Sensitivity of Noisy Neurons to Coincident Inputs

Abstract

How do neurons compute? Two main theories compete: neurons could temporally integrate noisy inputs (rate-based theories) or they could detect coincident input spikes (spike timing-based theories). Correlations at fine timescales have been observed in many areas of the nervous system, but they might have a minor impact. To address this issue, we used a probabilistic approach to quantify the impact of coincidences on neuronal response in the presence of fluctuating synaptic activity. We found that when excitation and inhibition are balanced, as in the sensory cortex in vivo, synchrony in a very small proportion of inputs results in dramatic increases in output firing rate. Our theory was experimentally validated with in vitro recordings of cortical neurons of mice. We conclude that not only are noisy neurons well equipped to detect coincidences, but they are so sensitive to fine correlations that a rate-based description of neural computation is unlikely to be accurate in general.

Gamma oscillations in the midbrain spatial attention network: linking circuits to function

“…The oscillations entrain rhythmic neural firing in the thalamus and, subsequently, in the forebrain that synchronize with the midbrain oscillations. The thalamic oscillations enable more effective communication between the thalamus and forebrain first, by creating temporally aligned windows of excitability between the thalamus and forebrain [64]; and second, by enhancing transmission efficacy by coincidence-dependent temporal integration, that is, coincident spikes are more effective at driving post-synaptic targets than incoherent spikes [65,66] (Figure 3a, top versus bottom).…”

Spike Resonance Properties in Hippocampal O-LM Cells Are Dependent on Refractory Dynamics

“…Both these features have been observed in a wide range of different cell types. It seems likely that fluctuation-driven spiking and the concomitant increase in membrane conductance associated with synaptic inputs are general features of mammalian central neurons during active states (Rossant et al, 2011). …”

Cumulants of Hawkes point processes

“…It has been shown that nerve cells can be extremely sensitive to synchronous input from large groups of neurons [30], More precisely, a neuron's firing rate profile depends, to a large degree, on higher-order correlations among the presynaptic spikes [31], Of course, which synchronous patterns are favored by the network is also determined hy its connection structure. While the contribution of specific structural motifs to the emergence of pairwise correlations (i.e., two-spike patterns) has already been dissected [16], no such result exists in the case of more complex patterns, stemming from correlations of higher order.…”