Spiking Neurons and Cognition

Simon J. Thorpe

Brain and Cognition Research Centre (CerCo), Toulouse, France

Neurons transmit information by generating electrical pulses - spikes. In this course, I will attempt to show how this fact is critical for understanding cognition. We will start with an analysis of the performance of biological vision systems, arguing that the speed with which our visual system can analyse complex natural scenes imposes extremely severe temporal constraints on the underlying mechanisms. I will then give a rapid overview of processing in the ventral stream of the visual system. Then we will look at how information can be coded in spiking neurons and how Spike Time Dependent Plasticity (STDP) can allow neurons to become selective to stimuli that occur repeatedly. Finally, I will discuss the possibility that very long term sensory memories may be stored in highly selective "grandmother cells" that have no spontaneous firing, and could constitute a form of neocortical dark matter.

Lecture 1. Introduction - Performance of Biological Vision Systems

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  1. Fabre-Thorpe, M. (2011). The characteristics and limits of rapid visual categorization. Front Psychol, 2, 243.
  2. Thorpe, S., Fize, D., & Marlot, C. (1996). Speed of processing in the human visual system. Nature, 381(6582), 520-522.
  3. Fabre-Thorpe, M., Richard, G., & Thorpe, S. J. (1998). Rapid categorization of natural images by rhesus monkeys. Neuroreport, 9(2), 303-308.
  4. Rousselet, G. A., Fabre-Thorpe, M., & Thorpe, S. J. (2002). Parallel processing in high-level categorization of natural images. Nat Neurosci, 5(7), 629-630.
  5. Kirchner, H., & Thorpe, S. J. (2006). Ultra-rapid object detection with saccadic eye movements: Visual processing speed revisited. Vision Res, 46(11), 1762-1776.
  6. Crouzet, S. M., Kirchner, H., & Thorpe, S. J. (2010). Fast saccades towards faces: Face detection in just 100 ms. J Vis, 10(4), 1-17.
  7. Crouzet, S. M., Joubert, O. R., Thorpe, S. J., & Fabre-Thorpe, M. (2012). Animal Detection Precedes Access to Scene Category. PLoS ONE, 7(12), e51471.

Lecture 2. Processing in the visual system

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  1. Kiani, R., Esteky, H., & Tanaka, K. (2005). Differences in onset latency of macaque inferotemporal neural responses to primate and non-primate faces. J Neurophysiol, 94(2), 1587-1596.
  2. Kreiman, G., Koch, C., & Fried, I. (2000). Category-specific visual responses of single neurons in the human medial temporal lobe. Nat Neurosci, 3(9), 946-953.
  3. Kreiman, G., Fried, I., & Koch, C. (2002). Single-neuron correlates of subjective vision in the human medial temporal lobe. Proc Natl Acad Sci U S A, 99(12), 8378-8383.
  4. Logothetis, N. K., Pauls, J., & Poggio, T. (1995). Shape representation in the inferior temporal cortex of monkeys. Curr Biol, 5(5), 552-563.
  5. Quian Quiroga, R., Reddy, L., Kreiman, G., Koch, C., & Fried, I. (2005). Invariant visual representation by single neurons in the human brain. Nature, 435(7045), 1102-1107.
  6. Quian Quiroga, R., Mukamel, R., Isham, E. A., Malach, R., & Fried, I. (2008). Human single-neuron responses at the threshold of conscious recognition. Proc Natl Acad Sci U S A, 105(9), 3599-3604.
  7. Serre, T., Oliva, A., & Poggio, T. (2007). A feedforward architecture accounts for rapid categorization. Proc Natl Acad Sci U S A, 104(15), 6424-6429.
  8. Keysers, C., Xiao, D. K., Foldiak, P., & Perrett, D. I. (2001). The speed of sight. J Cogn Neurosci, 13(1), 90-101.
  9. Sheinberg, D. L., & Logothetis, N. K. (2001). Noticing familiar objects in real world scenes: the role of temporal cortical neurons in natural vision. J Neurosci, 21(4), 1340-1350.

Lecture 3. Coding with Spikes

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  1. Delorme, A., & Thorpe, S. J. (2001). Face identification using one spike per neuron: resistance to image degradations. Neural Netw, 14(6-7), 795-803.
  2. Gollisch, T., & Meister, M. (2008). Rapid neural coding in the retina with relative spike latencies. Science, 319(5866), 1108-1111.
  3. Johansson, R. S., & Birznieks, I. (2004). First spikes in ensembles of human tactile afferents code complex spatial fingertip events. Nat Neurosci, 7(2), 170-177.
  4. VanRullen, R., & Thorpe, S. J. (2001). Rate coding versus temporal order coding: what the retinal ganglion cells tell the visual cortex. Neural Comput, 13(6), 1255-1283.
  5. VanRullen, R., & Thorpe, S. J. (2002). Surfing a spike wave down the ventral stream. Vision Res, 42(23), 2593-2615

Lecture 4. Learning with Spikes

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  1. Bichler, O., Querlioz, D., Thorpe, S. J., Bourgoin, J. P., & Gamrat, C. (2012). Extraction of temporally correlated features from dynamic vision sensors with spike-timing-dependent plasticity. Neural Netw, 32, 339-348.
  2. Masquelier, T., & Thorpe, S. J. (2007). Unsupervised Learning of Visual Features through Spike Timing Dependent Plasticity. PLoS Comput Biol, 3(2), e31.
  3. Masquelier, T., Guyonneau, R., & Thorpe, S. J. (2008). Spike timing dependent plasticity finds the start of repeating patterns in continuous spike trains. PLoS ONE, 3(1), e1377.
  4. Masquelier, T., Guyonneau, R., & Thorpe, S. J. (2009). Competitive STDP-Based Spike Pattern Learning. Neural Comput, 21(5), 1259-1276.
  5. Masquelier, T., Hugues, E., Deco, G., & Thorpe, S. J. (2009). Oscillations, Phase-of-Firing Coding, and Spike Timing-Dependent Plasticity: An Efficient Learning Scheme. J Neurosci, 29(43), 13484-13493.

Lecture 5. Long term memories and Spikes.

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Required Readings:
  1. Agus, T. R., Thorpe, S. J., & Pressnitzer, D. (2010). Rapid Formation of Robust Auditory Memories: Insights from Noise. Neuron, 66(4), 610-618.
  2. Thorpe, S. J. (2002). Localized Versus Distributed Representations. In M. A. Arbib (Ed.), Handbook of Brain Theory and Neural Networks (2nd ed., pp. 549-552). Cambridge, MA: MIT Press.
  3. Thorpe, S. J. (2009). Why Connectionist Models needs spikes. In D. Heinke & E. Mavritsaki (Eds.), Computational Modelling in Behavioural Neuroscience: Closing the gap between neurophysiology and behaviour (pp. 21-44). London: Psychology Press.
  4. Thorpe, S. J. (2011). Grandmother Cells and Distributed Representations. In N. Kriegeskorte & G. Kreiman (Eds.), Understanding visual population codes - Towards a common multivariate framework for cell recording and functional imaging (pp. 23-51). Cambridge: MIT Press.