arXiv subjects

Peter E. Latham

Publications and source records attributed to Peter E. Latham.

22 records · Page 2Linked to original sources

arXiv2015

Synaptic sampling: A connection between PSP variability and uncertainty explains neurophysiological observations

When an action potential is transmitted to a postsynaptic neuron, a small change in the postsynaptic neuron's membrane potential occurs. These small changes, known as a postsynaptic potentials (PSPs), are highly variable, and current models assume that this variability is corrupting noise. In contrast, we show that this variability could have an important computational role: representing a synapse's uncertainty about the optimal synaptic weight (i.e. the best possible setting for the synaptic weight). We show that this link between uncertainty and variability, that we call synaptic sampling, leads to more accurate estimates of the uncertainty in task relevant quantities, leading to more effective decision making. Synaptic sampling makes four predictions, all of which have some experimental support. First the more variable a synapse is, the more it should change during LTP protocols. Second, variability should increase as the presynpatic firing rate falls. Third, PSP variance should be proportional to PSP mean. Fourth, variability should increase with distance from the cell soma. We provide support for the first two predictions by reanalysing existing datasets, and we find preexisting data in support of the last two predictions.

q-bio.NC↗

arXiv2011

Role of correlations in population coding

Correlations among spikes, both on the same neuron and across neurons, are ubiquitous in the brain. For example cross-correlograms can have large peaks, at least in the periphery, and smaller -- but still non-negligible -- ones in cortex, and auto-correlograms almost always exhibit non-trivial temporal structure at a range of timescales. Although this has been known for over forty years, it's still not clear what role these correlations play in the brain -- and, indeed, whether they play any role at all. The goal of this chapter is to shed light on this issue by reviewing some of the work on this subject.

q-bio.NC↗

arXiv2007

A balanced memory network

A fundamental problem in neuroscience is understanding how working memory -- the ability to store information at intermediate timescales, like 10s of seconds -- is implemented in realistic neuronal networks. The most likely candidate mechanism is the attractor network, and a great deal of effort has gone toward investigating it theoretically. Yet, despite almost a quarter century of intense work, attractor networks are not fully understood. In particular, there are still two unanswered questions. First, how is it that attractor networks exhibit irregular firing, as is observed experimentally during working memory tasks? And second, how many memories can be stored under biologically realistic conditions? Here we answer both questions by studying an attractor neural network in which inhibition and excitation balance each other. Using mean field analysis, we derive a three-variable description of attractor networks. From this description it follows that irregular firing can exist only if the number of neurons involved in a memory is large. The same mean field analysis also shows that the number of memories that can be stored in a network scales with the number of excitatory connections, a result that has been suggested for simple models but never shown for realistic ones. Both of these predictions are verified using simulations with large networks of spiking neurons.

q-bio.NC↗

arXiv1998

Coding Strategies in Monkey V1 and Inferior Temporal Cortices

We would like to know whether the statistics of neuronal responses vary across cortical areas. We examined stimulus-elicited spike count response distributions in V1 and IT cortices of awake monkeys. In both areas the distribution of spike counts for each stimulus was well-described by a Gaussian, with the log of the variance in the spike count linearly related to the log of the mean spike count. Two significant differences in response characteristics were found: both the range of spike counts and the slope of the log(variance) vs. log(mean) regression were larger in V1 than in IT. However, neurons in the two areas transmitted approximately the same amount of information about the stimuli, and had about the same channel capacity (the maximum possible transmitted information given noise in the responses). These results suggest that neurons in V1 use more variable signals over a larger dynamic range than neurons in IT, which use less variable signals over a smaller dynamic range. The two coding strategies are approximately as effective in transmitting information.

q-bio.NC↗