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Conversion of phase information into a spike-count code by bursting neurons.

Samengo I, Montemurro MA - PLoS ONE (2010)

Bottom Line: We show that the number of spikes per burst varies systematically with the phase of the fluctuating input at the time of burst onset.The mapping between input phase and number of spikes per burst is a robust response feature for a broad range of stimulus statistics.Our results suggest that cortical bursting neurons could play a crucial role in translating LFP phase information into an easily decodable spike count code.

View Article: PubMed Central - PubMed

Affiliation: Centro Atómico Bariloche and Instituto Balseiro, San Carlos de Bariloche, Argentina.

ABSTRACT
Single neurons in the cerebral cortex are immersed in a fluctuating electric field, the local field potential (LFP), which mainly originates from synchronous synaptic input into the local neural neighborhood. As shown by recent studies in visual and auditory cortices, the angular phase of the LFP at the time of spike generation adds significant extra information about the external world, beyond the one contained in the firing rate alone. However, no biologically plausible mechanism has yet been suggested that allows downstream neurons to infer the phase of the LFP at the soma of their pre-synaptic afferents. Therefore, so far there is no evidence that the nervous system can process phase information. Here we study a model of a bursting pyramidal neuron, driven by a time-dependent stimulus. We show that the number of spikes per burst varies systematically with the phase of the fluctuating input at the time of burst onset. The mapping between input phase and number of spikes per burst is a robust response feature for a broad range of stimulus statistics. Our results suggest that cortical bursting neurons could play a crucial role in translating LFP phase information into an easily decodable spike count code.

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Response to a constant input current.(A). Membrane potential traces for different intensities of the input current. In the lower panels (B–D), the colored symbols correspond to the traces in A of matching colour. (B) Spike firing rate as a function of the input current intensity. The two singular points correspond to the onset of firing at  nA, and the change in the size of bursts from 3 spike bursts to 4 spike bursts at  nA. (C) Burst size (in number of spikes per burst, n) as a function of the input current. (D) Inter-burst time interval Δt. Apart from the discontinuity at the onset of firing, the interval between bursts decreases smoothly as the input current increases. (E) Intra-burst inter-spike interval δt. After a rapid adjustment following the singular points corresponding to firing onset and burst size transition, the intra-burst ISI remains essentially unaffected by variations on the constant input.
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pone-0009669-g003: Response to a constant input current.(A). Membrane potential traces for different intensities of the input current. In the lower panels (B–D), the colored symbols correspond to the traces in A of matching colour. (B) Spike firing rate as a function of the input current intensity. The two singular points correspond to the onset of firing at nA, and the change in the size of bursts from 3 spike bursts to 4 spike bursts at nA. (C) Burst size (in number of spikes per burst, n) as a function of the input current. (D) Inter-burst time interval Δt. Apart from the discontinuity at the onset of firing, the interval between bursts decreases smoothly as the input current increases. (E) Intra-burst inter-spike interval δt. After a rapid adjustment following the singular points corresponding to firing onset and burst size transition, the intra-burst ISI remains essentially unaffected by variations on the constant input.

Mentions: As a first step, we considered constant input currents, which are useful to motivate the study of more natural signals (see below). When driven with a constant stimulus, after an initial transient period model neurons set onto a periodic firing regime (Figure 3A). The mean firing rate, the intra-burst period and the inter-spike interval within each burst depend on the intensity of the input current, as explained in the supporting Text S1 and Figure 3B, D and E. The number of spikes per burst n, however, changes much more slowly as the input current is varied. For instance, while the firing rate varies from around 30 Hz to 50 Hz, the number of spikes per burst remains fixed at 5. This rigid behavior of the burst size contrasts with the flexibility observed in Figure 2B, where a broad variation in n-values is observed. The wider range of burst sizes obtained with time-dependent stimuli suggests that n encodes dynamic stimulus features. To explore this hypothesis in a systematic way, in the following sections we used time-varying stimuli of increasing complexity.


Conversion of phase information into a spike-count code by bursting neurons.

Samengo I, Montemurro MA - PLoS ONE (2010)

Response to a constant input current.(A). Membrane potential traces for different intensities of the input current. In the lower panels (B–D), the colored symbols correspond to the traces in A of matching colour. (B) Spike firing rate as a function of the input current intensity. The two singular points correspond to the onset of firing at  nA, and the change in the size of bursts from 3 spike bursts to 4 spike bursts at  nA. (C) Burst size (in number of spikes per burst, n) as a function of the input current. (D) Inter-burst time interval Δt. Apart from the discontinuity at the onset of firing, the interval between bursts decreases smoothly as the input current increases. (E) Intra-burst inter-spike interval δt. After a rapid adjustment following the singular points corresponding to firing onset and burst size transition, the intra-burst ISI remains essentially unaffected by variations on the constant input.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2837377&req=5

pone-0009669-g003: Response to a constant input current.(A). Membrane potential traces for different intensities of the input current. In the lower panels (B–D), the colored symbols correspond to the traces in A of matching colour. (B) Spike firing rate as a function of the input current intensity. The two singular points correspond to the onset of firing at nA, and the change in the size of bursts from 3 spike bursts to 4 spike bursts at nA. (C) Burst size (in number of spikes per burst, n) as a function of the input current. (D) Inter-burst time interval Δt. Apart from the discontinuity at the onset of firing, the interval between bursts decreases smoothly as the input current increases. (E) Intra-burst inter-spike interval δt. After a rapid adjustment following the singular points corresponding to firing onset and burst size transition, the intra-burst ISI remains essentially unaffected by variations on the constant input.
Mentions: As a first step, we considered constant input currents, which are useful to motivate the study of more natural signals (see below). When driven with a constant stimulus, after an initial transient period model neurons set onto a periodic firing regime (Figure 3A). The mean firing rate, the intra-burst period and the inter-spike interval within each burst depend on the intensity of the input current, as explained in the supporting Text S1 and Figure 3B, D and E. The number of spikes per burst n, however, changes much more slowly as the input current is varied. For instance, while the firing rate varies from around 30 Hz to 50 Hz, the number of spikes per burst remains fixed at 5. This rigid behavior of the burst size contrasts with the flexibility observed in Figure 2B, where a broad variation in n-values is observed. The wider range of burst sizes obtained with time-dependent stimuli suggests that n encodes dynamic stimulus features. To explore this hypothesis in a systematic way, in the following sections we used time-varying stimuli of increasing complexity.

Bottom Line: We show that the number of spikes per burst varies systematically with the phase of the fluctuating input at the time of burst onset.The mapping between input phase and number of spikes per burst is a robust response feature for a broad range of stimulus statistics.Our results suggest that cortical bursting neurons could play a crucial role in translating LFP phase information into an easily decodable spike count code.

View Article: PubMed Central - PubMed

Affiliation: Centro Atómico Bariloche and Instituto Balseiro, San Carlos de Bariloche, Argentina.

ABSTRACT
Single neurons in the cerebral cortex are immersed in a fluctuating electric field, the local field potential (LFP), which mainly originates from synchronous synaptic input into the local neural neighborhood. As shown by recent studies in visual and auditory cortices, the angular phase of the LFP at the time of spike generation adds significant extra information about the external world, beyond the one contained in the firing rate alone. However, no biologically plausible mechanism has yet been suggested that allows downstream neurons to infer the phase of the LFP at the soma of their pre-synaptic afferents. Therefore, so far there is no evidence that the nervous system can process phase information. Here we study a model of a bursting pyramidal neuron, driven by a time-dependent stimulus. We show that the number of spikes per burst varies systematically with the phase of the fluctuating input at the time of burst onset. The mapping between input phase and number of spikes per burst is a robust response feature for a broad range of stimulus statistics. Our results suggest that cortical bursting neurons could play a crucial role in translating LFP phase information into an easily decodable spike count code.

Show MeSH
Related in: MedlinePlus