Heterogeneity of intrinsic biophysical properties among cochlear nucleus neurons improves the population coding of temporal information.

Reliable representation of the spectrotemporal features of an acoustic stimulus is critical for sound recognition. However, if all neurons respond with identical firing to the same stimulus, redundancy in the activity patterns would reduce the information capacity of the population. We thus investigated spike reliability and temporal fluctuation coding in an ensemble of neurons recorded in vitro from the avian auditory brain stem. Sequential patch-clamp recordings were made from neurons of the cochlear nucleus angularis while injecting identical filtered Gaussian white noise currents, simulating synaptic drive. The spiking activity in neurons receiving these identically fluctuating stimuli was highly correlated, measured pairwise across neurons and as a pseudo-population. Two distinct uncorrelated noise stimuli could be discriminated using the temporal patterning, but not firing rate, of the spike trains in the neural ensemble, with best discrimination using information at time scales of 5-20 ms. Despite high cross-correlation values, the spike patterns observed in individual neurons were idiosyncratic, with notable heterogeneity across neurons. To investigate how temporal information is being encoded, we used optimal linear reconstruction to produce an estimate of the original current stimulus from the spike trains. Ensembles of trains sampled across the neural population could be used to predict >50% of the stimulus variation using optimal linear decoding, compared with ∼20% using the same number of spike trains recorded from single neurons. We conclude that heterogeneity in the intrinsic biophysical properties of cochlear nucleus neurons reduces firing pattern redundancy while enhancing representation of temporal information.

The information content of spontaneous retinal waves.

Spontaneous neural activity that is present in the mammalian retina before the onset of vision is required for the refinement of retinotopy in the lateral geniculate nucleus and superior colliculus. This paper explores the information content of this retinal activity, with the goal of determining constraints on the nature of the developmental mechanisms that use it. Through information-theoretic analysis of multielectrode and calcium-imaging experiments, we show that the spontaneous retinal activity present early in development provides information about the relative positions of retinal ganglion cells and can, in principle, be used at retinogeniculate and retinocollicular synapses to refine retinotopy. Remarkably, we find that most retinotopic information provided by retinal waves exists on relatively coarse time scales, suggesting that developmental mechanisms must be sensitive to timing differences from 100 msec up to 2 sec to make optimal use of it. In fact, a simple Hebbian-type learning rule with a correlation window on the order of seconds is able to extract the bulk of the available information. These findings are consistent with bursts of action potentials (rather than single spikes) being the unit of information used during development and suggest new experimental approaches for studying developmental plasticity of the retinogeniculate and retinocollicular synapses. More generally, these results demonstrate how the properties of neuronal systems can be inferred from the statistics of their input.

Retinal waves are governed by collective network properties.

Propagating neural activity in the developing mammalian retina is required for the normal patterning of retinothalamic connections. This activity exhibits a complex spatiotemporal pattern of initiation, propagation, and termination. Here, we discuss the behavior of a model of the developing retina using a combination of simulation and analytic calculation. Our model produces spatially and temporally restricted waves without requiring inhibition, consistent with the early depolarizing action of neurotransmitters in the retina. We find that highly correlated, temporally regular, and spatially restricted activity occurs over a range of network parameters; this ensures that such spatiotemporal patterns can be produced robustly by immature neural networks in which synaptic transmission by individual neurons may be unreliable. Wider variation of these parameters, however, results in several different regimes of wave behavior. We also present evidence that wave properties are locally determined by a single variable, the fraction of recruitable (i.e., nonrefractory) cells within the dendritic field of a retinal neuron. From this perspective, a given local area's ability to support waves with a wide range of propagation velocities-as observed in experiment-reflects the variability in the local state of excitability of that area. This prediction is supported by whole-cell voltage-clamp recordings, which measure significant wave-to-wave variability in the amount of synaptic input a cell receives when it participates in a wave. This approach to describing the developing retina provides unique insight into how the organization of a neural circuit can lead to the generation of complex correlated activity patterns.

Dynamic processes shape spatiotemporal properties of retinal waves.

In the developing mammalian retina, spontaneous waves of action potentials are present in the ganglion cell layer weeks before vision. These waves are known to be generated by a synaptically connected network of amacrine cells and retinal ganglion cells, and exhibit complex spatiotemporal patterns, characterized by shifting domains of coactivation. Here, we present a novel dynamical model consisting of two coupled populations of cells that quantitatively reproduces the experimentally observed domain sizes, interwave intervals, and wavefront velocity profiles. Model and experiment together show that the highly correlated activity generated by retinal waves can be explained by a combination of random spontaneous activation of cells and the past history of local retinal activity.