scholarly journals The synaptic inputs and thalamic projections of two classes of layer 6 corticothalamic neurons in primary somatosensory cortex of the mouse

2021 ◽  
Author(s):  
Courtney Michelle Whilden ◽  
Maxime Chevée ◽  
Seong Yeol An ◽  
Solange Pezon Brown
Keyword(s):  
Somatosensory Cortex ◽  
Primary Somatosensory Cortex ◽  
Synaptic Inputs ◽  
Thalamic Projections

Intrinsic discharge patterns and somatosensory inputs for neurons in raccoon primary somatosensory cortex

1994 ◽  
Vol 72 (6) ◽  
pp. 2827-2839 ◽  
Author(s):  
P. J. Istvan ◽  
P. Zarzecki
Keyword(s):  
Somatosensory Cortex ◽  
Cortical Neurons ◽  
Primary Somatosensory Cortex ◽  
Membrane Properties ◽  
Postsynaptic Potentials ◽  
Synaptic Inputs ◽  
Ionic Conductances ◽  
Discharge Patterns ◽  
Intrinsic Membrane Properties ◽  
Stimulation Of

1. Discharge patterns of neurons are regulated by synaptic inputs and by intrinsic membrane properties such as their complement of ionic conductances. Discharge patterns evoked by synaptic inputs are often used to identify the source and modality of sensory input. However, the interpretation of these discharge patterns may be complicated if different neurons respond to the same synaptic input with a variety of discharge patterns due to differences in intrinsic membrane properties. The purposes of this study were 1) to investigate intrinsic discharge patterns of neurons in primary somatosensory cortex of raccoon in vivo and 2) to use somatosensory postsynaptic potentials evoked by stimulation of forepaw digits to determine thalamocortical connectivity for the same neurons. 2. Conventional intracellular recordings with sharp electrodes were made from 121 neurons in the cortical representation of glabrous skin of digit four (d4). Intracellular injection of identical current pulses (100-120 ms in duration) elicited various patterns of discharge in different neurons. Neurons were classified on the basis of these intrinsic patterns of discharge, rates of spike adaptation, and characteristics of spike waveforms. Three main groups were identified: regular spiking (RS) neurons, intrinsic bursting (IB) neurons, and fast spiking (FS) neurons. Subclasses were identified for the RS and IB groups. 3. Neurons were tested for somatosensory inputs by stimulating electrically d3, d4, and d5. Excitatory postsynaptic potentials (EPSPs) were elicited in 100% of the neurons by electrical stimulation of d4, the "on-focus" digit. EPSPs were usually followed by inhibitory postsynaptic potentials (IPSPs). Many neurons (41%) responded with EPSP-IPSP sequences after stimulation of d3 or d5, the "off-focus" digits. 4. Latencies of somatosensory EPSPs and IPSPs were used to determine the synaptic order in the cortical circuitry of RS, IB, and FS neurons. EPSPs with monosynaptic thalamocortical latencies were recorded in RS, IB, and FS neurons. 5. We conclude that precise patterns of neural discharge in primary somatosensory cortex cannot be reliable estimates of sensory inputs reaching these neurons because patterns of discharge are so strongly influenced by intrinsic membrane properties. Ionic conductances governing patterns of neuronal discharge seem almost identical in intact cortex of raccoon, rat, and cat, and in slices of rodent cortex, because similar patterns of discharge are found. The consistency of patterns of discharge across species and types of preparation suggests that these intrinsic membrane properties are a general property of cerebral cortical neurons and should be considered when evaluation sensory coding by these neurons.

scholarly journals Refractoriness accounts for variable spike burst responses in somatosensory cortex

2017 ◽  
Author(s):  
Bartosz Teleńczuk ◽  
Richard Kempter ◽  
Gabriel Curio ◽  
Alain Destexhe
Keyword(s):  
Somatosensory Cortex ◽  
Cortical Neurons ◽  
Single Neuron ◽  
Primary Somatosensory Cortex ◽  
General Mechanism ◽  
Population Activity ◽  
Synaptic Inputs ◽  
Neuron Spike ◽  
Biophysical Mechanism ◽  
Spike Patterns

AbstractNeurons in the primary somatosensory cortex (S1) respond to peripheral stimulation with synchronised bursts of spikes, which lock to the macroscopic 600 Hz EEG waves. The mechanism of burst generation and synchronisation in S1 is not yet understood. Using models of single-neuron responses fitted to unit recordings from macaque monkeys, we show that these synchronised bursts are the consequence of correlated synaptic inputs combined with a refractory mechanism. In the presence of noise these models reproduce also the observed trial-to-trial response variability, where individual bursts represent one of many stereotypical temporal spike patterns. When additional slower and global excitability fluctuations are introduced the single-neuron spike patterns are correlated with the population activity, as demonstrated in experimental data. The underlying biophysical mechanism of S1 responses involves thalamic inputs arriving through depressing synapses to cortical neurons in a high-conductance state. Our findings show that a simple feedforward processing of peripheral inputs could give rise to neuronal responses with non-trivial temporal and population statistics. We conclude that neural systems could use refractoriness to encode variable cortical states into stereotypical short-term spike patterns amenable to processing at neuronal time scales (tens of milliseconds).Significance statementNeurons in the hand area of the primary somatosensory cortex respond to repeated presentation of the same stimulus with variable sequences of spikes, which can be grouped into distinct temporal spike patterns. In a simplified model, we show that such spike patterns are product of synaptic inputs and intrinsic neural properties. This model can reproduce both single-neuron and population responses only when a private variability in each neuron is combined with a multiplicative gain shared over whole population, which fluctuates over trials and might represent the dynamical state of the early stages of sensory processing. This phenomenon exemplifies a general mechanism of transforming the ensemble cortical states into precise temporal spike patterns at the level of single neurons.

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