Readings in Computational Neuroscience – Basics

Computational Neuroscience – Really Crucial Early Papers

• Wilson, H.R., and J.D. Cowan. 1972. Excitatory and inhibitory interactions in localized populations of model neurons. Biophysical Journal 12, 1-24. (pdf)

Computational Neuroscience – Reading List

• Moran, R., Pinotsis D.A., & Friston, K. (2013). Neural masses and fields in dynamic causal modeling. Frontiers in Computational Neuroscience 7, 57 ff. pdf
  An important read, worth several re-reads:
  

  Dynamic causal modeling (DCM) provides a framework for the analysis of effective connectivity among neuronal subpopulations that subtend invasive (electrocorticograms and local field potentials) and non-invasive (electroencephalography and magnetoencephalography) electrophysiological responses. This paper reviews the suite of neuronal population models including neural masses, fields and conductance-based models that are used in DCM. These models are expressed in terms of sets of differential equations that allow one to model the synaptic underpinnings of connectivity.

• Engel, A.K., Gerloff, C., Hilgetag, C.C., & Nolte, G. (2013). Intrinsic coupling modes: multiscale interactions in ongoing brain activity. Neuron 9, 38 ff. (http://dx.doi.org/10.1016/j.neuron.2013.09.038) pdf
  
  

  Intrinsic coupling constitutes a key feature of ongoing brain activity, which exhibits rich spatiotemporal
  patterning and contains information that influences cognitive processing. We discuss evidence for two
  distinct types of intrinsic coupling modes which seem to reflect the operation of different coupling mechanisms.
  One type arises from phase coupling of band-limited oscillatory signals, whereas the other results
  from coupled aperiodic fluctuations of signal envelopes. The two coupling modes differ in their dynamics,
  their origins, and their putative functions and with respect to their alteration in neuropsychiatric disorders.
  We propose that the concept of intrinsic coupling modes can provide a unifying framework for capturing
  the dynamics of intrinsically generated neuronal interactions at multiple spatial and temporal scales.

• Engel, A.K., Fries, P., Singer, W. (2001). Dynamic predictions: oscillations and synchrony in top-down processing. Nature Reviews Neuroscience 2, 704-716. pdf
  
  

  Classical theories of sensory processing view the brain as a passive, stimulus-driven device. By contrast, more recent approaches emphasize the constructive nature of perception, viewing it as an active and highly selective process. Indeed, there is ample evidence that the processing of stimuli is controlled by top–down influences that strongly shape the intrinsic dynamics of
  thalamocortical networks and constantly create predictions about forthcoming sensory events. We discuss recent experiments indicating that such predictions might be embodied in the temporal structure of both stimulus-evoked and ongoing activity, and that synchronous
  oscillations are particularly important in this process.

• Single neuron dynamics, Brunel N, Hakim V, Richardson MJE: Single neuron dynamics and computation. Current Opinion in Neurobiology 2014, 25:149–155.
• Cortes, J.M., Desroches, M., Rodrigues, S., Veltz, R., Munoz, M.A., & Sejnowski, T.J. (2013). Short-term synaptic plasticity in the deterministic Tsodyks-Markram model leads to unpredictable network dynamics. Proc. Nat’l. Acad. Sci. USA 110, 16611. PMCID:PMC3799370. pdf
• Sejnowski, T.J., Poizner, H., Lynch, G., Gepshtein, S., & Greenspan, R. (2014). Prospective optimization. Proc. IEEE 102, 799-811. PMCID:PMCID: PMC4201124. pdf

More Engel papers at: Engel pdfs.