods of quiescence (bursts) while others within the same stage of sensory processing fire in a tonic manner. However, the respective computational advantages of bursting and tonic neurons for encoding time varying signals largely remain a mystery. Weakly electric fish use cutaneous electroreceptors to convey information about sensory stimuli and it has been shown that some electroreceptors exhibit bursting dynamics while others do not. In this study, we compare the neural coding capabilities of tonically firing and bursting electroreceptor model neurons using information theoretic measures. We find that both bursting and tonically firing model neurons efficiently transmit information about the stimulus. However, the decoding mechanisms that must be used for each differ greatly: a non-linear decoder would be required to extract all the available information transmitted by the bursting model neuron whereas a linear one might suffice for the tonically firing model neuron. Further investigations using stimulus reconstruction techniques reveal that, unlike the tonically firing model neuron, the bursting model neuron does not encode the detailed time course of the stimulus. A novel measure of feature detection reveals that the bursting neuron signals certain stimulus features. Finally, we show that feature extraction and stimulus estimation are mutually exclusive computations occurring in bursting and tonically firing model neurons, respectively. Our results therefore suggest that stimulus estimation and feature extraction might be parallel computations in certain sensory systems rather than being sequential as has been previously proposed.

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Section: Bursting Neurons Are Non-linearsupporting

confidence: 88%

Section: Introductionmentioning

confidence: 99%

To Burst or Not to Burst?

2004

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“…This suggests a code in which bursts of larger length are more likely to be elicited by stimuli with larger amplitudes. This is consistent with the proposed functional role of feature detection by bursts as bursts of larger length are more likely to overcome synaptic unreliability and thus to be transmitted to higher brain centers (Lisman 1997).…”

Section: Do Bursts Contain Information About Sensory Stimuli In Theirsupporting

confidence: 87%

In vivo conditions influence the coding of stimulus features by bursts of action potentials

Akerberg

Chacron

2011

J Comput Neurosci

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The functional role of burst firing (i.e. the firing of packets of action potentials followed by quiescence) in sensory processing is still under debate. Should bursts be considered as unitary events that signal the presence of a particular feature in the sensory environment or is information about stimulus attributes contained within their temporal structure? We compared the coding of stimulus attributes by bursts in vivo and in vitro of electrosensory pyramidal neurons in weakly electric fish by computing correlations between burst and stimulus attributes. Our results show that, while these correlations were strong in magnitude and significant in vitro, they were actually much weaker in magnitude if at all significant in vivo. We used a mathematical model of pyramidal neuron activity in vivo and showed that such a model could reproduce the correlations seen in vitro, thereby suggesting that differences in burst coding were not due to differences in bursting seen in vivo and in vitro. We next tested whether variability in the baseline (i.e. without stimulation) activity of ELL pyramidal neurons could account for these differences. To do so, we injected noise into our model whose intensity was calibrated to mimic baseline activity variability as quantified by the coefficient of variation. We found that this noise caused significant decreases in the magnitude of correlations between burst and stimulus attributes and could account for differences between in vitro and in vivo conditions. We then tested this prediction experimentally by directly injecting noise in vitro through the recording electrode. Our results show that this caused a lowering in magnitude of the correlations between burst and stimulus attributes in vitro and gave rise to values that were quantitatively similar to those seen under in vivo conditions.While it is expected that noise in the form of baseline activity variability will lower correlations between burst and stimulus attributes, our results show that such variability can account for differences seen in vivo. Thus, the high variability seen under in vivo conditions has profound consequences on the coding of information by bursts in ELL pyramidal neurons. In particular, our results support the viewpoint that bursts serve as a detector of particular stimulus features but do not carry detailed information about such features in their structure.

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“…However, it seems likely that multiple alterations in neural activity or neuromodulatory tone are generated by novelty or enhanced learning and may have a more significant effect on the dynamics of synaptic plasticity. The requirement for multiple post-synaptic spikes to induce LTP, for example, implies that bursting may be the primary mode of encoding salient information in the hippocampus [74,75]. Further empirical studies are required to delineate the changes in neural activity observed during periods of enhanced learning, and the manner in which these changes contribute to the induction of bidirectional synaptic plasticity.…”

Section: Discussionmentioning

confidence: 99%

Calcium control of triphasic hippocampal STDP

Bush¹,

Jin

2012

J Comput Neurosci

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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...

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Bursts as a unit of neural information: making unreliable synapses reliable

Cited by 1,225 publications

References 49 publications

To Burst or Not to Burst?

To Burst or Not to Burst?

In vivo conditions influence the coding of stimulus features by bursts of action potentials

Calcium control of triphasic hippocampal STDP

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