Abstract
Even in the absence of sensory stimulation the brain is spontaneously active. This background “noise” seems to be the dominant cause of the notoriously high trial-to-trial variability of neural recordings. Recent experimental observations have extended our knowledge of trial-to-trial variability and spontaneous activity in several directions: 1. Trial-to-trial variability systematically decreases following the onset of a sensory stimulus or the start of a motor act. 2. Spontaneous activity states in sensory cortex outline the region of evoked sensory responses. 3. Across development, spontaneous activity aligns itself with typical evoked activity patterns. 4. The spontaneous brain activity prior to the presentation of an ambiguous stimulus predicts how the stimulus will be interpreted. At present it is unclear how these observations relate to each other and how they arise in cortical circuits. Here we demonstrate that all of these phenomena can be accounted for by a deterministic self-organizing recurrent neural network model (SORN), which learns a predictive model of its sensory environment. The SORN comprises recurrently coupled populations of excitatory and inhibitory threshold units and learns via a combination of spike-timing dependent plasticity (STDP) and homeostatic plasticity mechanisms. Similar to balanced network architectures, units in the network show irregular activity and variable responses to inputs. Additionally, however, the SORN exhibits sequence learning abilities matching recent findings from visual cortex and the network’s spontaneous activity reproduces the experimental findings mentioned above. Intriguingly, the network’s behaviour is reminiscent of sampling-based probabilistic inference, suggesting that correlates of sampling-based inference can develop from the interaction of STDP and homeostasis in deterministic networks. We conclude that key observations on spontaneous brain activity and the variability of neural responses can be accounted for by a simple deterministic recurrent neural network which learns a predictive model of its sensory environment via a combination of generic neural plasticity mechanisms.
Highlights
Our brains are always active, even when we rest or sleep
We conclude that key observations on spontaneous brain activity and the variability of neural responses can be accounted for by a simple deterministic recurrent neural network which learns a predictive model of its sensory environment via a combination of generic neural plasticity mechanisms
The spontaneous activity occurring in the absence of any sensory stimulus, which is usually considered a kind of background noise, often has a magnitude comparable to the activity evoked by stimulus presentation and interacts with sensory inputs in interesting ways
Summary
Our brains are always active, even when we rest or sleep. This may seem somewhat surprising given the high metabolic costs associated with neural activity [1]. This was shown by Arieli et al in a seminal study [8] almost 20 years ago using voltage-sensitive dye imaging (VSDI) They demonstrated that trial-to-trial variability can be almost perfectly predicted from the spontaneous activity prior to stimulus onset through a simple linear combination of the spontaneous activity prior to stimulus onset and the stimulus-triggered-average response. They concluded that “the effect of a stimulus might be likened to the additional ripples caused by tossing a stone into a wavy sea.”. A number of additional studies suggest that attention has a similar effect in both primates and rodents (see [11] for review)
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