Cortical neural populations can guide behavior by integrating inputs linearly, independent of synchrony

Abstract

Neurons are sensitive to the relative timing of inputs, both because several inputs must coincide to reach spike threshold and because active dendritic mechanisms can amplify synchronous inputs. To determine if input synchrony can influence behavior, we trained mice to report activation of excitatory neurons in visual cortex using channelrhodopsin-2. We used light pulses that varied in duration from a few milliseconds to 100 ms and measured neuronal responses and animals' detection ability. We found detection performance was well predicted by the total amount of light delivered. Short pulses provided no behavioral advantage, even when they concentrated evoked spikes into an interval a few milliseconds long. Arranging pulses into trains of varying frequency from beta to gamma also produced no behavioral advantage. Light intensities required to drive behavior were low (at low intensities, channelrhodopsin-2 conductance varies linearly with intensity), and the accompanying changes in firing rate were small (over 100 ms, average change: 1.1 spikes per s). Firing rate changes varied linearly with pulse intensity and duration, and behavior was predicted by total spike count independent of temporal arrangement. Thus, animals' detection performance reflected the linear integration of total input over 100 ms. This behavioral linearity despite neurons' nonlinearities can be explained by a population code using noisy neurons. Ongoing background activity creates probabilistic spiking, allowing weak inputs to change spike probability linearly, with little amplification of coincident input. Summing across a population then yields a total spike count that weights inputs equally, regardless of their arrival time.neuronal circuits | population coding | optogenetics | mouse M any neurons in the brain receive thousands of inputs spread over their dendritic trees, and several of those inputs need to be active simultaneously to generate a spike reliably (1, 2). In this way, coincident synaptic inputs can be amplified relative to asynchronous inputs. In addition to this nonlinearity caused by spike threshold, other active processes such as dendritic calcium spikes (3-5) can preferentially amplify synchronous inputs. A variety of ways that timing could impact network function have been explored, including oscillatory synchronization (6, 7), strong cascading effects of individual neurons or synapses (8-11), and information encoding via temporal patterns (12). In the songbird, for example, song neurons receive precisely timed coincident input that recruits active calcium conductances, generating strong, reliable spike bursts that control the song (13, 14). However, synchrony might not always be critical for neuronal processing. Several types of models show that neuronal networks can be insensitive to precise spike timing even though individual neurons are highly sensitive. Most of these models rely on strong (15) or numerous (16-18) inputs and amplification of small perturbations, leading to chaotic network dynamics and noisy single neu...

Behavioral assessment of sensitivity to intracortical microstimulation of primate somatosensory cortex

“…For example, simply introducing irregularity in the pulse trains (while keeping their overall frequency constant) yields more detectable percepts, perhaps because irregular spiking is more biomimetic than regular spiking (39; but see ref. 40 for counterevidence). A compelling example of how biomimetic patterning might modulate the efficiency of ICMS is provided by Kimmel and Moore (41), who delivered stimulation to the frontal eye fields (FEF) and showed that pulse trains whose frequency ramps up-thereby more closely mimicking the natural activation of FEF neurons-are more effective at evoking saccades than flat, decelerating, or randomly varying ones.…”

Beyond the Connectome: The Dynome

“…This complements other approaches, including dynamical causal modeling, which tends to focus on more abstract, neural mass models (e.g., Kiebel et al , 2009; Moran et al , 2013), which may not reflect the biophysical properties critical to some neural computations. We focus, as invited, on brain rhythms, which reflect and influence spiking activity; however, dynamic processes can also happen in the absence of rhythmic brain activity (Ecker et al , 2010; Renart et al , 2010; Ainsworth et al , 2012; Histed & Maunsell, 2014) and the dynome includes other temporal structures as well (Larson-Prior et al , 2013). …”

For things needing your attention: the role of neocortical gamma in sensory perception

“…Another recent paper using direct detection of brain stimulation, Histed and colleagues (2014), employed direct optogenetic drive of pyramidal neurons across neocortical layers in V1 [78 ]. Histed et al manipulated the synchronous output of visual cortex by comparing a high intensity, short duration versus lower intensity longer duration optogenetic stimuli, which the authors matched for the number of total action potentials generated.…”

“…However, Lee et al (2012)also observed benefits to perception from FS activation in a regime they showed in other experiments should largely suppress pyramidal firing, suggesting that area-specific differences may not be a viable explanation. The discrepancy in the inferences drawn from these several studies and those ofHisted et al (2014) indicates that parallel experiments should be conducted that directly address this issue, ideally in the same laboratory or in close collaboration across the laboratories that came to distinct conclusions.…”