Scientific Understanding of Consciousness
Spike Synchrony in Small Populations of Neurons
Science 2 April 2010: Vol. 328. no. 5974, pp. 106 - 109
Synchrony of Thalamocortical Inputs Maximizes Cortical Reliability
Hsi-Ping Wang,1,2 Donald Spencer,1 Jean-Marc Fellous,3 Terrence J. Sejnowski1,2
1 Howard Hughes Medical Institute, Computational Neurobiology Laboratory, Salk Institute, La Jolla, CA 92037, USA.
Thalamic inputs strongly drive neurons in the primary visual cortex, even though these neurons constitute only ~5% of the synapses on layer 4 spiny stellate simple cells. We modeled the feedforward excitatory and inhibitory inputs to these cells based on in vivo recordings in cats, and we found that the reliability of spike transmission increased steeply between 20 and 40 synchronous thalamic inputs in a time window of 5 milliseconds, when the reliability per spike was most energetically efficient. The optimal range of synchronous inputs was influenced by the balance of background excitation and inhibition in the cortex, which could gate the flow of information into the cortex. Ensuring reliable transmission by spike synchrony in small populations of neurons may be a general principle of cortical function.
Neurons can perform coincidence detection of synaptic inputs with a temporal integration window that depends on the time courses of the synaptic conductances and the intrinsic properties of the postsynaptic neuron. Synchronous cortical inputs occur when there is a salient event in the sensory environment, such as the entrance of a moving object into a receptive field or the deflection of a whisker in rodent. The precise timing of action potentials has been shown to potentially provide information in addition to the spike rate. For a population of presynaptic neurons to fire nearly simultaneously, however, requires resources to time spike initiation precisely, parallel anatomical pathways to carry the spikes, and energy costs for redundant spikes, which may outweigh the benefits of increased information rate. We explored these issues in the projections from the lateral geniculate nucleus (LGN) to the primary visual cortex.
The question of efficient information transfer is particularly important for thalamocortical connections, because thalamic synaptic inputs, which are comparable in strength to cortical inputs, constitute only ~5% of the total synaptic input to cortical simple cells, but are nonetheless capable of reliably driving cortical neurons. To examine the relationship between synchrony and reliability, we performed computer simulations of a detailed biophysical model of a spiny stellate cell in layer 4 of area V1 of the cat primary visual cortex. This cell received 300 synaptic inputs from the LGN, competing with 5500 other excitatory and inhibitory intracortical synapses, including feedforward inhibition. All synapses were stochastic, and the excitatory synapses included short-term history-dependent modulation of release probability. We used this model to quantify the number of synchronous synaptic inputs that maximizes the efficient transfer of information to the cortical cell, and we compared these predictions to experimental data obtained in vivo from anesthetized cats.
Spike output reliability can be predicted by synchrony magnitude (SM), which is the number of thalamocortical synapses that are simultaneously driven by the same presynaptic thalamic spike train. Output reliability was a highly nonlinear function of SM, rising steeply from 20 to a maximum at 40 synapses, more rapidly than the output firing rate. The reliability-per-SM (RPSM) function, defined by dividing the reliability by the SM, reached an optimal synchrony magnitude (OSM) at approximately 30 synapses.
The quantitative analysis of output reliability can be used to predict the number of synchronous inputs that drive cortical cell responses during in vivo behavioral experiments. We plotted data from in vivo recordings of V1 cells supplied by other researchers against the transfer function of input synchrony as a function of output reliability, as determined by our model, to infer synchrony magnitude of the cells in vivo. Each of the four neurons (from different animals) predicted input synchrony in the range of 20 to 60 synchronous synapses. The reliability and firing rate at the OSM from our model, using recorded thalamic input spike trains, fall within the observed ranges of the experimental values.
These results were robust to increasing the jitter and varying the strengths of thalamic inputs and feedforward inhibitory synapses. However, the OSM depended on the balance between background excitatory (E) and inhibitory (I) inputs to the 5500 intracortical synapses. When the integrated inputs were balanced (total average excitatory input equal to the total average inhibitory input), the neuron became highly sensitive to correlated fluctuations of the membrane potential, which affected the reliability as well as the gain of the input spike rate to output spike rate curve. We varied the cortical background rate and the ratio of inhibitory to excitatory input rates, β = I/E, to assess their influence on the reliability of the synchronous inputs to LGN synapses. Background excitatory inputs above 1 spike/s depressed the reliability response by directly competing with the LGN excitatory inputs and introducing high levels of spurious output firing. Increasing the background inhibition also increased the OSM and was therefore a potential mechanism for setting the threshold for synchrony detection. The sensitivity of the cortex to synchronous inputs can be varied over a wide range by regulating β.
Spiking due to input synchrony may be a way to ensure that important events are registered by the spiny stellate neurons in the cortex, regardless of asynchronously arriving spikes from ongoing cortical computation.
The output spike pattern of a layer 4 neuron is determined by the temporal pattern, as well as the rate, of the synchronous thalamic inputs according to the history-dependent dynamics of its synapses acting coherently within 6 to 8 ms. Spike synchrony, observed throughout the cortex, may also have a more general function in ensuring information transmission between cortical areas.
(end of paraphrase)
Nature Reviews, Neuroscience, Volume 9, February 2008, 97
Regulation of spike timing in visual cortical circuits
Paul Tiesinga*, Jean-Marc Fellous‡ and Terrence J. Sejnowski§||
*Physics and Astronomy Department, University of North Carolina at Chapel Hill, North Carolina 27599-3255, USA.
‡Psychology and Applied Mathematics Departments, University of Arizona at Tucson, Arizona 85721-0068, USA.
§Computational Neurobiology Laboratory and Howard Hughes Medical Institute at the Salk Institute, La Jolla, California 92037, USA.
||Division of Biological Sciences, University of California at San Diego, La Jolla, California, 92039- 0348 USA.
A train of action potentials (a spike train) can carry information in both the average firing rate and the pattern of spikes in the train. Cortical neurons in vivo are modulated by local oscillatory neuronal activity and by top-down inputs. In a cortical circuit, precise spike patterns thus reflect the interaction between internally generated activity and sensory information encoded by input spike trains.
Spike trains that are produced by a cortical pyramidal cell depend on the coherent states that are generated by recurrent columnar connectivity, the activation of top-down projections, and the current sensory stimulation through the feedforward pathway. Each of these three types of input by itself can only modulate the pyramidal cell’s output, which raises the question of the nature of the relationship between the temporal dynamics of the stimulus and the spike patterns that are generated by the pyramidal cell. Our research suggests that ensembles of neurons produce slowly modulated activity that is accompanied by coherent volleys, with fast rhythms (beta and gamma rhythms) perturbing the timing or even gating the transmission of volleys, and slower rhythms (alpha, theta and delta rhythms) controlling the amplitude of fast rhythms. Subcortical and top-down intercortical projections can influence information processing by modulating the phase of these rhythms.
(end of paraphrase)