Synchrony Makes Neurons Fire in Sequence, and Stimulus Properties Determine Who Is Ahead

Abstract

The synchronized activity of cortical neurons often features spike delays of several milliseconds. Usually, these delays are considered too small to play a role in cortical computations. Here, we use simultaneous recordings of spiking activity from up to 12 neurons to show that, in the cat visual cortex, the pairwise delays between neurons form a preferred order of spiking, called firing sequence. This sequence spans up to ϳ15 ms and is referenced not to external events but to the internal cortical activity (e.g., beta/gamma oscillations). Most importantly, the preferred sequence of firing changed consistently as a function of stimulus properties. During beta/gamma oscillations, the reliability of firing sequences increased and approached that of firing rates. This suggests that, in the visual system, short-lived spatiotemporal patterns of spiking defined by consistent delays in synchronized activity occur with sufficient reliability to complement firing rates as a neuronal code.

Synchronized excitability in a network enables generation of internal neuronal sequences

“…It has been shown that internal sequences are locked to individual cycles of rhythmic excitatory waves (oscillations) in other brain structures (Ji and Wilson, 2007; Luczak et al, 2009; Peyrache et al, 2009; Havenith et al, 2011; Harvey et al, 2012; Xu et al, 2012; Carrillo-Reid et al, 2015; Markowitz et al, 2015; Mello et al, 2015). Thus, the same mechanism for sequence generation might be utilized not only in the hippocampus but also in other brain regions.…”

“…Sequences generated in the absence of sensory cues have been observed in various cortical areas, basal ganglia, and the hippocampus (Wilson and McNaughton, 1994; Nádasdy et al, 1999; Louie and Wilson, 2001; Dragoi and Buzsáki, 2006; O’Neil et al, 2006; Foster and Wilson, 2006; Ji and Wilson, 2007; Lee and Wilson, 2002; Pastalkova et al, 2008; Luczak et al, 2009; Peyrache et al, 2009; Havenith et al, 2011; Harvey et al, 2012; Xu et al, 2012; Carrillo-Reid et al, 2015; Markowitz et al, 2015; Mello et al, 2015). These internally generated sequences seem to support mental functions such as cognitive planning, motor planning, visual memory, and episodic memory.…”

Bits from Brains for Biologically Inspired Computing

“…Recurrent neural networks (RNNs) consist of a reservoir of nodes or artificial neurons connected in some recurrent network structure (Maass et al, 2002;Jaeger and Haas, 2004). Typically, this structure is constructed at random, with only the output neurons connections trained to perform a given task.…”

Divisive suppression explains high-precision firing and contrast adaptation in retinal ganglion cells

“…For example, the LN model does not capture the effects of contrast adaptation, which includes a reduced gain (i.e., filter amplitude) at high contrast (Kim and Rieke, 2001; Meister and Berry, 1999; Shapley and Victor, 1978). The LN model also does not predict firing at high temporal resolution (Berry and Meister, 1998; Butts et al, 2016, 2007; Keat et al, 2001; Passaglia and Troy, 2004; Uzzell and Chichilnisky, 2004), and yet precise firing likely represents an essential element of downstream visual processing (Bruno and Sakmann, 2006; Havenith et al, 2011; Kelly et al, 2014; Wang et al, 2010a). …”