17 papers · ranked by Valyu relevance
Stefano Panzeri, Jakob H. Macke, Joachim Gross, Christoph Kayser
Title: Highlights 1. • Neural population codes are organized at multiple spatial scales. 2. • Microscopic organization of neural codes reveals a key role of neural heterogeneity. 3. • Microscopic and population dynamics interact to make processing state-dependent. 4. • Additional computational analyses of neural…
Elad Ganmor, Ronen Segev, Elad Schneidman, David Kleinfeld
Information is carried in the brain by the joint spiking patterns of large groups of noisy, unreliable neurons. This noise limits the capacity of the neural code and determines how information can be transmitted and read-out. To accurately decode, the brain must overcome this noise and identify which patterns are…
Thomas E. Yerxa, Eric Kee, Michael R. DeWeese, Emily A. Cooper + 1 more
'Alan Alfred Stocker'] According to the efficient coding hypothesis, sensory systems are adapted to maximize their ability to encode information about the environment. Sensory neurons play a key role in encoding by selectively modulating their firing rate for a subset of all possible stimuli. This pattern of modulation…
Richard Naud, Wulfram Gerstner, Olaf Sporns
The response of a neuron to a time-dependent stimulus, as measured in a Peri-Stimulus-Time-Histogram (PSTH), exhibits an intricate temporal structure that reflects potential temporal coding principles. Here we analyze the encoding and decoding of PSTHs for spiking neurons with arbitrary refractoriness and adaptation.…
Jason S. Prentice, Olivier Marre, Mark L. Ioffe, Adrianna R. Loback + 3 more
'Gašper Tkačik' 'Michael J. Berry II' 'Ian H. Stevenson'] Across the nervous system, certain population spiking patterns are observed far more frequently than others. A hypothesis about this structure is that these collective activity patterns function as population codewords-collective modes-carrying information…
Kevin S. Chen, Fernando Morgado-Dias, George Em Karniadakis
The efficient coding hypothesis states that neural response should maximize its information about the external input. Theoretical studies focus on optimal response in single neuron and population code in networks with weak pairwise interactions. However, more biological settings with asymmetric connectivity and the…
Michael J. Berry II, Gašper Tkačik
We propose that correlations among neurons are generically strong enough to organize neural activity patterns into a discrete set of clusters, which can each be viewed as a population codeword. Our reasoning starts with the analysis of retinal ganglion cell data using maximum entropy models, showing that the population…
Jacob L. Yates, Benjamin Scholl
The synaptic inputs to single cortical neurons exhibit substantial diversity in their sensory-driven activity. What this diversity reflects is unclear, and appears counter-productive in generating selective somatic responses to specific stimuli. One possibility is that this diversity reflects the propagation of…
Omer Revah, Fred Wolf, Michael J. Gutnick, Andreas Neef + 1 more
Sixty years after the concept of population coding in neuronal networks was introduced, we still lack a comprehensive understanding of its performance limits and the role of neuronal physiology. Here, we use dynamic gain analysis in a general model of population coding and demonstrate that disparate parameters of…
Kelsey M Hallinen, Ross Dempsey, Monika Scholz, Xinwei Yu + 7 more
'Ashley Linder' 'Francesco Randi' 'Anuj K Sharma' 'Joshua W Shaevitz' 'Andrew M Leifer' 'Ronald L Calabrese' 'Ronald L Calabrese'] We investigated the neural representation of locomotion in the nematode C. elegans by recording population calcium activity during movement. We report that population activity more…
Joel Zylberberg, Eric Shea-Brown
Neural systems contain many cells, and an important problem is to understand if and how those neurons work together to form a functioning system. In sensory neuroscience -- which is our focus -- this function is to encode information about a stimulus so that it can be transmitted to other brain areas. An experimentally…
Mikhail V. Kiselev
At present, it is obvious that different sections of nervous system utilize different methods for information coding. Primary afferent signals in most cases are represented in form of spike trains using a combination of rate coding and population coding while there are clear evidences that temporal coding is used in…
Meng Li, Joe Z. Tsien
A major stumbling block to cracking the real-time neural code is neuronal variability - neurons discharge spikes with enormous variability not only across trials within the same experiments but also in resting states. Such variability is widely regarded as a noise which is often deliberately averaged out during data…
He Chen, Jun Kunimatsu, Tomomichi Oya, Yuri Imaizumi + 5 more
'Masayuki Matsumoto' 'Takafumi Minamimoto' 'Yuji Naya' 'Hiroshi Yamada'] Title: Abstract Neural population dynamics provide a key computational framework for understanding information processing in the sensory, cognitive, and motor functions of the brain. They systematically depict complex neural population activity…
MEHRAD SARMASHGHI, SHANTANU P. JADHAV, URI T. EDEN
Neurons can code for multiple variables simultaneously and neuroscientists are often interested in classifying neurons based on their receptive field properties. Statistical models provide powerful tools for determining the factors influencing neural spiking activity and classifying individual neurons. However, as…
Zach Mobille, Usama Bin Sikandar, Simon Sponberg, Hannah Choi
Convergent and divergent structures in the networks that make up biological brains are found across many species and brain regions at various spatial scales. Neurons in these networks fire action potentials, or “spikes”, whose precise timing is becoming increasingly appreciated as large sources of information about…
Byron H. Price, Jeffrey P. Gavornik
While it is universally accepted that the brain makes predictions, there is little agreement about how this is accomplished and under which conditions. Accurate prediction requires neural circuits to learn and store spatiotemporal patterns observed in the natural environment, but it is not obvious how such information…