The Competing Benefits of Noise and Heterogeneity in Neural Coding

Abstract

Noise and heterogeneity are both known to benefit neural coding. Stochastic resonance describes how noise, in the form of random fluctuations in a neuron's membrane voltage, can improve neural representations of an input signal. Neuronal heterogeneity refers to variation in any one of a number of neuron parameters, and is also known to increase the information content of a population. We explore the interaction between noise and heterogeneity and find that their benefits to neural coding are not independent. Specifically, a neuronal population better represents an input signal when either noise or heterogeneity is added, but adding both does not always improve representation further. To explain this phenomenon, we propose that noise and heterogeneity operate using two shared mechanisms: 1) temporally desynchronizing the firing of neurons in the population, and 2) linearizing the response of a population to a stimulus. We first characterize the effects of noise and heterogeneity on the information content of populations of either leaky integrate-and-fire or FitzHugh-Nagumo neurons. We then examine how the mechanisms of desynchronization and linearization produce these effects, and find that they work to distribute information equally across all neurons in the population, both in terms of signal timing (desynchronization) and in terms of signal amplitude (linearization). Without noise or heterogeneity, all neurons encode the same aspects of the input signal; adding noise or heterogeneity allows neurons to encode complementary aspects of the input signal, thereby increasing information content. The simulations detailed in this paper highlight the importance of heterogeneity and noise in population coding, demonstrate their complex interactions in terms of the information content of neurons, and explain these effects in terms of underlying mechanisms.

Differential effects of excitatory and inhibitory heterogeneity on the gain and asynchronous state of sparse cortical networks

“…In particular, the role of heterogeneity on synchronization has been extensively studied (Golomb and Rinzel, 1993; White et al, 1998; Neltner et al, 2000; Golomb et al, 2001; Denker et al, 2004; Talathi et al, 2008, 2009; Luccioli and Politi, 2010; Olmi et al, 2010; Brette, 2012; Mejias and Longtin, 2012). More recently, the effect of neural heterogeneities on neuronal correlations (Chelaru and Dragoi, 2008; Yim et al, 2013), detection of weak signals (Tessone et al, 2006; Perez et al, 2010) and different types of neural coding (Chelaru and Dragoi, 2008; Savard et al, 2011; Mejias and Longtin, 2012; Hunsberger et al, 2014) have drawn special attention as well. Novel approaches and mean-field approximations to tackle the problem of heterogeneity have also been recently proposed (Nicola and Campbell, 2013; Yim et al, 2013).…”

Human-Inspired Neurorobotic System for Classifying Surface Textures by Touch

“…By allowing them to vary randomly, the information content of the population is increased [21]. In particular, the network can further process the 6-dimensional representation using (2) while being robust to noise.…”

“…These networks can be efficiently simulated on neuromorphic hardware such as SpiNNaker [18], resulting in significant energy savings [19]. Simulations are also more efficient than all-to-all connected networks due to the use of factored weight matrices [20], and are generally robust to noise and physical variability due to heterogeneity [21].…”

Optimising threshold levels for information transmission in binary threshold networks: Independent multiplicative noise on each threshold

“…Since this seminal work, SSR has been extensively investigated for the case where all thresholds have the same value [17][18][19][20][21][22][23][24][25][26][27]. Closely related work has focused on the interesting case when threshold devices are replaced by identical neuron models [28][29][30][31][32][33]. However, other work has relaxed the constraint of identical thresholds [34], and found the optimal thresholds for maximising the mutual information as a function of additive noise intensity.…”